Geography Notes

Essay on thailand: an outstanding essay on thailand.


Next to Myanmar, Thailand is the second largest country on the Southeast Asian mainland. Its territory of 198,115 sq. miles (over 513,117 sq. km) shelters a population of over 62 million. Geography and history have conspired to make the country a unique nation. There has been a major Thai state in the present territory of Thai­land for the last six hundred years, and the country is one of the very few in Asia to have escaped European colonialism.

Lying off the major historic sea lanes, it was spared the influences that shaped the mari­time world to the south and east—notably the Muslim religion and the European rule. Thailand has thus acted as a buffer be­tween the conflicting interests of France and England for control of the region, and partly because of this competition suc­ceeded in preserving its independence during the European colonial partition of Southeast Asia.

The monarchy became and remains a potent symbol of the country’s historical continuity and national identity. For over a century, the country has prac­ticed a neutral stance in world affairs, and its post-1950 dependence on the West is a sharp break with tradition.

The non-colonial development of the economy is illustrated by the fact that em­phasis was placed almost entirely on non-estate agriculture except for the rub­ber plantations in the southern peninsula. The great rubber, tea, coffee, coconut, palm, and other plantation estates of Indo­nesia, the Philippines and Indonesia are conspicuously absent in the nation as has been the European and American capital.

It is only in the exploitation in tin extrac­tion in its peninsula region and in the growing manufacturing sector that the American and European involvement has been significant. Thailand, for a long time, remained neglected by the West, partly be­cause the colonial powers were engaged elsewhere, and due in part to Thailand’s lo­cation off the historic routes of maritime trade.

Physical Characteristics:

Thailand’s physical configuration is simple: a south- facing river basin enclosed on the west, north and the southeast by mountains, and a long, slender peninsular finger in the south. The northern and western moun­tains are the southward continuation of the complex mountain system of the Hi­malayas from eastern Tibet curving to the south that, in part, form the boundary be­tween southern Myanmar and Thailand.

These mountains are a series of north- south ranges, rise to nearly 8,000 feet (2,440 meters), and trend southward into Malaysia. To the north are the hills and dissected plateau region of Myanmar that contains caves from which remains of pre­historic humans have been excavated.

The Khorat Plateau in the northeast covers a third of the country that gently tilts to­ward the east, and lies in the drainage of the Mekong. The Plateau is enclosed on the west and south by low, linear hills. Surface elevations on the Khorat range from 650 feet (198 meters) in the north­west to 300 feet in the southeast.

Lying between the northern and western moun­tain ranges and the Khorat Plateau is a sizable Chao Phraya River basin, which is the cultural and economic heartland of the country, known also as the Central Low­lands. This region consists of rolling plains in the north and a low-lying flood plain and delta of the Chao Phraya formed by the large deposits of alluvium brought by the tributaries of the rivers.

The alluvial deposits of the river valleys are the most fertile in Thailand, as these are replenished year after year with river sediments swol­len with annual monsoon rains. The topography of the peninsular arm is roll­ing to mountainous, with little flat land. Higher mountains rise to about 5,000 feet (1,524 meters) on the west, off the rugged and indented coast, lie several small is­lands, including the Phuketl Island, which is rich in tin.

The climate of Thailand may be de­scribed as tropical monsoonal. The major influences are the country’s location in the tropics, monsoon zone and the topo­graphic features affect the distribution of rainfall. In early May the southwest mon­soons flow from the Indian Ocean, and bring large amounts of rainfall, which reaches a maximum in September.

The wind system is reversed between Novem­ber and February, when a northeast monsoon brings cool, dry air. Occasion­ally, typhoons may come across the China Sea and bring some rain but fades out across Thailand. The amount of rainfall varies from 40 inches to 120 (1,016 to 3,048 mm) in the various parts of the country.

In the southern peninsular region a dry sea­son seldom occurs and receives as much as 160 inches of precipitation annually, whereas Bangkok gets 55 inches (1,397 mm) and Khorat, sheltered by hills on all sides even less than 30 inches (762 min) and almost the whole of the peninsular region receives over 80 inches distributed throughout the year. Temperatures are, in general, moderate to high, averaging be­tween 77° and 84°F (25° and 29°C).

The season of highest temperatures is in late March, April and early May. In central, peninsular and southeastern Thailand, maximum temperatures seldom reach 100°F (37.7°C), while minimum tempera­tures are lower than 65°F (18.3°C). In northern Thailand, temperature range tends to be much larger.

Soils of the river valleys are fertile, and the most fertile land IS in the flood plains of the lower Chao Phraya basin because it receives large amounts of the rich, alluvial deposits of soil every year. Relatively flat areas else­where and parts of the coast also have fertile soils. Elsewhere, soils tend to be poor, highly leached laterites of the humid tropics.

Cultural Patterns:

Among Southeast Asian countries, Thailand is the most iden­tity-conscious nation. Relatively homo­geneous, the country does not possess the multiplicity of languages found in Indone­sia and the Philippines, nor contains a complex ethnic mix as in Malaysia. Eighty- five percent of the population speaks Thai, which is a member of a large cluster of lan­guages spoken in all bordering countries as well as southern China and northern Viet­nam.

Like the people of Myanmar, Cambodia and Laos, the Thais are Bud­dhists of the Theravada school. In 1991 ninety-five percent of the population was listed as adhering to Buddhism. The mi­norities include Muslims (who account for four percent of the population), Hindus, Sikhs, and a few Christians, which are con­centrated chiefly around Bangkok. The national government plays down regional loyalties, and the Thai language is taught in schools throughout the country.

Non-Thais number nearly 12 million or 20 percent of the population. The larg­est ethnic minority, comprising over 8 million or 12 percent of the total popula­tion are Chinese, who have been assimilated to a far greater degree than in either Malaysia or Indonesia. There are no barriers to intermarriage, and most have embraced Thailand’s Buddhism.

The next largest minority is that of Malays, who profess the Muslim faith, and are largely concentrated in the southern peninsular neck of the country close to the Malaysian border. In the northern and northwestern part of the country along the Myanmar border are several hill peo­ple—the tribal groups, chief of which are Karens, and Shans (numbering over one million each). Most are shifting cultivators.

Also included among Thailand’s minori­ties are Vietnamese, who moved and settled in the northeastern part of the country in the 1940s and 1950s to escape Indo-China war with the French, and Khmers (Cambodians) who fled their homeland as refugees after the 1979 Viet­namese invasion of Cambodia.

Such border areas inhibited by the minority groups in the north, northwest, northeast and the southern peninsula are imperfectly integrated into the Thai state, and are eco­nomically backward as well. The Thais dominate the lowlands, and there are lay­ers of non-Thai people in the mountainous borderlands.

Economic Activity:

Traditionally, ag­riculture has been the dominant sector of Thailand’s economy. Although through government encouragement to small in­dustry, its contributions to economic growth have declined consistently since 1950. The proportion of the agricultural la­bor force has declined from 88 percent in the 1950s to less than 50 percent.

Agricul­ture’s contribution to the national economy relative to manufacturing has also declined from more than 50 percent in the 1950s to less than 11 percent in the 1999. Despite this shift to manufacturing, agricultural production has continued to expand, and Thai farmers continue to pro­duce enough rice for the country’s needs as well as a surplus for export.

Today, Thailand is the world’s fifth largest producer of rice and its largest ex­porter (exporting one-third to a quarter of rice exports of the world). Agriculture is overwhelmingly associated with rice culti­vation, and close to ninety percent of the country’s cultivable area is given to it, nearly one-half of which lies in the Chao Phraya basin where the flood waters of the river provide irrigation and silt-laden fer­tile soils to the fields.

During the 1960s movement toward crop diversification be­came popular and the farmers began growing such other export crops as maize, sugarcane, pineapples, tobacco, coconuts, and kenaf (a substitute for jute) on a larger scale than before.

These crops have since been slowly acquiring greater prominence. In addition, large quantities of vegetables and fruits are also grown. Cattle breeding are important in the Central plains, and pigs and poultry are widely raised. Fishing is also of considerable importance, and con­stitutes a growing export commodity. Rubber production—introduced into the country during the 19th century—is im­portant in the southern, peninsular section.

Thailand ranks third in the world in natural rubber production. It produces nearly one-sixth of the world’s production of hardwoods—particularly teak. Its major forest products are now exported in small quantities, following a government ban on logging imposed in 1989.

Mining constitutes a small segment of nation’s economy, with only 0.2 of labor- force engaged in it and contributing less than 2 percent to the domestic gross prod­uct. Tin, mined mostly in the peninsula, has long been a valuable mineral resource, and the country has become one of the world’s largest tin producers, producing on the average about one-tenth of the world’s total output. Coal, zinc, gypsum, tungsten, and limestone are some other minerals produced.

The manufacturing sector has dramati­cally grown during the last four decades, representing primarily the large invest­ments made by private firms; the larger ones have been financed by foreign and Thai capital. Japan, South Korea, Taiwan, and Singapore have been the major sources of investment for industry that is particu­larly oriented to producing consumer goods such as clothing, canned goods, and electrical products. Japanese capital is in­creasingly invested in the manufacture of textiles and machinery.

At the same time, growth of the traditional, factory-type in­dustry including that of rice milling, sugar and timber, the manufacture of tobacco, jute sacks and cement as the production of textiles (especially based on silk), clothing, furniture, and footwear owned primarily by domestic investors has also registered substantial gains. Factory industry is heav­ily concentrated in the Bangkok area.

Thailand’s imports include electrical machinery, minerals and fuels, iron and steel, vehicles, plastics, and organic chemi­cals—items necessary for its growing industrialization and domestic needs. Its major exports in the mid-1990s in the cate­gory of manufactured items were electric machinery, textiles and apparel, and nu­clear reactors that collectively accounted for nearly forty percent of all exports, whereas the traditional exports of rice, tin, rubber, and teak made up for nearly 22 percent of the nation’s export earnings.

Physical and Economic Regionalism :

Physically, and economically, Thailand is composed of several distinctive natural units, although the key area is the central lowland, the plain of the Chao Phraya, which accounts for about one-fifth of the country’s territory and two-fifths of its population. This is the area of most com­pact Thai settlement and most important agriculture.

Population densities are high­est of any region: over 600 persons per sq. mile (230 persons per sq. km). It was for­merly forested but now consists of unbroken paddy (rice) fields. Soils are ex­tremely fertile, composed of rich alluvium brought by the river. Despite receiving a relatively low total rainfall of a little over 50 inches (1,250 millimeters) a year, it is the country’s agricultural heartland and the rice basket.

Cassava, maize and other crops are also grown here. For most of the nation’s history, the capital has been lo­cated here and the people of the central lowland have been the dominant group in the country. Most of Thailand’s commercial, indus­trial, and service industries are located in the central lowland, focused largely on Bangkok, the capital.

The most important theme of the nation’s modern history has been the steady concentration of political authority and economic power in a cen­tralized government and at a single place: Bangkok, the capital (population 5.6 mil­lion), which has come to concentrate all facets of Thai life to a remarkable degree unsurpassed elsewhere.

In the process, the city grew to be a classic example of a “pri­mate city,” collecting nearly 10 percent of the national population; its metropolitan area is nearly 30 times larger than the next biggest city—Nakhon Ratchasima 250 miles to the northeast in the Khorat Pla­teau. Containing more than 300 Buddhist temples, the royal place, and other cultural attractions, it is a tourist Mecca.

Most of the country’s trade passes through its port, and the manufacturing sector is growing rapidly. Chiang Mai (population: 1.6 mil­lion) located in the north, is another tourist center outside the capital. The vast northeastern region, sepa­rated from Laos by the Mekong River, is the plateau area of Khorat. Not blessed with the fertile soils and adequate precipi­tation of the central plains, it is the poorest area of Thailand, and contains about eight million people who are officially desig­nated as living in poverty.

Like the northern region, this area had a history of semi-autonomy until the late 19th century. The people speak a language similar to the Lao, and have often displayed discontent with the central Thai administration, which has recently been trying to bring them into the national fold. The long peninsular tail to the south which joins central Thailand with Malaysia is less fer­tile, but is the country’s major rubber-growing and tin producing region.

Thailand has recorded some of Southeast Asia’s most impressive eco­nomic gains (averaging between 6 and 7 percent a year) during the last three dec­ades. The fastest expansion has been seen in the manufacturing, service and trading sectors. Domestic markets have expanded and production of such commodities as ce­ment, soft drinks and textiles has continued to grow. American military ex­penditure during the 1960s and 1970s and Japanese investments further bolstered the economy.

Between 1950 and 1970 a rapidly grow­ing population particularly in the Central lowlands and around Bangkok had caused great concern, and the administration which had previously supported popula­tion growth reversed its policies. Since 1970s the family planning programs of the government helped to substantially reduce the population growth rates, which now stand at 0.9 percent a year at nearly one- third of those prevailing during the 1960s and 1970s.

The country is now a model for other developing nations seeking to reduce their rates of population increase. How­ever, a third of Thailand’s population belongs to the youthful age group (be­tween 20 and 40) that creates high demands on the nation’s education, hous­ing, health and employment systems, but the government is trying to utilize its highly literate human resource (with a lit­eracy rate of over 90 percent) for economic development.

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Thailand snapshot banner

This article provides an overview of Thailand for those keen on exploring the possibility of living and working there. The information presented is gathered from open sources and is not exhaustive or meant to supplement or substitute legal and professional advice.

  • Official name: Kingdom of Thailand 1
  • Capital: Bangkok 2
  • Geography: 76 provinces and two special local territories – Bangkok and Pattaya 3
  • Land area (2019): 513,120 sq km 4
  • Population (2019): 66.6 million 5
  • Head of government: Prime Minister General Prayut Chan-o-cha 6
  • National language: Thai 7
  • Currency: Thai Baht (THB) 8
  • Gross domestic product (GDP) (2019): 16,879 billion baht 9
  • GDP per capita (2019): 228,373 baht 10


Thailand is situated in the heart of Southeast Asia, bordered by the Laos and Cambodia in the east; the Gulf of Thailand and Malaysia in the south; the Andaman Sea and Myanmar in the west; and Laos and Myanmar in the north. It is spread over 513,120 sq km. 11

Demographic profile

The total population of Thailand was almost 66.6 million in 2019. Around 48.96 percent (32.61 million persons) were male and 50.98 percent (33.95 million persons) female. Most of the population lived in the northeastern region (33.05 percent), followed by the northern region (18.2 percent) and Bangkok (16.42 percent). Majority of the population are Buddhists, followed by Muslims and Christians. 12

Thailand has a mixed economy with the major economic sectors being agriculture, manufacturing, tourism, service and natural resource. Its GDP in 2019 was 16,879 billion baht. 13 Its economic growth sectors include tourism, automotive and food manufacturing, which are supported by its well-developed transportation system, infrastructure and communications systems. 14

A leading exporter of rubber, Thailand also exports crops such as rice, vegetables and fruits. It is also famous for its livestock exports, as well as exports of freshwater fish and marine fishery. Its industrial exports include agro-industry, textile, electric appliance and automobiles. Important natural resources like limestone, gypsum, glass, sand, marble, tin and natural gas also contribute to the economy. 15

Singapore and Thailand

Singapore and Thailand share a bilateral network known as the Singapore-Thailand Enhanced Economic Relationship. This is a platform for government agencies and private sectors to promote closer economic cooperation and deepen the level of contact and consultative process between the two countries. 16

Thailand’s key business sectors include infrastructure development and manufacturing. 17 The government approved a 3.3 trillion baht (US$101 billion) Infrastructure Development Plan (2015–2022) that aims to improve Thailand’s transport links (railways, roads, water transportation, aviation and mass transit projects), both within Thailand and with its neighbours. 18

Thailand has the fourth largest consumer market in ASEAN. Its unemployment rate is at a low one percent and the country has a purchasing power of an estimated US$6,000. In recent years, the purchasing power outside of capital city Bangkok has also grown. 19

The Thai automotive sector is the largest in ASEAN. However, the industry has been impacted by the global shift towards electric vehicles (EVs) in recent years. Hence, the “Thai government is persistently trying to encourage the manufacturing of EVs and high-tech auto parts through promotion of incentive packages.” 20

Thailand is home to Buddhist temples, exotic wildlife and spectacular islands. It is also known for its fascinating history, unique culture and delectable local food. The tourism industry plays an important role in the Thai economy and contributes an estimated 18.4 percent to the national GDP. The tourism sector not only depends on foreign visitors but also domestic tourists whose number dwarfs that of foreign tourists. In 2019, Thailand had 39,797,406 visitors. 21

Thailand 4.0

Thailand 4.0 is an economic model that aims to “unlock Thailand from several economic challenges and help the country break free from the middle-income trap”. It comprises four objectives: economic prosperity – to create a value-based economy that is driven by innovation, technology and creativity; social well-being – to create a society that moves forward without leaving anyone behind (inclusive society) through realisation of the full potential of all members of society; raising human values – to transform cost-effective labour into a skilled workforce; and environmental protection – to become a liveable society that possesses an economic system capable of adjusting to climate change and low carbon society. 22

Statistical yearbook Thailand 2020. (2020). National Statistical Office, Ministry of Digital Economy and Society , p. 13. Retrieved December 9, 2020, from   ↩

Statistical yearbook Thailand 2020. (2020). National Statistical Office, Ministry of Digital Economy and Society , p. 14. Retrieved December 9, 2020, from   ↩

Prime Minister. (n.d.). Office of the Prime Minister. Retrieved December 9, 2020, from   ↩

Statistical yearbook Thailand 2020. (2020). National Statistical Office, Ministry of Digital Economy and Society , p. 15. Retrieved December 9, 2020, from   ↩

Statistical yearbook Thailand 2020. (2020). National Statistical Office, Ministry of Digital Economy and Society , p. 58. Retrieved December 9, 2020, from   ↩

Statistical yearbook Thailand 2020. (2020). National Statistical Office, Ministry of Digital Economy and Society , p. 13, 76-78. Retrieved December 9, 2020, from   ↩

Thailand. (2020). Enterprise Singapore. Retrieved December 9, 2020, from   ↩

Statistical yearbook Thailand 2020. (2020). National Statistical Office, Ministry of Digital Economy and Society , p. 16. Retrieved December 9, 2020, from   ↩

Thailand. (n.d.). Ministry of Foreign Affairs Singapore. Retrieved December 9, 2020, from   ↩

Profiled industries. (n.d.). Enterprise Singapore. Retrieved December 9, 2020, from   ↩

Thailand plans massive investment in transport infrastructure. (2017, January 10). LinkedIn. Retrieved December 9, 2020, from   ↩

Profiled industries. (n.d.). Enterprise Singapore. Retrieved December 9, 2020, from ; Thailand’s automotive industry outlook 2019. (2019). AEC Business Advisory. Retrieved December 9, 2020, from   ↩

Statistical yearbook Thailand 2020. (2020). National Statistical Office, Ministry of Digital Economy and Society, p. 569. Retrieved December 9, 2020, from   ↩

Thailand 4.0. (n.d.).* Royal Thai Embassy, Washington D.C.* Retrieved December 9, 2020, from   ↩


  • Global Insights

Thailand: Introduction

Thailand is a country located in Southeastern Asia bordering the Andaman Sea and the Gulf of Thailand. Neighboring countries include Burma, Cambodia, Laos, and Malaysia. The geography consists of a mountain range in the west and a southern isthmus that joins the landmass with Malaysia. The government system is a constitutional monarchy; the chief of state is the king, and the head of government is the prime minister. Thailand has a mixed economic system in which there is a variety of private freedom, combined with centralized economic planning and government regulation. Thailand is a member of the Asia-Pacific Economic Cooperation (APEC) and the Association of Southeast Asian Nations (ASEAN).

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Thailand is in the heart of Southeast Asia.

Thailand is in the heart of Southeast Asia. Cambodia and Laos border the country to the east and northeast, and Myanmar lies to the northwest. To the west is the Andaman Sea and the Gulf of Thailand, southeast of Burma. The long southern region, connecting with Malaysia, is hilly and forested. The highest mountains are in northern Thailand.

Map created by National Geographic Maps


About 90 percent of the people are Buddhist, but about three million Muslims live in the south near the border with Malaysia.

Thai children go to elementary school for six years. Then they may attend high school for another six years, but their families must pay for the education. Boys begin military training in ninth grade.

Food in Thailand is influenced by Chinese and Indian cultures. Most Thai dishes are spicy and many common dishes include hot chilies, lemongrass, basil, ginger, and coconut milk.

Thai farmers cultivate mulberry trees that feed silkworms. The worms create silk, which is made into beautiful silk clothing in Thailand, France , and the United States .

Bangkok is called the Venice of the East because there are 83 canals. As many as 10,000 boats full of fruits, vegetables, and fish crowd the canals and create a floating market.

The city of Bangkok is home to many impressive Buddhist structures featuring gold-layered spires, graceful pagodas, and giant Buddha statues.

Rain falls almost every day between the months of May and September. The moist and humid weather encourages the diverse and abundant wildlife in Thailand.

Lotus flowers are common and the favorite flower in Thailand. Lotus flowers live above the surface, but they are rooted in the mud. There are many flowering trees and shrubs, and fruit trees. In the jungle, one can find carnivorous (meat-eating) plants such as the mysterious insect-eating pitcher plant .

The deep forests are home to tigers , elephants , wild ox, leopards , and the Malayan tapir. The tapir is covered in black fur on the first half of its body and white fur to the rear. Cobras and crocodiles are also found in Thailand.


Known as Siam until 1939, Thailand is the only Southeast Asian country never to have been taken over by a European power. A revolution in 1932 led to a constitutional monarchy.

The king is the leader of the country. The prime minister is picked from among members of the House of Representatives, but is appointed by the king.

Agriculture and tourism are the most important industries in Thailand.

In December 2004, the catastrophic Indian Ocean tsunami hit Thailand, but the country's economy has largely recovered from the disaster's effect.

Around 2000 B.C. people built settlements in the hillsides of Thailand. The first one is thought to be Ben Chiang. Pieces of pottery, tools, and jewelry from 200 B.C. to 300 A.D. have been dug up in this area.

Thailand, which means “land of the free,” was known as Siam until 1939.

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We seek to provide our volunteers and staff across the globe with the highest level of safety, support, and stability. Based on CDC and U.S. State Department elevated travel advisories, we will suspend programs as needed, and constantly assess conditions at our program locations to meet our revised health and safety protocols. Social distancing and cleaning procedures have become a way of life for all of us and are now impacting how we are planning our programs in terms of accommodation, projects, leisure activities, and meals. When you're ready to fly, you'll also see that a lot has changed at the airport and on board aircraft. Airlines are boarding fewer customers at a time and starting from the back of the plane to avoid crowding in the gate area, on the jet bridge and in the aisle. They’re blocking middle seats to give you enough space on board, requiring employees on board, including flight attendants, to wear masks and, in most cases, making masks available to their customers. These measures are in addition to sanitation procedures like cleaning aircraft with electrostatic sprayers, and taking employees' temperatures daily to protect passengers. We provide new recommendations regarding the use of protective masks, cleaning supplies, and other tools. We are developing several ways to reduce the density of groups on our programs. We will also take extra precautions to protect the most vulnerable among us at our program locations, especially those with ailments that heighten the risks of the most severe COVID-19 cases. Below are a couple of frequently updated resources to give you an idea of how COVID19 has progressed at this program location:



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The Kingdom of Thailand draws more visitors than any other country in southeast Asia with its irresistible combination of breathtaking natural beauty, inspiring temples, renowned hospitality, robust cuisine and ruins of fabulous ancient kingdoms.

From the stupa-studded mountains of Mae Hong Son and the verdant limestone islands of the Andaman Sea, to the pulse-pounding dance clubs of Bangkok and the tranquil villages moored along the Mekong River, Thailand offers something for every type of traveller.

Of course Thailand, like other Asian countries, has been influenced by contact with foreign cultures. But the never-changing character of Thai culture has remained dominant, even in modern city life. Often depicted as fun-loving, happy-go-lucky folk (which indeed they often are), the Thais are also proud and strong, and have struggled for centuries to preserve their independence of spirit.

Recent outbreaks of avian flu have resulted in fatalities and travellers are reminded to avoid situations where they will be closely exposed to live poultry or birds.

On 28 October 2004, an explosion in Sungai Ko-lok, a resort town in Thailand's predominantely Muslim Narathiwat province frequented primarily by Malaysians, killed one person. It seemed to signal a retaliation by the PULO Islamic separatist organisation for the deaths in custody, just days earlier, of 78 Muslim protesters in the province.

The incidents followed an explosion on 26 August, 2004, when a bomb exploded at Mamong market of Narathiwat province, also linked to separatists. Separatists have also staged attacks on police stations in the provinces of Pattani, Songkhla and Yala. While security forces have regained control and the situation has quietened, the area is still volatile, as exemplified by the explosion of three bombs in Buddhist temples in Narathiwat on 16 May, 2004. Travel in the area is to be approached with caution.

Although Thailand is a safe destination, travellers should be aware of occasional violence and banditry in some border areas, petty theft in cities and resort areas, and security issues on public transport, particularly in regard to women travelling on their own.

Thailand's borders with both Cambodia and Myanmar contain a volatile mixture of land mines, bandits, smugglers and rebels, and are the scene of occasional low-level military stoushes. Check the latest consular information for the most up-to-date information.

In Bangkok, unlicensed taxis, recognisable by their black and white licence plates, should be avoided. This is most relevant for solo women travelling at night. Look for licensed taxis that have yellow and black licence plates. Hotel rooms should be locked and bolted at night, and cheap, thin-walled rooms checked for strategic peepholes. Obtain an itemised receipt for valuables left in hotel safes, especially around Chiang Mai.

Full country name: Kingdom of Thailand Area: 517,000 sq km Population: 62 million Capital City: Bangkok People: 75% Thai, 11% Chinese, 3.5% Malay; also Mon, Khmer, Phuan and Karen minorities Language: Thai Religion: 95% Buddhism, 4% Muslim Government: constitutional monarchy Head of State: King Bhumibol Adulyadej (Rama IX) Head of Government: Prime Minister Thaksin Shinawatra

GDP: US$166 billion GDP per capita: US$2,168 Annual Growth: 3.5% Inflation: 2% Major Industries: Computers, garments, integrated circuits, gems, jewellery Major Trading Partners: ASEAN, USA, European Union

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Study Paragraphs

Short Essay About Thailand

Short essay on thailand.

Thailand is a beautiful country located in Southeast Asia. Known for its stunning beaches, delicious food, and rich culture, it is a popular destination for tourists from all over the world.

One of the most iconic landmarks in Thailand is the Wat Phra Kaew temple, located in the capital city of Bangkok. It houses the famous Emerald Buddha, a highly revered religious icon in Thai culture. Visitors can also explore the Grand Palace, a complex of buildings that served as the official residence of the Kings of Siam.

Another popular destination is the city of Chiang Mai, known for its ancient temples, night markets, and elephant sanctuaries. Visitors can experience traditional Thai culture by participating in a cooking class, trying on traditional clothing, or taking part in a traditional Thai massage.

Thailand is also famous for its stunning beaches, such as Phuket, Koh Samui, and Krabi. Visitors can enjoy activities such as snorkeling, diving, or simply relaxing on the beach.

In addition to its natural beauty and cultural heritage, Thailand is also known for its delicious cuisine. Popular dishes include pad thai, green curry, and tom yum soup.

Overall, Thailand is a must-visit destination for those looking for a unique and unforgettable travel experience. From its vibrant cities to its stunning beaches and rich culture, there is something for everyone to enjoy in this amazing country.

Paragraph Writing

Hello! Welcome to my Blog My name is Angelina. I am a college professor. I love reading writing for kids students. This blog is full with valuable knowledge for all class students. Thank you for reading my articles.

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introduction to thailand essay

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From the modern metropolis of Bangkok and the scenic mountains of Chiang Mai to the laid-back beaches and stunning coastlines of Phuket, Krabi and Koh Samui, Thailand is a dynamic destination that offers a unique combination of transformative travel experiences, from wellness immersions, active lifestyle adventures, cultural discoveries and tribal arts and crafts traditions . Admire Bangkok's dazzling  Grand Palace, lively markets and contemporary fine dining scene. Taste the spice of northern Thai cuisine and explore hill tribe farms and villages near Chiang Mai or travel south to experience tropical island life in the Andaman Sea. 

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  • How to write an essay introduction | 4 steps & examples

How to Write an Essay Introduction | 4 Steps & Examples

Published on February 4, 2019 by Shona McCombes . Revised on July 23, 2023.

A good introduction paragraph is an essential part of any academic essay . It sets up your argument and tells the reader what to expect.

The main goals of an introduction are to:

  • Catch your reader’s attention.
  • Give background on your topic.
  • Present your thesis statement —the central point of your essay.

This introduction example is taken from our interactive essay example on the history of Braille.

The invention of Braille was a major turning point in the history of disability. The writing system of raised dots used by visually impaired people was developed by Louis Braille in nineteenth-century France. In a society that did not value disabled people in general, blindness was particularly stigmatized, and lack of access to reading and writing was a significant barrier to social participation. The idea of tactile reading was not entirely new, but existing methods based on sighted systems were difficult to learn and use. As the first writing system designed for blind people’s needs, Braille was a groundbreaking new accessibility tool. It not only provided practical benefits, but also helped change the cultural status of blindness. This essay begins by discussing the situation of blind people in nineteenth-century Europe. It then describes the invention of Braille and the gradual process of its acceptance within blind education. Subsequently, it explores the wide-ranging effects of this invention on blind people’s social and cultural lives.

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Table of contents

Step 1: hook your reader, step 2: give background information, step 3: present your thesis statement, step 4: map your essay’s structure, step 5: check and revise, more examples of essay introductions, other interesting articles, frequently asked questions about the essay introduction.

Your first sentence sets the tone for the whole essay, so spend some time on writing an effective hook.

Avoid long, dense sentences—start with something clear, concise and catchy that will spark your reader’s curiosity.

The hook should lead the reader into your essay, giving a sense of the topic you’re writing about and why it’s interesting. Avoid overly broad claims or plain statements of fact.

Examples: Writing a good hook

Take a look at these examples of weak hooks and learn how to improve them.

  • Braille was an extremely important invention.
  • The invention of Braille was a major turning point in the history of disability.

The first sentence is a dry fact; the second sentence is more interesting, making a bold claim about exactly  why the topic is important.

  • The internet is defined as “a global computer network providing a variety of information and communication facilities.”
  • The spread of the internet has had a world-changing effect, not least on the world of education.

Avoid using a dictionary definition as your hook, especially if it’s an obvious term that everyone knows. The improved example here is still broad, but it gives us a much clearer sense of what the essay will be about.

  • Mary Shelley’s  Frankenstein is a famous book from the nineteenth century.
  • Mary Shelley’s Frankenstein is often read as a crude cautionary tale about the dangers of scientific advancement.

Instead of just stating a fact that the reader already knows, the improved hook here tells us about the mainstream interpretation of the book, implying that this essay will offer a different interpretation.

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Next, give your reader the context they need to understand your topic and argument. Depending on the subject of your essay, this might include:

  • Historical, geographical, or social context
  • An outline of the debate you’re addressing
  • A summary of relevant theories or research about the topic
  • Definitions of key terms

The information here should be broad but clearly focused and relevant to your argument. Don’t give too much detail—you can mention points that you will return to later, but save your evidence and interpretation for the main body of the essay.

How much space you need for background depends on your topic and the scope of your essay. In our Braille example, we take a few sentences to introduce the topic and sketch the social context that the essay will address:

Now it’s time to narrow your focus and show exactly what you want to say about the topic. This is your thesis statement —a sentence or two that sums up your overall argument.

This is the most important part of your introduction. A  good thesis isn’t just a statement of fact, but a claim that requires evidence and explanation.

The goal is to clearly convey your own position in a debate or your central point about a topic.

Particularly in longer essays, it’s helpful to end the introduction by signposting what will be covered in each part. Keep it concise and give your reader a clear sense of the direction your argument will take.

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As you research and write, your argument might change focus or direction as you learn more.

For this reason, it’s often a good idea to wait until later in the writing process before you write the introduction paragraph—it can even be the very last thing you write.

When you’ve finished writing the essay body and conclusion , you should return to the introduction and check that it matches the content of the essay.

It’s especially important to make sure your thesis statement accurately represents what you do in the essay. If your argument has gone in a different direction than planned, tweak your thesis statement to match what you actually say.

To polish your writing, you can use something like a paraphrasing tool .

You can use the checklist below to make sure your introduction does everything it’s supposed to.

Checklist: Essay introduction

My first sentence is engaging and relevant.

I have introduced the topic with necessary background information.

I have defined any important terms.

My thesis statement clearly presents my main point or argument.

Everything in the introduction is relevant to the main body of the essay.

You have a strong introduction - now make sure the rest of your essay is just as good.

  • Argumentative
  • Literary analysis

This introduction to an argumentative essay sets up the debate about the internet and education, and then clearly states the position the essay will argue for.

The spread of the internet has had a world-changing effect, not least on the world of education. The use of the internet in academic contexts is on the rise, and its role in learning is hotly debated. For many teachers who did not grow up with this technology, its effects seem alarming and potentially harmful. This concern, while understandable, is misguided. The negatives of internet use are outweighed by its critical benefits for students and educators—as a uniquely comprehensive and accessible information source; a means of exposure to and engagement with different perspectives; and a highly flexible learning environment.

This introduction to a short expository essay leads into the topic (the invention of the printing press) and states the main point the essay will explain (the effect of this invention on European society).

In many ways, the invention of the printing press marked the end of the Middle Ages. The medieval period in Europe is often remembered as a time of intellectual and political stagnation. Prior to the Renaissance, the average person had very limited access to books and was unlikely to be literate. The invention of the printing press in the 15th century allowed for much less restricted circulation of information in Europe, paving the way for the Reformation.

This introduction to a literary analysis essay , about Mary Shelley’s Frankenstein , starts by describing a simplistic popular view of the story, and then states how the author will give a more complex analysis of the text’s literary devices.

Mary Shelley’s Frankenstein is often read as a crude cautionary tale. Arguably the first science fiction novel, its plot can be read as a warning about the dangers of scientific advancement unrestrained by ethical considerations. In this reading, and in popular culture representations of the character as a “mad scientist”, Victor Frankenstein represents the callous, arrogant ambition of modern science. However, far from providing a stable image of the character, Shelley uses shifting narrative perspectives to gradually transform our impression of Frankenstein, portraying him in an increasingly negative light as the novel goes on. While he initially appears to be a naive but sympathetic idealist, after the creature’s narrative Frankenstein begins to resemble—even in his own telling—the thoughtlessly cruel figure the creature represents him as.

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Your essay introduction should include three main things, in this order:

  • An opening hook to catch the reader’s attention.
  • Relevant background information that the reader needs to know.
  • A thesis statement that presents your main point or argument.

The length of each part depends on the length and complexity of your essay .

The “hook” is the first sentence of your essay introduction . It should lead the reader into your essay, giving a sense of why it’s interesting.

To write a good hook, avoid overly broad statements or long, dense sentences. Try to start with something clear, concise and catchy that will spark your reader’s curiosity.

A thesis statement is a sentence that sums up the central point of your paper or essay . Everything else you write should relate to this key idea.

The thesis statement is essential in any academic essay or research paper for two main reasons:

  • It gives your writing direction and focus.
  • It gives the reader a concise summary of your main point.

Without a clear thesis statement, an essay can end up rambling and unfocused, leaving your reader unsure of exactly what you want to say.

The structure of an essay is divided into an introduction that presents your topic and thesis statement , a body containing your in-depth analysis and arguments, and a conclusion wrapping up your ideas.

The structure of the body is flexible, but you should always spend some time thinking about how you can organize your essay to best serve your ideas.

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introduce yourself in thai

How to Introduce Yourself in Thai in 10 Lines


Want to speak Thai? Yes? Good – keep reading. This is for those that truly want to learn the language. Here’s how you introduce yourself in Thai in 10 easy lines… and this might take you 2 to 3 minutes or less. With this lesson…

  • You get the Thai, translations and romanizations.
  • Read out loud to practice your speaking.
  • Feel free to print this sheet out for extra review.

Here’s how you introduce yourself in Thai. Let’s go.

… if you want to REALLY learn Thai with Audio & Video lessons from real teachers, be sure to check out and click here )

1) Hello, It’s nice to meet you.

Hello and Nice to meet you in Thai are a must-know phrases. And any introduction will probably will start with these words.

  • Hello, it’s nice to meet you.
  • สวัสดีค่ะ ยินดีที่ได้รู้จัก
  • Sa-wat-dee-kha yin-dii-tii-dai-ruu-jak

introduce yourself in thai

2) My name is _____.

This is simple. To say “my name is” in Thai, use the phrase below. We’re using “Isra” as an example.

  • My name is Isra .
  • ชื่อของฉันคืออิสระ ชื่อของฉันคืออิสระ
  • Chuue khaawng chan khuue it-sa-ra

3) I am from ______.

So, where are you from? America? Europe? Africa? Asia? Just stick the name of your country inside this phrase. We’ll use Thailand as an example.

  • I’m from Thailand.
  • ฉันมาจากประเทศไทย
  • Chan maa jaak bpra-theet-thai

introduce yourself in thai

4) I live in ______.

What about now – where do you live? Just fill in the blank with the country or city (if famous) into this phrase. I’ll use Bangkok as an example.

  • I live in Bangkok.
  • ฉันอาศัยอยู่ในกรุงเทพฯ ฉันอาศัยอยู่ในกรุงเทพฯ
  • Chan aa-sai yuu nai grung-theep

introduce yourself in thai

5) I’ve been learning Thai for _____.

How long have you been learning Thai for? A month? A year?

  • I’ve been learning Thai for a year.
  • ฉันได้เรียนภาษาไทยมาหนึ่งปี
  • Chan dai riian phaa-saa thai maa nueng bpii

introduce yourself in thai

6) I’m learning Thai at _____.

Where are you learning Thai? At school? At home? This would be a great line to know and use when you’re introducing yourself. Here’s my example:

  • I’m learning Thai at .
  • ฉันเรียนภาษาไทยจาก
  • Chan riian phaa-saa thai jaak ThaiPod101.Com

introduce yourself in thai

7) I am ____ years old.

Here’s how to say how old you are in Thai.

  • I’m 27 years old.
  • ฉันอายุ 27 ปี
  • Chan Aa-yuu yii-sip-jet bpii

introduce yourself in thai

8) I am ______.

What about your position? Are you a student? Yoga teacher? Lawyer for the potato industry? Potato salesman? Super important question that people like to ask (and judge you about – Hey, I’m just a blogger! ).

  • I’m a teacher.
  • Chan bpen khruu

introduce yourself in thai

9) One of my hobbies is _____.

Now, let’s move onto personal interests – hobbies! My hobbies are languages, linguajunkieing and such. How about you? You’ll definitely need this line when introducing yourself in Thai.

Here’s an example to use:

  • One of my hobbies is reading.
  • หนึ่งในงานอดิเรกของฉันคือการอ่าน
  • Nueng nai ngaan a-di-reek khaawng chan khuue gaan-aan

introduce yourself in thai

10) I enjoy listening to music.

Now, this is just another example line about your hobbies . You can use something else where.

  • I enjoy listening to music.
  • ฉันสนุกกับการฟังเพลง
  • Chan sa-nuuk gup gaan fang phleeng

introduce yourself in thai

So now you know how to introduce yourself in Thai in 10 lines. I’m sure there’s a ton more you can say – but this is an easy, simple start that any beginner can put to use. It’s all about starting easy.

See if you can introduce yourself below. Leave me a comment.

I read all comments!

Hope you enjoyed this!

– The Main Junkie

P.S. I highly recommend this for Thai learners. If you REALLY want to learn to Thai with effective lessons by real teachers – Sign up for free at ThaiPod101 (click here) and start learning!

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introduction to thailand essay

IELTS Writing Task 2: How to write a good introduction

Introductions are an important part of a Writing Task 2 essay. They let your examiner know what to expect from your essay. That’s why we have put together a quick list of tips you can use to write an effective introduction for Writing Task 2.

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An introduction is important to the essay because it creates an initial impression in terms of the quality of your writing. A clear, well-organised and relevant introduction will most certainly create a positive first impression on the examiner. So, what makes up an effective introduction? Let’s take a look.  

Tip 1: Stop to read and analyse the question

In Writing Task 2, you need to address all the parts of the question or task in a relevant way. Because your introduction is the first step towards achieving this goal, you need to introduce your answer to all the different parts of the question. This is why it is important to take some time to read and analyse the task before you start writing, so you know exactly what you are being asked to write about. 

Tip 2: Begin with a general statement and then focus in on the details of the question

Writing Task 2 questions usually begin with a general statement before focusing in on more specific points or questions about the topic. Using a similar model in your own introduction is a great way to start your essay, but make sure that your general statement is clearly related to your topic and is not too broad. 

Tip 3: Use your own words

While it is perfectly acceptable for you to use the task as a guide for your introduction, make sure you do not copy material from the task.  

Copying the task word-for-word shows the examiner that you have a limited range of language, which can affect your band score. Instead, change the order of the information, use synonyms, and explain more complex ideas in your own words.  

It is also important not to use a memorised introduction where you insert words related to the question topic. Examiners read thousands of responses so can recognise memorised scripts.

Tip 4: State your position

In Writing Task 2, you will need to develop a position while exploring the different parts of the task. It is then important that you clearly state your position in your introduction. 

Tip 5: Explain how you plan to develop your essay

Even though this strategy can be considered as optional, briefly explaining how you plan to develop the topic can help you better organise your writing. It is also a good way to let the examiner know what you’ll be covering in the essay. 

Review your introduction

Don’t forget to re-read your introduction once you’ve finished writing your essay. It is common for test takers to begin their essays thinking about a specific argument, or a specific way to organise their writing but change their minds as they develop the topic. So, after completing your Writing Task 2, make sure that your final draft still matches your introduction. 

Now that we have gone over some important strategies for writing a good introduction for Writing Task 2, it’s time to look at a sample introduction. Start by reading and analysing the prompt, as mentioned in tip 1. Then, carefully read the sample introduction and notice the different strategies used, which have been highlighted for you.

Sample question

The threat of nuclear weapons maintains world peace. Nuclear power provides cheap and clean energy. 

The benefits of nuclear technology far outweigh the disadvantages. 

To what extent do you agree or disagree?  

Give reasons for your answer and include any relevant examples from your own knowledge or experience. 

Write at least 250 words.

Sample introduction

General Statement: 

Nuclear technology has been around for many years.  


Whether this technology is used for weapons of mass destruction or as a source of energy, many are of the belief that the use of nuclear energy has more advantages than disadvantages. 


In my opinion, nuclear technology can indeed be a very efficient energy source. However, nuclear weapons possess such enormous destructive power that any benefits that this technology may offer to humankind are not enough to counter its potential devastating effects. 


This essay will address why the drawbacks of nuclear technology outweigh the benefits and will include relevant examples to support this position.

Just as an effective introduction will let the examiner know what they can expect from your essay, a good conclusion will remind them of the main points presented and will summarise what you want your examiner to remember from your writing. Check our blog for our post on strategies for writing a good conclusion! 

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Our essay writing service presents to you an open-access selection of free Thailand essay samples. We'd like to stress that the showcased papers were crafted by skilled writers with relevant academic backgrounds and cover most various Thailand essay topics. Remarkably, any Thailand paper you'd find here could serve as a great source of inspiration, valuable insights, and content organization practices.

It might so happen that you're too pressed for time and cannot allow yourself to spend another minute browsing Thailand essays and other samples. In such a case, our website can offer a time-saving and very practical alternative solution: a completely original Thailand essay example written specifically for you according to the provided instructions. Get in touch today to learn more about practical assistance opportunities provided by our buy an essay service in Thailand writing!

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The Best Guide for How to Introduce Yourself in Thai


When you learn Thai language, introducing yourself in Thai is one of the most important things you’ll learn. How to introduce yourself in Thai is a basic Thai lesson for starters, and we’ll provide you with all you need to learn how to introduce yourself in Thai.

After reading this article, you’ll know the following things about how to speak Thai when introducing yourself:

  • Things Thai people normally say in their self-introductions
  • Things Thai people want to know when they meet a foreigner
  • Things that can be said to describe yourself in Thai
  • What to say in formal versus informal situations
  • Some tips to impress Thai people during self-introductions

For people who have just started learning the Thai language, or are just beginning “introduce yourself in Thai” lessons, there’s a lot to remember. There are various Thai introduction phrases, both formal and informal, that you can use. So before you start learning how to present yourself in Thai, it will make things much easier to learn a little basic Thai grammar.

So if you’re ready to learn and explore how to introduce yourself (in Thai to English), then let’s get started.

Table of Contents

  • Basic Thai Grammar
  • Introducing Oneself in Thai


1. Basic Thai Grammar

Talking About Yourself

When introducing yourself in Thai, grammar plays an important role. If you know some pronouns, as well as how to make sentences sound formal, you’ll find it easier to remember how to introduce yourself in Thai language.

1- Thai Pronouns

Before you can learn Thai language, introduce yourself in Thai, and move a conversation forward, you’ll need to a few pronouns. In Thai learning, introduce yourself using one of the many Thai pronouns you can use to call yourself. Each one can be used in different situations, depending on the level of formality and the gender of the speaker. Here’s a list of pronouns you can use, ordered by level of formality, from the most formal to the least formal. (Later on, we’ll also be going over additional “introducing yourself in Thai” vocabulary!)

  • ข้าพเจ้า ( khâa-phá-jâo )
  • ผม ( phǒm )
  • เรา ( rao )
  • ดิฉัน ( dì-chǎn )
  • ฉัน ( chǎn )

2- Khráp and Khâ

To make a sentence sound formal in Thai, Thai people put the word ครับ ( khráp ) and ค่ะ ( khà ) at the end of a sentence when speaking. ครับ ( khráp ) is used when the speaker is male, while ค่ะ ( khâ ) is used when the speaker is female.

2. Introducing Oneself in Thai

Introducing Yourself

One may wonder how to introduce myself in Thai language, or further, how to go about introducing yourself when in Thailand. That’s what we’ll go over in this section of the article. Below is a list of sentences you can use in self-introductions, and questions you may hear from another party. You can use them to introduce yourself in Thai in 10 lines.

When trying to give a self-introduction in Thai language-learning, introduce yourself by starting with your name. Below is some information on talking about your name in Thai.

1- Name / ชื่อ ( chûue )

  • คำถาม: คุณชื่ออะไรครับ / คะ Kham-thǎam: khun chûue à-rai khráp / khá Question: “What is your name?”
  • คำตอบ: ผม / ฉันชื่อ…..ครับ / ค่ะ Kham-dtàawp: phǒm / chǎn chûue …… khráp / khâ Answer: “My name is ……”

2- Nickname / ชื่อเล่น ( chûue-lêen )

  • คำถาม: คุณชื่อเล่นชื่ออะไรครับ / คะ Kham-thǎam: khun chûue-lêen chûue à-rai khráp / khá Question: “What is your nickname?”
  • คำตอบ: ชื่อเล่นของผม / ฉันคือ…..ครับ / ค่ะ Kham-dtàawp: chûue-lêen khǎawng phǒm / chǎn khuue……khráp / khâ Answer: “My nickname is ……”

3- Age / อายุ ( aa-yú )

  • คำถาม: คุณอายุเท่าไหร่ครับ / คะ Kham-thǎam: khun aa-yú thâo-rài khráp / khá Question: “How old are you?”
  • คำตอบ: ผม / ฉันอายุ ….. ปีครับ / ค่ะ Kham-dtàawp: phǒm / chǎn aa-yú…..bpii khráp / khâ Answer: “I’m ….. years old.”

4- Family / ครอบครัว ( khrâawp-khruua )

When you learn Thai, how to introduce yourself can be confusing in terms of what you should share. That said, talking about your family in Thai is a great way to keep a conversation flourishing!

Question 1: Marriage Status

  • คำถาม: คุณแต่งงานหรือยังครับ / คะ Kham-thǎam: khun dtàang-ngaan rǔue yang khráp / khá Question: “Are you married?”
  • คำตอบ: แต่งงานแล้วครับ / ค่ะ Kham-dtàawp: dtàang-ngaan láaeo khráp / khâ Answer: “I’m already married.”
  • คำตอบ: มีแฟนแล้ว แต่ยังไม่ได้แต่งงานครับ / ค่ะ Kham-dtàawp: mii faaen láaeo dtàae yang mâi dâi dtàang-ngaan khráp / khâ Answer: “I have a boyfriend / girlfriend. But I’m not married yet.”
  • คำตอบ: ยังโสดครับ / ค่ะ Kham-dtàawp: yang sòot khráp / khâ Answer: “I’m still single.”

Question 2: Children

  • คำถาม: คุณมีลูกรึยังครับ / คะ Kham-thǎam: khun mii lûuk rúe yang khráp / khá Question: “Do you have children?”
  • คำตอบ: มี…..คนครับ / ค่ะ Kham-dtàawp: mii…..khon khráp / khâ Answer: “I have ….. child(ren).”
  • คำตอบ: ยังไม่มีครับ / ค่ะ Kham-dtàawp: yang mâi mii khráp / khâ Answer: “I don’t have one.”

I Have Two Children

Question 3: Brother / Sister

  • คำถาม: คุณมีพี่น้องรึเปล่าครับ / คะ Kham-thǎam: khun mii phîi-náawng rúe-bplàao khráp / khá Question: “Do you have a brother or sister?”
  • คำตอบ: ผม / ฉันเป็นลูกคนเดียวครับ / ค่ะ Kham-dtàawp: phǒm / chǎn bpen lûuk khon diiao khráp / khâ Answer: “I’m an only child.”
  • คำตอบ: ผม / ฉันมีพี่น้อง…..คนครับ / ค่ะ Kham-dtàawp: phǒm / chǎn-mii phîi-náawng…..khon khráp / khâ Answer: “I have ….. brother(s) / sister(s).”

5- Address / ที่อยู่ ( thîi-yùu )

  • คำถาม: คุณพักอยู่แถวไหนครับ / คะ Kham-thǎam: khun phák yùu thǎeeo nǎi khráp / khá Question: “Where do you live?”
  • คำถาม: คุณพักอยู่ที่ไหนครับ / คะ Kham-thǎam: khun phák yùu thîi nǎi khráp / khá Question: “Where do you live?”
  • คำถาม: บ้านคุณอยู่ที่ไหนครับ/คะ Kham-thǎam: bâan khun yùu thîi nǎi khráp / khá Question: “Where is your house?”
  • คำตอบ: ผม / ฉันอยู่แถว…..ครับ / ค่ะ Kham-dtàawp: phǒm / chǎn-yùu thǎaeo…..khráp / khâ Answer: “I live in ….. area.”
  • คำตอบ: บ้านของผม / ฉันอยู่แถว…..ครับ / ค่ะ Kham-dtàawp: bâan khǎawng phǒm / chǎn yùu thǎaeo…..khráp / khâ Answer: “My house is in ….. area.”
  • คำตอบ: ผม / ฉันอยู่ที่…..ครับ / ค่ะ Kham-dtàawp: phǒm / chǎn yùu thîi…..khráp / khâ Answer: “I live in ……”
  • คำตอบ: บ้านของผม / ฉันอยู่ที่…..ครับ / ค่ะ Kham-dtàawp: bâan khǎawng phǒm / chǎn yùu thîi…..khráp / khâ Answer: “My house is in……”

6- Nationality / สัญชาติ ( sǎn-châat )


  • คำถาม: คุณเป็นคนชาติอะไรครับ/คะ Kham-thǎam: khun bpen khon châat à-rai khráp / khá Question: “What is your nationality?”
  • คำตอบ: ผม / ฉันเป็นคน…..ครับ / ค่ะ Kham-dtàawp: phǒm / chǎn bpen khon…..khráp / khâ Answer: “I am……”

Possible Answers

  • “British” = อังกฤษ ( ang-grìt )
  • “American” = อเมริกา ( à-mee-rí-gaa )
  • “French” = ฝรั่งเศษ ( fà-ràng-sèet )
  • “German” = เยอรมัน ( yooe-rá-man )
  • “Italian” = อิตาลี ( ì-dtaa-lîi )
  • “Turkish” = ตุรกี ( dtù-rá-gii )
  • “Russian” = รัซเซีย ( rát-siia )
  • “Australian” = ออสเตเลีย ( áawt-dtee-liia )
  • “Mexican” = แม็กซิโก ( máek-sì-goo )
  • “Canadian” = แคนนาดา ( khaaen-naa-daa )
  • “Chinese” = จีน ( jiin )
  • “Japanese” = ญี่ปุ่น ( yîi-bpùn )
  • “Korean” = เกาหลี ( gao-lǐi )
  • “Singaporian” = สิงค์โปร ( sǐng-khà-bpoo )
  • “Malaysian” = มาเลเซีย ( ma-lee-siia )
  • “Vietnamese” = เวียดนาม ( wîiat-naam )
  • “Laos” = ลาว ( laao )
  • “Burmese” = พม่า ( phá-mâa )
  • “Indonesian” = อินโดนีเซีย ( in-doo-nee-siia )
  • “Filipino” = ฟิลิปปินส์ ( fí-líp-bpin )
  • “Indian” = อินเดีย ( in-diia )

7- School / โรงเรียน ( roong-riian ) and University / มหาวิทยาลัย ( má-hǎa-wít-thá-yaa-lai )

  • คำถาม: คุณเรียนที่ไหนครับ / คะ Kham-thǎam: khun riian thîi nǎi khráp / khá Question: “Which school/university are you studying at?”
  • คำตอบ: ผม / ฉันเรียนที่……ครับ / ค่ะ Kham-dtàawp: phǒm / chǎn riian thîi…..khráp / khâ Answer: “I am studying at…..”
  • คำถาม: คุณเรียนจบจากที่ไหนครับ / คะ Kham-thǎam: khun riian jòb jàak thîi nǎi khráp / khá Question: “Which school/university are you graduated from?”
  • คำตอบ: ผม / ฉันเรียนจบจากที่……ครับ / ค่ะ Kham-dtàawp: phǒm / chǎn riian jòb jàak thîi…..khráp / khâ Answer: “I am graduated from…..”

8- Occupation / อาชีพ ( aa-chîip )

  • คำถาม: คุณทำอาชีพอะไรครับ / คะ Kham-thǎam: khun tham aa-chîip à-rai khráp / khá Question: “What is your occupation ?”
  • คำตอบ: ผม / ฉันเป็น……ครับ / ค่ะ Kham-dtàawp: phǒm / chǎn bpen…..khráp / khâ Answer: “I am …..”
  • “Doctor” = หมอ ( mǎaw )
  • “Nurse” = พยาบาล ( phá-yaa-baan )
  • “Male cook” = พ่อครัว ( phâaw-khruua )
  • “Female cook” = แม่ครัว ( mâae-khruua )
  • “Secretary” = เลขานุการ ( lee-khǎa-nú-gaan )
  • “Teacher” = ครู ( khruu )
  • “Consultant” = ที่ปรึกษา ( thîi-bprùek-sǎa )
  • “Government officer” = ข้าราชการ ( khâa-râat-chá-gaan )
  • “Driver” = คนขับรถ ( khon-khàp-rót )
  • “Singer” = นักร้อง ( nák-ráawng )
  • “ Musician ” = นักดนตรี ( nák-don-dtrii )
  • “Male model” = นายแบบ ( naai-bàaep )
  • “Female model” = นางแบบ ( naang-bàaep )
  • “Actor / actress” = นักแสดง ( nák-sà-daaeng )

9- Hobby / งานอดิเรก ( ngaan à-dì-rèek )

  • คำถาม: งานอดิเรกของคุณคืออะไรครับ / คะ Kham-thǎam: ngan à-dì-rèek khǎawng khun khuue à-rai khráp / khá Question: “What is your hobby?”
  • คำถาม: คุณทำอะไรในเวลาว่างครับ / คะ Kham-thǎam: khun tham à-rai nai wee-laa wâng khráp / khá Question: “What do you do in your free time?”
  • คำตอบ: ผม / ฉันชอบ……ครับ / ค่ะ Kham-dtàawp: phǒm / chǎn châawp…..khráp / khâ Answer: “I like to…….”
  • “Listen to music” = ฟังเพลง ( fang phleeng )
  • “Watch television” = ดูทีวี ( duu thii-wii )
  • “Play games” = เล่นเกมส์ ( lêen gaaem )
  • “Draw pictures” = วาดรูป ( wâat rûup )
  • “Read books” = อ่านหนังสือ ( àan nǎng-sǔue )
  • “ Cook food ” = ทำอาหาร ( tham aa-hǎan )
  • “Take photos” = ถ่ายรูป ( thàai rûup )
  • “ Play with my pet ” = เล่นกับสัตว์เลี้ยง ( lêen gàp sàt-líiang )
  • “Plant tree” = ปลูกต้นไม้ ( bplùuk dtôn-mái )
  • “Browse social media” = เล่นโซเชียลมีเดีย ( lêen soo-chîian mii-dìia )
  • “Sing” = ร้องเพลง ( ráawng phleeng )
  • “Play piano” = เล่นเปียโน ( lêen bpiia-noo )
  • “Play guitar” = เล่นกีตาร์ ( lêen gii-dtâa )
  • “Play drum” = ตีกลอง ( dtii glaawng )
  • “Play violin” = เล่นไวโอลิน ( lêen wai-oo-lin )
  • “ Play sports ” = เล่นกีฬา ( lên gii-laa )
  • “Shopping online” = ซื้อของออนไลน์ ( súue khǎawng aawn-laai )

10- Favorite Things / สิ่งที่ชอบ ( sìng thîi châawp )

Question 1: color.

  • คำถาม: คุณชอบสีอะไรครับ / คะ Kham-thǎam: khun châawp sǐi à-rai khráp / khá Question: “ Which color do you like? “
  • คำตอบ: ผม / ฉันชอบสี…..ครับ / ค่ะ Kham-dtàawp: phǒm / chǎnchâawp sǐi…..khráp / khâ Answer: “I like …….”

Question 2: Food

  • คำถาม: คุณชอบอาหารอะไรครับ / คะ Kham-thǎam: khun châawp aa-hǎan à-rai khráp / khá Question: “ Which food do you like? “

Question 3: Movies

  • คำถาม: คุณชอบหนังเรื่องอะไรครับ / คะ Kham-thǎam: khun châawp nǎng rûueang à-rai khráp / khá Question: “Which movie do you like?”
  • คำตอบ: ผม / ฉันชอบ…..ครับ / ค่ะ Kham-dtàawp: phǒm / chǎn châawp…..khráp / khâ Answer: “I like …….”

Question 4: Books

  • คำถาม: คุณชอบหนังสือเรื่องอะไรครับ / คะ Kham-thǎam: khun châawp nǎng-sǔue rûueang à-rai khráp / khá Question: “Which book do you like?”

First Encounter

“It’s hard to describe myself in Thai or to present myself in Thai.”

You may have this kind of thought if you’ve just started learning Thai and aren’t really confident in your Thai pronunciation . This is normal when you try to speak a language that’s new to you. So here are some tips that will help you with your first few self-introductions.

Thailand is a land of smiles; Thai people really do smile a lot. So any time you’re not confident or are unsure of what to do, just smile. During a self-introduction, smiling helps to create a good first impression.

Smile During Self-Introduction

In Thailand, wâi is an action that Thai people do to pay respect to older people. So when you first meet someone who’s older than you, you can greet them formally by doing this action, and saying sà-wàt-dii at the same time, before introducing yourself.

Wâi During Greeting

3- Nice to meet you

Even if you can’t speak fluently, you can convey that you are happy to know another party by saying ยินดีที่ได้รู้จัก ( yin-dii-thîi-dâi-rúu-jàk ) which is “nice to meet you” in thai language after being introduced to someone.

4- Formal / Informal Way to Introduce Yourself

In Thai, you talk differently to different people, depending on their age and the situation you’re in. In business or when talking with older people, it’s better to more formally introduce yourself in Thai.

But when you talk to friends or people of a similar age, you should use a more informal way to introduce yourself in Thai.

The sentence you speak will sound either formal or informal, depending on the pronoun you use to call yourself and whether you put khráp / khâ at the end of a sentence or not.

5- Introduce Yourself in Thai Essay

How can you introduce yourself in a Thai paragraph? Luckily for you, writing a Thai paragraph about yourself isn’t that different from speaking. You can put all the self-introduction sentences you learned above together in writing.

Sample Composition about Myself in Thai

ฉันชื่อญาดา ชื่อเล่นของฉัน คือ แนน ตอนนี้ฉันอายุ 25 ปี และฉันมีพี่สาว 1 คน บ้านของฉันอยู่แถวอารีย์ ฉันเป็นคนไทย เรียนจบจากมหาวิทยาลัยธรรมศาสตร์ ตอนนี้ทำอาชีพเป็นทนายความ ในเวลาว่างฉันชอบอ่านหนังสือ ฉันชอบเรื่องแฮร์รี่ พ็อตเตอร์เป็นพิเศษ

Chǎn chûue yaa-daa chûue-lêen khǎawng chǎn khuue naaen dtaawn-níi chǎn aa-yú yîi-sìp-hâa bpiii láe chǎn mii phîi-sǎao nùeng khon bâan khǎawng chǎn yùu thǎaeo aa-rii chǎn pen khon thai riian jòp jàak má-hǎ-wít-thá-yaa-lai tham-má-sàat dtaawn-níi tham aa-chîip bpen thá-naai-khwaam nai wee-laa wâng chǎn châawp àan nǎng-sǔue chǎn châawp rûueang haae-rîi-pháwt-dtôoe bpen phí-sèet .

My name is Yada. My nickname is Nan. I’m now twenty-five years old and I have one older sister. My house is in Aree area. I’m Thai and I have graduated from Thammasart University . Now, I work as a lawyer. In my free time, I like to read. My favorite book is Harry Potter.

Writing Self-Introduction in Thai

4. Conclusion

We hope learning how to introduce yourself in Thai isn’t too hard for you. With our “introducing yourself in Thai” lessons, our tips, and a little practice, you’re surely going to get better at self-introduction. As a foreigner, if you introduce yourself in Thai, despite not pronouncing correctly, Thai people will be very impressed. Still, you need to remember to consider the situation you’re in so that you can adjust the level of formality you use. Also, don’t forget to smile, as this helps with first impressions as well.

Once you can introduce yourself perfectly, you should visit to learn and practice other Thai lessons to further master your Thai.

So, reader, do you feel more prepared to introduce yourself in Thai? Why not do so in the comments below? We look forward to hearing from you!

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data analysis in qualitative research process

Qualitative data analysis.

  • Discovering Qualitative Data
  • QDA Software
  • Qualitative Data Analysis Strategies
  • Working Collaboratively
  • Qualitative Methods Texts


Qualitative Research goes beyond the examination of who, what, when, and where, to explore the why and how. Qualitative researchers use surveys, interviews, observation, focus groups, and case studies to understand experiences, gather first hand information, and explore a topic through discussion.

Qualitative Data Analysis (QDA) includes a range of processes used to convert collected data that is typically unstructured into accessible forms for researchers to categorize, interpret, and find meaning.

Computer Assisted Qualitative Data Analysis (CAQDAS) software is used to assist researchers with data management, transcription analysis, coding, mapping, interpretation of text, audio, or video, and other tasks. One advantage of the tools is their ability to store the analysis in a searchable interactive format.

This guide is based on a similar guide created by Jessica Hagman at the University of Illinois University Library.

  • Next: Discovering Qualitative Data >>
  • Last Updated: Dec 14, 2023 10:08 AM
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Grad Coach

Qualitative Data Analysis Methods 101:

The “big 6” methods + examples.

By: Kerryn Warren (PhD) | Reviewed By: Eunice Rautenbach (D.Tech) | May 2020 (Updated April 2023)

Qualitative data analysis methods. Wow, that’s a mouthful. 

If you’re new to the world of research, qualitative data analysis can look rather intimidating. So much bulky terminology and so many abstract, fluffy concepts. It certainly can be a minefield!

Don’t worry – in this post, we’ll unpack the most popular analysis methods , one at a time, so that you can approach your analysis with confidence and competence – whether that’s for a dissertation, thesis or really any kind of research project.

Qualitative data analysis methods

What (exactly) is qualitative data analysis?

To understand qualitative data analysis, we need to first understand qualitative data – so let’s step back and ask the question, “what exactly is qualitative data?”.

Qualitative data refers to pretty much any data that’s “not numbers” . In other words, it’s not the stuff you measure using a fixed scale or complex equipment, nor do you analyse it using complex statistics or mathematics.

So, if it’s not numbers, what is it?

Words, you guessed? Well… sometimes , yes. Qualitative data can, and often does, take the form of interview transcripts, documents and open-ended survey responses – but it can also involve the interpretation of images and videos. In other words, qualitative isn’t just limited to text-based data.

So, how’s that different from quantitative data, you ask?

Simply put, qualitative research focuses on words, descriptions, concepts or ideas – while quantitative research focuses on numbers and statistics . Qualitative research investigates the “softer side” of things to explore and describe , while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them. If you’re keen to learn more about the differences between qual and quant, we’ve got a detailed post over here .

qualitative data analysis vs quantitative data analysis

So, qualitative analysis is easier than quantitative, right?

Not quite. In many ways, qualitative data can be challenging and time-consuming to analyse and interpret. At the end of your data collection phase (which itself takes a lot of time), you’ll likely have many pages of text-based data or hours upon hours of audio to work through. You might also have subtle nuances of interactions or discussions that have danced around in your mind, or that you scribbled down in messy field notes. All of this needs to work its way into your analysis.

Making sense of all of this is no small task and you shouldn’t underestimate it. Long story short – qualitative analysis can be a lot of work! Of course, quantitative analysis is no piece of cake either, but it’s important to recognise that qualitative analysis still requires a significant investment in terms of time and effort.

Need a helping hand?

data analysis in qualitative research process

In this post, we’ll explore qualitative data analysis by looking at some of the most common analysis methods we encounter. We’re not going to cover every possible qualitative method and we’re not going to go into heavy detail – we’re just going to give you the big picture. That said, we will of course includes links to loads of extra resources so that you can learn more about whichever analysis method interests you.

Without further delay, let’s get into it.

The “Big 6” Qualitative Analysis Methods 

There are many different types of qualitative data analysis, all of which serve different purposes and have unique strengths and weaknesses . We’ll start by outlining the analysis methods and then we’ll dive into the details for each.

The 6 most popular methods (or at least the ones we see at Grad Coach) are:

Content analysis

  • Narrative analysis
  • Discourse analysis
  • Thematic analysis
  • Grounded theory (GT)
  • Interpretive phenomenological analysis (IPA)

Let’s take a look at each of them…

QDA Method #1: Qualitative Content Analysis

Content analysis is possibly the most common and straightforward QDA method. At the simplest level, content analysis is used to evaluate patterns within a piece of content (for example, words, phrases or images) or across multiple pieces of content or sources of communication. For example, a collection of newspaper articles or political speeches.

With content analysis, you could, for instance, identify the frequency with which an idea is shared or spoken about – like the number of times a Kardashian is mentioned on Twitter. Or you could identify patterns of deeper underlying interpretations – for instance, by identifying phrases or words in tourist pamphlets that highlight India as an ancient country.

Because content analysis can be used in such a wide variety of ways, it’s important to go into your analysis with a very specific question and goal, or you’ll get lost in the fog. With content analysis, you’ll group large amounts of text into codes , summarise these into categories, and possibly even tabulate the data to calculate the frequency of certain concepts or variables. Because of this, content analysis provides a small splash of quantitative thinking within a qualitative method.

Naturally, while content analysis is widely useful, it’s not without its drawbacks . One of the main issues with content analysis is that it can be very time-consuming , as it requires lots of reading and re-reading of the texts. Also, because of its multidimensional focus on both qualitative and quantitative aspects, it is sometimes accused of losing important nuances in communication.

Content analysis also tends to concentrate on a very specific timeline and doesn’t take into account what happened before or after that timeline. This isn’t necessarily a bad thing though – just something to be aware of. So, keep these factors in mind if you’re considering content analysis. Every analysis method has its limitations , so don’t be put off by these – just be aware of them ! If you’re interested in learning more about content analysis, the video below provides a good starting point.

QDA Method #2: Narrative Analysis 

As the name suggests, narrative analysis is all about listening to people telling stories and analysing what that means . Since stories serve a functional purpose of helping us make sense of the world, we can gain insights into the ways that people deal with and make sense of reality by analysing their stories and the ways they’re told.

You could, for example, use narrative analysis to explore whether how something is being said is important. For instance, the narrative of a prisoner trying to justify their crime could provide insight into their view of the world and the justice system. Similarly, analysing the ways entrepreneurs talk about the struggles in their careers or cancer patients telling stories of hope could provide powerful insights into their mindsets and perspectives . Simply put, narrative analysis is about paying attention to the stories that people tell – and more importantly, the way they tell them.

Of course, the narrative approach has its weaknesses , too. Sample sizes are generally quite small due to the time-consuming process of capturing narratives. Because of this, along with the multitude of social and lifestyle factors which can influence a subject, narrative analysis can be quite difficult to reproduce in subsequent research. This means that it’s difficult to test the findings of some of this research.

Similarly, researcher bias can have a strong influence on the results here, so you need to be particularly careful about the potential biases you can bring into your analysis when using this method. Nevertheless, narrative analysis is still a very useful qualitative analysis method – just keep these limitations in mind and be careful not to draw broad conclusions . If you’re keen to learn more about narrative analysis, the video below provides a great introduction to this qualitative analysis method.

QDA Method #3: Discourse Analysis 

Discourse is simply a fancy word for written or spoken language or debate . So, discourse analysis is all about analysing language within its social context. In other words, analysing language – such as a conversation, a speech, etc – within the culture and society it takes place. For example, you could analyse how a janitor speaks to a CEO, or how politicians speak about terrorism.

To truly understand these conversations or speeches, the culture and history of those involved in the communication are important factors to consider. For example, a janitor might speak more casually with a CEO in a company that emphasises equality among workers. Similarly, a politician might speak more about terrorism if there was a recent terrorist incident in the country.

So, as you can see, by using discourse analysis, you can identify how culture , history or power dynamics (to name a few) have an effect on the way concepts are spoken about. So, if your research aims and objectives involve understanding culture or power dynamics, discourse analysis can be a powerful method.

Because there are many social influences in terms of how we speak to each other, the potential use of discourse analysis is vast . Of course, this also means it’s important to have a very specific research question (or questions) in mind when analysing your data and looking for patterns and themes, or you might land up going down a winding rabbit hole.

Discourse analysis can also be very time-consuming  as you need to sample the data to the point of saturation – in other words, until no new information and insights emerge. But this is, of course, part of what makes discourse analysis such a powerful technique. So, keep these factors in mind when considering this QDA method. Again, if you’re keen to learn more, the video below presents a good starting point.

QDA Method #4: Thematic Analysis

Thematic analysis looks at patterns of meaning in a data set – for example, a set of interviews or focus group transcripts. But what exactly does that… mean? Well, a thematic analysis takes bodies of data (which are often quite large) and groups them according to similarities – in other words, themes . These themes help us make sense of the content and derive meaning from it.

Let’s take a look at an example.

With thematic analysis, you could analyse 100 online reviews of a popular sushi restaurant to find out what patrons think about the place. By reviewing the data, you would then identify the themes that crop up repeatedly within the data – for example, “fresh ingredients” or “friendly wait staff”.

So, as you can see, thematic analysis can be pretty useful for finding out about people’s experiences , views, and opinions . Therefore, if your research aims and objectives involve understanding people’s experience or view of something, thematic analysis can be a great choice.

Since thematic analysis is a bit of an exploratory process, it’s not unusual for your research questions to develop , or even change as you progress through the analysis. While this is somewhat natural in exploratory research, it can also be seen as a disadvantage as it means that data needs to be re-reviewed each time a research question is adjusted. In other words, thematic analysis can be quite time-consuming – but for a good reason. So, keep this in mind if you choose to use thematic analysis for your project and budget extra time for unexpected adjustments.

Thematic analysis takes bodies of data and groups them according to similarities (themes), which help us make sense of the content.

QDA Method #5: Grounded theory (GT) 

Grounded theory is a powerful qualitative analysis method where the intention is to create a new theory (or theories) using the data at hand, through a series of “ tests ” and “ revisions ”. Strictly speaking, GT is more a research design type than an analysis method, but we’ve included it here as it’s often referred to as a method.

What’s most important with grounded theory is that you go into the analysis with an open mind and let the data speak for itself – rather than dragging existing hypotheses or theories into your analysis. In other words, your analysis must develop from the ground up (hence the name). 

Let’s look at an example of GT in action.

Assume you’re interested in developing a theory about what factors influence students to watch a YouTube video about qualitative analysis. Using Grounded theory , you’d start with this general overarching question about the given population (i.e., graduate students). First, you’d approach a small sample – for example, five graduate students in a department at a university. Ideally, this sample would be reasonably representative of the broader population. You’d interview these students to identify what factors lead them to watch the video.

After analysing the interview data, a general pattern could emerge. For example, you might notice that graduate students are more likely to read a post about qualitative methods if they are just starting on their dissertation journey, or if they have an upcoming test about research methods.

From here, you’ll look for another small sample – for example, five more graduate students in a different department – and see whether this pattern holds true for them. If not, you’ll look for commonalities and adapt your theory accordingly. As this process continues, the theory would develop . As we mentioned earlier, what’s important with grounded theory is that the theory develops from the data – not from some preconceived idea.

So, what are the drawbacks of grounded theory? Well, some argue that there’s a tricky circularity to grounded theory. For it to work, in principle, you should know as little as possible regarding the research question and population, so that you reduce the bias in your interpretation. However, in many circumstances, it’s also thought to be unwise to approach a research question without knowledge of the current literature . In other words, it’s a bit of a “chicken or the egg” situation.

Regardless, grounded theory remains a popular (and powerful) option. Naturally, it’s a very useful method when you’re researching a topic that is completely new or has very little existing research about it, as it allows you to start from scratch and work your way from the ground up .

Grounded theory is used to create a new theory (or theories) by using the data at hand, as opposed to existing theories and frameworks.

QDA Method #6:   Interpretive Phenomenological Analysis (IPA)

Interpretive. Phenomenological. Analysis. IPA . Try saying that three times fast…

Let’s just stick with IPA, okay?

IPA is designed to help you understand the personal experiences of a subject (for example, a person or group of people) concerning a major life event, an experience or a situation . This event or experience is the “phenomenon” that makes up the “P” in IPA. Such phenomena may range from relatively common events – such as motherhood, or being involved in a car accident – to those which are extremely rare – for example, someone’s personal experience in a refugee camp. So, IPA is a great choice if your research involves analysing people’s personal experiences of something that happened to them.

It’s important to remember that IPA is subject – centred . In other words, it’s focused on the experiencer . This means that, while you’ll likely use a coding system to identify commonalities, it’s important not to lose the depth of experience or meaning by trying to reduce everything to codes. Also, keep in mind that since your sample size will generally be very small with IPA, you often won’t be able to draw broad conclusions about the generalisability of your findings. But that’s okay as long as it aligns with your research aims and objectives.

Another thing to be aware of with IPA is personal bias . While researcher bias can creep into all forms of research, self-awareness is critically important with IPA, as it can have a major impact on the results. For example, a researcher who was a victim of a crime himself could insert his own feelings of frustration and anger into the way he interprets the experience of someone who was kidnapped. So, if you’re going to undertake IPA, you need to be very self-aware or you could muddy the analysis.

IPA can help you understand the personal experiences of a person or group concerning a major life event, an experience or a situation.

How to choose the right analysis method

In light of all of the qualitative analysis methods we’ve covered so far, you’re probably asking yourself the question, “ How do I choose the right one? ”

Much like all the other methodological decisions you’ll need to make, selecting the right qualitative analysis method largely depends on your research aims, objectives and questions . In other words, the best tool for the job depends on what you’re trying to build. For example:

  • Perhaps your research aims to analyse the use of words and what they reveal about the intention of the storyteller and the cultural context of the time.
  • Perhaps your research aims to develop an understanding of the unique personal experiences of people that have experienced a certain event, or
  • Perhaps your research aims to develop insight regarding the influence of a certain culture on its members.

As you can probably see, each of these research aims are distinctly different , and therefore different analysis methods would be suitable for each one. For example, narrative analysis would likely be a good option for the first aim, while grounded theory wouldn’t be as relevant. 

It’s also important to remember that each method has its own set of strengths, weaknesses and general limitations. No single analysis method is perfect . So, depending on the nature of your research, it may make sense to adopt more than one method (this is called triangulation ). Keep in mind though that this will of course be quite time-consuming.

As we’ve seen, all of the qualitative analysis methods we’ve discussed make use of coding and theme-generating techniques, but the intent and approach of each analysis method differ quite substantially. So, it’s very important to come into your research with a clear intention before you decide which analysis method (or methods) to use.

Start by reviewing your research aims , objectives and research questions to assess what exactly you’re trying to find out – then select a qualitative analysis method that fits. Never pick a method just because you like it or have experience using it – your analysis method (or methods) must align with your broader research aims and objectives.

No single analysis method is perfect, so it can often make sense to adopt more than one  method (this is called triangulation).

Let’s recap on QDA methods…

In this post, we looked at six popular qualitative data analysis methods:

  • First, we looked at content analysis , a straightforward method that blends a little bit of quant into a primarily qualitative analysis.
  • Then we looked at narrative analysis , which is about analysing how stories are told.
  • Next up was discourse analysis – which is about analysing conversations and interactions.
  • Then we moved on to thematic analysis – which is about identifying themes and patterns.
  • From there, we went south with grounded theory – which is about starting from scratch with a specific question and using the data alone to build a theory in response to that question.
  • And finally, we looked at IPA – which is about understanding people’s unique experiences of a phenomenon.

Of course, these aren’t the only options when it comes to qualitative data analysis, but they’re a great starting point if you’re dipping your toes into qualitative research for the first time.

If you’re still feeling a bit confused, consider our private coaching service , where we hold your hand through the research process to help you develop your best work.

data analysis in qualitative research process

Psst… there’s more (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

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Quant data analysis methods 101


Richard N

This has been very helpful. Thank you.


Thank you madam,

Mariam Jaiyeola

Thank you so much for this information


I wonder it so clear for understand and good for me. can I ask additional query?


Very insightful and useful

Susan Nakaweesi

Good work done with clear explanations. Thank you.


Thanks so much for the write-up, it’s really good.

Hemantha Gunasekara

Thanks madam . It is very important .


thank you very good

Pramod Bahulekar

This has been very well explained in simple language . It is useful even for a new researcher.

Derek Jansen

Great to hear that. Good luck with your qualitative data analysis, Pramod!

Adam Zahir

This is very useful information. And it was very a clear language structured presentation. Thanks a lot.


Thank you so much.


very informative sequential presentation


Precise explanation of method.


Hi, may we use 2 data analysis methods in our qualitative research?

Thanks for your comment. Most commonly, one would use one type of analysis method, but it depends on your research aims and objectives.

Dr. Manju Pandey

You explained it in very simple language, everyone can understand it. Thanks so much.


Thank you very much, this is very helpful. It has been explained in a very simple manner that even a layman understands


Thank nicely explained can I ask is Qualitative content analysis the same as thematic analysis?

Thanks for your comment. No, QCA and thematic are two different types of analysis. This article might help clarify –

Rev. Osadare K . J

This is my first time to come across a well explained data analysis. so helpful.

Tina King

I have thoroughly enjoyed your explanation of the six qualitative analysis methods. This is very helpful. Thank you!


Thank you very much, this is well explained and useful


i need a citation of your book.


Thanks a lot , remarkable indeed, enlighting to the best


Hi Derek, What other theories/methods would you recommend when the data is a whole speech?


Keep writing useful artikel.


It is important concept about QDA and also the way to express is easily understandable, so thanks for all.

Carl Benecke

Thank you, this is well explained and very useful.


Very helpful .Thanks.

Hajra Aman

Hi there! Very well explained. Simple but very useful style of writing. Please provide the citation of the text. warm regards

Hillary Mophethe

The session was very helpful and insightful. Thank you

This was very helpful and insightful. Easy to read and understand


As a professional academic writer, this has been so informative and educative. Keep up the good work Grad Coach you are unmatched with quality content for sure.

Keep up the good work Grad Coach you are unmatched with quality content for sure.


Its Great and help me the most. A Million Thanks you Dr.


It is a very nice work

Noble Naade

Very insightful. Please, which of this approach could be used for a research that one is trying to elicit students’ misconceptions in a particular concept ?


This is Amazing and well explained, thanks


great overview


What do we call a research data analysis method that one use to advise or determining the best accounting tool or techniques that should be adopted in a company.

Catherine Shimechero

Informative video, explained in a clear and simple way. Kudos

Van Hmung

Waoo! I have chosen method wrong for my data analysis. But I can revise my work according to this guide. Thank you so much for this helpful lecture.


This has been very helpful. It gave me a good view of my research objectives and how to choose the best method. Thematic analysis it is.

Livhuwani Reineth

Very helpful indeed. Thanku so much for the insight.

Storm Erlank

This was incredibly helpful.

Jack Kanas

Very helpful.


very educative

Wan Roslina

Nicely written especially for novice academic researchers like me! Thank you.


choosing a right method for a paper is always a hard job for a student, this is a useful information, but it would be more useful personally for me, if the author provide me with a little bit more information about the data analysis techniques in type of explanatory research. Can we use qualitative content analysis technique for explanatory research ? or what is the suitable data analysis method for explanatory research in social studies?


that was very helpful for me. because these details are so important to my research. thank you very much

Kumsa Desisa

I learnt a lot. Thank you

Tesfa NT

Relevant and Informative, thanks !


Well-planned and organized, thanks much! 🙂

Dr. Jacob Lubuva

I have reviewed qualitative data analysis in a simplest way possible. The content will highly be useful for developing my book on qualitative data analysis methods. Cheers!

Nyi Nyi Lwin

Clear explanation on qualitative and how about Case study

Ogobuchi Otuu

This was helpful. Thank you


This was really of great assistance, it was just the right information needed. Explanation very clear and follow.

Wow, Thanks for making my life easy

C. U

This was helpful thanks .

Dr. Alina Atif

Very helpful…. clear and written in an easily understandable manner. Thank you.


This was so helpful as it was easy to understand. I’m a new to research thank you so much.


so educative…. but Ijust want to know which method is coding of the qualitative or tallying done?


Thank you for the great content, I have learnt a lot. So helpful


precise and clear presentation with simple language and thank you for that.


very informative content, thank you.

Oscar Kuebutornye

You guys are amazing on YouTube on this platform. Your teachings are great, educative, and informative. kudos!


Brilliant Delivery. You made a complex subject seem so easy. Well done.

Ankit Kumar

Beautifully explained.

Thanks a lot

Kidada Owen-Browne

Is there a video the captures the practical process of coding using automated applications?

Thanks for the comment. We don’t recommend using automated applications for coding, as they are not sufficiently accurate in our experience.

Mathewos Damtew

content analysis can be qualitative research?



Dev get

Thank you very much for such a wonderful content

Kassahun Aman

do you have any material on Data collection

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data analysis in qualitative research process

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  • What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

  • How does social media shape body image in teenagers?
  • How do children and adults interpret healthy eating in the UK?
  • What factors influence employee retention in a large organization?
  • How is anxiety experienced around the world?
  • How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

  • Observations: recording what you have seen, heard, or encountered in detailed field notes.
  • Interviews:  personally asking people questions in one-on-one conversations.
  • Focus groups: asking questions and generating discussion among a group of people.
  • Surveys : distributing questionnaires with open-ended questions.
  • Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
  • You take field notes with observations and reflect on your own experiences of the company culture.
  • You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
  • You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

  • Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
  • Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
  • Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
  • Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
  • Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

  • Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

  • Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

  • Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

  • Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Prevent plagiarism. Run a free check.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

  • Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

  • Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

  • Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

  • Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Chi square goodness of fit test
  • Degrees of freedom
  • Null hypothesis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Inclusion and exclusion criteria

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

  • Grounded theory involves collecting data in order to develop new theories.
  • Ethnography involves immersing yourself in a group or organization to understand its culture.
  • Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
  • Phenomenological research involves investigating phenomena through people’s lived experiences.
  • Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

  • Prepare and organize your data.
  • Review and explore your data.
  • Develop a data coding system.
  • Assign codes to the data.
  • Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Chapter 18. Data Analysis and Coding


Piled before you lie hundreds of pages of fieldnotes you have taken, observations you’ve made while volunteering at city hall. You also have transcripts of interviews you have conducted with the mayor and city council members. What do you do with all this data? How can you use it to answer your original research question (e.g., “How do political polarization and party membership affect local politics?”)? Before you can make sense of your data, you will have to organize and simplify it in a way that allows you to access it more deeply and thoroughly. We call this process coding . [1] Coding is the iterative process of assigning meaning to the data you have collected in order to both simplify and identify patterns. This chapter introduces you to the process of qualitative data analysis and the basic concept of coding, while the following chapter (chapter 19) will take you further into the various kinds of codes and how to use them effectively.

To those who have not yet conducted a qualitative study, the sheer amount of collected data will be a surprise. Qualitative data can be absolutely overwhelming—it may mean hundreds if not thousands of pages of interview transcripts, or fieldnotes, or retrieved documents. How do you make sense of it? Students often want very clear guidelines here, and although I try to accommodate them as much as possible, in the end, analyzing qualitative data is a bit more of an art than a science: “The process of bringing order, structure, and interpretation to a mass of collected data is messy, ambiguous, time-consuming, creative, and fascinating. It does not proceed in a linear fashion: it is not neat. At times, the researcher may feel like an eccentric and tormented artist; not to worry, this is normal” ( Marshall and Rossman 2016:214 ).

To complicate matters further, each approach (e.g., Grounded Theory, deep ethnography, phenomenology) has its own language and bag of tricks (techniques) when it comes to analysis. Grounded Theory, for example, uses in vivo coding to generate new theoretical insights that emerge from a rigorous but open approach to data analysis. Ethnographers, in contrast, are more focused on creating a rich description of the practices, behaviors, and beliefs that operate in a particular field. They are less interested in generating theory and more interested in getting the picture right, valuing verisimilitude in the presentation. And then there are some researchers who seek to account for the qualitative data using almost quantitative methods of analysis, perhaps counting and comparing the uses of certain narrative frames in media accounts of a phenomenon. Qualitative content analysis (QCA) often includes elements of counting (see chapter 17). For these researchers, having very clear hypotheses and clearly defined “variables” before beginning analysis is standard practice, whereas the same would be expressly forbidden by those researchers, like grounded theorists, taking a more emergent approach.

All that said, there are some helpful techniques to get you started, and these will be presented in this and the following chapter. As you become more of an expert yourself, you may want to read more deeply about the tradition that speaks to your research. But know that there are many excellent qualitative researchers that use what works for any given study, who take what they can from each tradition. Most of us find this permissible (but watch out for the methodological purists that exist among us).


Qualitative Data Analysis as a Long Process!

Although most of this and the following chapter will focus on coding, it is important to understand that coding is just one (very important) aspect of the long data-analysis process. We can consider seven phases of data analysis, each of which is important for moving your voluminous data into “findings” that can be reported to others. The first phase involves data organization. This might mean creating a special password-protected Dropbox folder for storing your digital files. It might mean acquiring computer-assisted qualitative data-analysis software ( CAQDAS ) and uploading all transcripts, fieldnotes, and digital files to its storage repository for eventual coding and analysis. Finding a helpful way to store your material can take a lot of time, and you need to be smart about this from the very beginning. Losing data because of poor filing systems or mislabeling is something you want to avoid. You will also want to ensure that you have procedures in place to protect the confidentiality of your interviewees and informants. Filing signed consent forms (with names) separately from transcripts and linking them through an ID number or other code that only you have access to (and store safely) are important.

Once you have all of your material safely and conveniently stored, you will need to immerse yourself in the data. The second phase consists of reading and rereading or viewing and reviewing all of your data. As you do this, you can begin to identify themes or patterns in the data, perhaps writing short memos to yourself about what you are seeing. You are not committing to anything in this third phase but rather keeping your eyes and mind open to what you see. In an actual study, you may very well still be “in the field” or collecting interviews as you do this, and what you see might push you toward either concluding your data collection or expanding so that you can follow a particular group or factor that is emerging as important. For example, you may have interviewed twelve international college students about how they are adjusting to life in the US but realized as you read your transcripts that important gender differences may exist and you have only interviewed two women (and ten men). So you go back out and make sure you have enough female respondents to check your impression that gender matters here. The seven phases do not proceed entirely linearly! It is best to think of them as recursive; conceptually, there is a path to follow, but it meanders and flows.

Coding is the activity of the fourth phase . The second part of this chapter and all of chapter 19 will focus on coding in greater detail. For now, know that coding is the primary tool for analyzing qualitative data and that its purpose is to both simplify and highlight the important elements buried in mounds of data. Coding is a rigorous and systematic process of identifying meaning, patterns, and relationships. It is a more formal extension of what you, as a conscious human being, are trained to do every day when confronting new material and experiences. The “trick” or skill is to learn how to take what you do naturally and semiconsciously in your mind and put it down on paper so it can be documented and verified and tested and refined.

At the conclusion of the coding phase, your material will be searchable, intelligible, and ready for deeper analysis. You can begin to offer interpretations based on all the work you have done so far. This fifth phase might require you to write analytic memos, beginning with short (perhaps a paragraph or two) interpretations of various aspects of the data. You might then attempt stitching together both reflective and analytical memos into longer (up to five pages) general interpretations or theories about the relationships, activities, patterns you have noted as salient.

As you do this, you may be rereading the data, or parts of the data, and reviewing your codes. It’s possible you get to this phase and decide you need to go back to the beginning. Maybe your entire research question or focus has shifted based on what you are now thinking is important. Again, the process is recursive , not linear. The sixth phase requires you to check the interpretations you have generated. Are you really seeing this relationship, or are you ignoring something important you forgot to code? As we don’t have statistical tests to check the validity of our findings as quantitative researchers do, we need to incorporate self-checks on our interpretations. Ask yourself what evidence would exist to counter your interpretation and then actively look for that evidence. Later on, if someone asks you how you know you are correct in believing your interpretation, you will be able to explain what you did to verify this. Guard yourself against accusations of “ cherry-picking ,” selecting only the data that supports your preexisting notion or expectation about what you will find. [2]

The seventh and final phase involves writing up the results of the study. Qualitative results can be written in a variety of ways for various audiences (see chapter 20). Due to the particularities of qualitative research, findings do not exist independently of their being written down. This is different for quantitative research or experimental research, where completed analyses can somewhat speak for themselves. A box of collected qualitative data remains a box of collected qualitative data without its written interpretation. Qualitative research is often evaluated on the strength of its presentation. Some traditions of qualitative inquiry, such as deep ethnography, depend on written thick descriptions, without which the research is wholly incomplete, even nonexistent. All of that practice journaling and writing memos (reflective and analytical) help develop writing skills integral to the presentation of the findings.

Remember that these are seven conceptual phases that operate in roughly this order but with a lot of meandering and recursivity throughout the process. This is very different from quantitative data analysis, which is conducted fairly linearly and processually (first you state a falsifiable research question with hypotheses, then you collect your data or acquire your data set, then you analyze the data, etc.). Things are a bit messier when conducting qualitative research. Embrace the chaos and confusion, and sort your way through the maze. Budget a lot of time for this process. Your research question might change in the middle of data collection. Don’t worry about that. The key to being nimble and flexible in qualitative research is to start thinking and continue thinking about your data, even as it is being collected. All seven phases can be started before all the data has been gathered. Data collection does not always precede data analysis. In some ways, “qualitative data collection is qualitative data analysis.… By integrating data collection and data analysis, instead of breaking them up into two distinct steps, we both enrich our insights and stave off anxiety. We all know the anxiety that builds when we put something off—the longer we put it off, the more anxious we get. If we treat data collection as this mass of work we must do before we can get started on the even bigger mass of work that is analysis, we set ourselves up for massive anxiety” ( Rubin 2021:182–183 ; emphasis added).

The Coding Stage

A code is “a word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data” ( Saldaña 2014:5 ). Codes can be applied to particular sections of or entire transcripts, documents, or even videos. For example, one might code a video taken of a preschooler trying to solve a puzzle as “puzzle,” or one could take the transcript of that video and highlight particular sections or portions as “arranging puzzle pieces” (a descriptive code) or “frustration” (a summative emotion-based code). If the preschooler happily shouts out, “I see it!” you can denote the code “I see it!” (this is an example of an in vivo, participant-created code). As one can see from even this short example, there are many different kinds of codes and many different strategies and techniques for coding, more of which will be discussed in detail in chapter 19. The point to remember is that coding is a rigorous systematic process—to some extent, you are always coding whenever you look at a person or try to make sense of a situation or event, but you rarely do this consciously. Coding is the process of naming what you are seeing and how you are simplifying the data so that you can make sense of it in a way that is consistent with your study and in a way that others can understand and follow and replicate. Another way of saying this is that a code is “a researcher-generated interpretation that symbolizes or translates data” ( Vogt et al. 2014:13 ).

As with qualitative data analysis generally, coding is often done recursively, meaning that you do not merely take one pass through the data to create your codes. Saldaña ( 2014 ) differentiates first-cycle coding from second-cycle coding. The goal of first-cycle coding is to “tag” or identify what emerges as important codes. Note that I said emerges—you don’t always know from the beginning what will be an important aspect of the study or not, so the coding process is really the place for you to begin making the kinds of notes necessary for future analyses. In second-cycle coding, you will want to be much more focused—no longer gathering wholly new codes but synthesizing what you have into metacodes.

You might also conceive of the coding process in four parts (figure 18.1). First, identify a representative or diverse sample set of interview transcripts (or fieldnotes or other documents). This is the group you are going to use to get a sense of what might be emerging. In my own study of career obstacles to success among first-generation and working-class persons in sociology, I might select one interview from each career stage: a graduate student, a junior faculty member, a senior faculty member.

data analysis in qualitative research process

Second, code everything (“ open coding ”). See what emerges, and don’t limit yourself in any way. You will end up with a ton of codes, many more than you will end up with, but this is an excellent way to not foreclose an interesting finding too early in the analysis. Note the importance of starting with a sample of your collected data, because otherwise, open coding all your data is, frankly, impossible and counterproductive. You will just get stuck in the weeds.

Third, pare down your coding list. Where you may have begun with fifty (or more!) codes, you probably want no more than twenty remaining. Go back through the weeds and pull out everything that does not have the potential to bloom into a nicely shaped garden. Note that you should do this before tackling all of your data . Sometimes, however, you might need to rethink the sample you chose. Let’s say that the graduate student interview brought up some interesting gender issues that were pertinent to female-identifying sociologists, but both the junior and the senior faculty members identified as male. In that case, I might read through and open code at least one other interview transcript, perhaps a female-identifying senior faculty member, before paring down my list of codes.

This is also the time to create a codebook if you are using one, a master guide to the codes you are using, including examples (see Sample Codebooks 1 and 2 ). A codebook is simply a document that lists and describes the codes you are using. It is easy to forget what you meant the first time you penciled a coded notation next to a passage, so the codebook allows you to be clear and consistent with the use of your codes. There is not one correct way to create a codebook, but generally speaking, the codebook should include (1) the code (either name or identification number or both), (2) a description of what the code signifies and when and where it should be applied, and (3) an example of the code to help clarify (2). Listing all the codes down somewhere also allows you to organize and reorganize them, which can be part of the analytical process. It is possible that your twenty remaining codes can be neatly organized into five to seven master “themes.” Codebooks can and should develop as you recursively read through and code your collected material. [3]

Fourth, using the pared-down list of codes (or codebook), read through and code all the data. I know many qualitative researchers who work without a codebook, but it is still a good practice, especially for beginners. At the very least, read through your list of codes before you begin this “ closed coding ” step so that you can minimize the chance of missing a passage or section that needs to be coded. The final step is…to do it all again. Or, at least, do closed coding (step four) again. All of this takes a great deal of time, and you should plan accordingly.

Researcher Note

People often say that qualitative research takes a lot of time. Some say this because qualitative researchers often collect their own data. This part can be time consuming, but to me, it’s the analytical process that takes the most time. I usually read every transcript twice before starting to code, then it usually takes me six rounds of coding until I’m satisfied I’ve thoroughly coded everything. Even after the coding, it usually takes me a year to figure out how to put the analysis together into a coherent argument and to figure out what language to use. Just deciding what name to use for a particular group or idea can take months. Understanding this going in can be helpful so that you know to be patient with yourself.

—Jessi Streib, author of The Power of the Past and Privilege Lost 

Note that there is no magic in any of this, nor is there any single “right” way to code or any “correct” codes. What you see in the data will be prompted by your position as a researcher and your scholarly interests. Where the above codes on a preschooler solving a puzzle emerged from my own interest in puzzle solving, another researcher might focus on something wholly different. A scholar of linguistics, for example, may focus instead on the verbalizations made by the child during the discovery process, perhaps even noting particular vocalizations (incidence of grrrs and gritting of the teeth, for example). Your recording of the codes you used is the important part, as it allows other researchers to assess the reliability and validity of your analyses based on those codes. Chapter 19 will provide more details about the kinds of codes you might develop.

Saldaña ( 2014 ) lists seven “necessary personal attributes” for successful coding. To paraphrase, they are the following:

  • Having (or practicing) good organizational skills
  • Perseverance
  • The ability and willingness to deal with ambiguity
  • Creativity, broadly understood, which includes “the ability to think visually, to think symbolically, to think in metaphors, and to think of as many ways as possible to approach a problem” (20)
  • Commitment to being rigorously ethical
  • Having an extensive vocabulary [4]

Writing Analytic Memos during/after Coding

Coding the data you have collected is only one aspect of analyzing it. Too many beginners have coded their data and then wondered what to do next. Coding is meant to help organize your data so that you can see it more clearly, but it is not itself an analysis. Thinking about the data, reviewing the coded data, and bringing in the previous literature (here is where you use your literature review and theory) to help make sense of what you have collected are all important aspects of data analysis. Analytic memos are notes you write to yourself about the data. They can be short (a single page or even a paragraph) or long (several pages). These memos can themselves be the subject of subsequent analytic memoing as part of the recursive process that is qualitative data analysis.

Short analytic memos are written about impressions you have about the data, what is emerging, and what might be of interest later on. You can write a short memo about a particular code, for example, and why this code seems important and where it might connect to previous literature. For example, I might write a paragraph about a “cultural capital” code that I use whenever a working-class sociologist says anything about “not fitting in” with their peers (e.g., not having the right accent or hairstyle or private school background). I could then write a little bit about Bourdieu, who originated the notion of cultural capital, and try to make some connections between his definition and how I am applying it here. I can also use the memo to raise questions or doubts I have about what I am seeing (e.g., Maybe the type of school belongs somewhere else? Is this really the right code?). Later on, I can incorporate some of this writing into the theory section of my final paper or article. Here are some types of things that might form the basis of a short memo: something you want to remember, something you noticed that was new or different, a reaction you had, a suspicion or hunch that you are developing, a pattern you are noticing, any inferences you are starting to draw. Rubin ( 2021 ) advises, “Always include some quotation or excerpt from your dataset…that set you off on this idea. It’s happened to me so many times—I’ll have a really strong reaction to a piece of data, write down some insight without the original quotation or context, and then [later] have no idea what I was talking about and have no way of recreating my insight because I can’t remember what piece of data made me think this way” ( 203 ).

All CAQDAS programs include spaces for writing, generating, and storing memos. You can link a memo to a particular transcript, for example. But you can just as easily keep a notebook at hand in which you write notes to yourself, if you prefer the more tactile approach. Drawing pictures that illustrate themes and patterns you are beginning to see also works. The point is to write early and write often, as these memos are the building blocks of your eventual final product (chapter 20).

In the next chapter (chapter 19), we will go a little deeper into codes and how to use them to identify patterns and themes in your data. This chapter has given you an idea of the process of data analysis, but there is much yet to learn about the elements of that process!

Qualitative Data-Analysis Samples

The following three passages are examples of how qualitative researchers describe their data-analysis practices. The first, by Harvey, is a useful example of how data analysis can shift the original research questions. The second example, by Thai, shows multiple stages of coding and how these stages build upward to conceptual themes and theorization. The third example, by Lamont, shows a masterful use of a variety of techniques to generate theory.

Example 1: “Look Someone in the Eye” by Peter Francis Harvey ( 2022 )

I entered the field intending to study gender socialization. However, through the iterative process of writing fieldnotes, rereading them, conducting further research, and writing extensive analytic memos, my focus shifted. Abductive analysis encourages the search for unexpected findings in light of existing literature. In my early data collection, fieldnotes, and memoing, classed comportment was unmistakably prominent in both schools. I was surprised by how pervasive this bodily socialization proved to be and further surprised by the discrepancies between the two schools.…I returned to the literature to compare my empirical findings.…To further clarify patterns within my data and to aid the search for disconfirming evidence, I constructed data matrices (Miles, Huberman, and Saldaña 2013). While rereading my fieldnotes, I used ATLAS.ti to code and recode key sections (Miles et al. 2013), punctuating this process with additional analytic memos. ( 2022:1420 )

Example 2:” Policing and Symbolic Control” by Mai Thai ( 2022 )

Conventional to qualitative research, my analyses iterated between theory development and testing. Analytical memos were written throughout the data collection, and my analyses using MAXQDA software helped me develop, confirm, and challenge specific themes.…My early coding scheme which included descriptive codes (e.g., uniform inspection, college trips) and verbatim codes of the common terms used by field site participants (e.g., “never quit,” “ghetto”) led me to conceptualize valorization. Later analyses developed into thematic codes (e.g., good citizens, criminality) and process codes (e.g., valorization, criminalization), which helped refine my arguments. ( 2022:1191–1192 )

Example 3: The Dignity of Working Men by Michèle Lamont ( 2000 )

To analyze the interviews, I summarized them in a 13-page document including socio-demographic information as well as information on the boundary work of the interviewees. To facilitate comparisons, I noted some of the respondents’ answers on grids and summarized these on matrix displays using techniques suggested by Miles and Huberman for standardizing and processing qualitative data. Interviews were also analyzed one by one, with a focus on the criteria that each respondent mobilized for the evaluation of status. Moreover, I located each interviewee on several five-point scales pertaining to the most significant dimensions they used to evaluate status. I also compared individual interviewees with respondents who were similar to and different from them, both within and across samples. Finally, I classified all the transcripts thematically to perform a systematic analysis of all the important themes that appear in the interviews, approaching the latter as data against which theoretical questions can be explored. ( 2000:256–257 )

Sample Codebook 1

This is an abridged version of the codebook used to analyze qualitative responses to a question about how class affects careers in sociology. Note the use of numbers to organize the flow, supplemented by highlighting techniques (e.g., bolding) and subcoding numbers.

01. CAPS: Any reference to “capitals” in the response, even if the specific words are not used

01.1: cultural capital 01.2: social capital 01.3: economic capital

(can be mixed: “0.12”= both cultural and asocial capital; “0.23”= both social and economic)

01. CAPS: a reference to “capitals” in which the specific words are used [ bold : thus, 01.23 means that both social capital and economic capital were mentioned specifically

02. DEBT: discussion of debt

02.1: mentions personal issues around debt 02.2: discusses debt but in the abstract only (e.g., “people with debt have to worry”)

03. FirstP: how the response is positioned

03.1: neutral or abstract response 03.2: discusses self (“I”) 03.3: discusses others (“they”)

Sample Coded Passage:

* Question: What other codes jump out to you here? Shouldn’t there be a code for feelings of loneliness or alienation? What about an emotions code ?

Sample Codebook 2

This is an example that uses "word" categories only, with descriptions and examples for each code

Further Readings

Elliott, Victoria. 2018. “Thinking about the Coding Process in Qualitative Analysis.” Qualitative Report 23(11):2850–2861. Address common questions those new to coding ask, including the use of “counting” and how to shore up reliability.

Friese, Susanne. 2019. Qualitative Data Analysis with ATLAS.ti. 3rd ed. A good guide to ATLAS.ti, arguably the most used CAQDAS program. Organized around a series of “skills training” to get you up to speed.

Jackson, Kristi, and Pat Bazeley. 2019. Qualitative Data Analysis with NVIVO . 3rd ed. Thousand Oaks, CA: SAGE. If you want to use the CAQDAS program NVivo, this is a good affordable guide to doing so. Includes copious examples, figures, and graphic displays.

LeCompte, Margaret D. 2000. “Analyzing Qualitative Data.” Theory into Practice 39(3):146–154. A very practical and readable guide to the entire coding process, with particular applicability to educational program evaluation/policy analysis.

Miles, Matthew B., and A. Michael Huberman. 1994. Qualitative Data Analysis: An Expanded Sourcebook . 2nd ed. Thousand Oaks, CA: SAGE. A classic reference on coding. May now be superseded by Miles, Huberman, and Saldaña (2019).

Miles, Matthew B., A. Michael Huberman, and Johnny Saldaña. 2019. Qualitative Data Analysis: A Methods Sourcebook . 4th ed. Thousand Oaks, CA; SAGE. A practical methods sourcebook for all qualitative researchers at all levels using visual displays and examples. Highly recommended.

Saldaña, Johnny. 2014. The Coding Manual for Qualitative Researchers . 2nd ed. Thousand Oaks, CA: SAGE. The most complete and comprehensive compendium of coding techniques out there. Essential reference.

Silver, Christina. 2014. Using Software in Qualitative Research: A Step-by-Step Guide. 2nd ed. Thousand Oaks, CA; SAGE. If you are unsure which CAQDAS program you are interested in using or want to compare the features and usages of each, this guidebook is quite helpful.

Vogt, W. Paul, Elaine R. Vogt, Diane C. Gardner, and Lynne M. Haeffele2014. Selecting the Right Analyses for Your Data: Quantitative, Qualitative, and Mixed Methods . New York: The Guilford Press. User-friendly reference guide to all forms of analysis; may be particularly helpful for those engaged in mixed-methods research.

  • When you have collected content (historical, media, archival) that interests you because of its communicative aspect, content analysis (chapter 17) is appropriate. Whereas content analysis is both a research method and a tool of analysis, coding is a tool of analysis that can be used for all kinds of data to address any number of questions. Content analysis itself includes coding. ↵
  • Scientific research, whether quantitative or qualitative, demands we keep an open mind as we conduct our research, that we are “neutral” regarding what is actually there to find. Students who are trained in non-research-based disciplines such as the arts or philosophy or who are (admirably) focused on pursuing social justice can too easily fall into the trap of thinking their job is to “demonstrate” something through the data. That is not the job of a researcher. The job of a researcher is to present (and interpret) findings—things “out there” (even if inside other people’s hearts and minds). One helpful suggestion: when formulating your research question, if you already know the answer (or think you do), scrap that research. Ask a question to which you do not yet know the answer. ↵
  • Codebooks are particularly useful for collaborative research so that codes are applied and interpreted similarly. If you are working with a team of researchers, you will want to take extra care that your codebooks remain in synch and that any refinements or developments are shared with fellow coders. You will also want to conduct an “intercoder reliability” check, testing whether the codes you have developed are clearly identifiable so that multiple coders are using them similarly. Messy, unclear codes that can be interpreted differently by different coders will make it much more difficult to identify patterns across the data. ↵
  • Note that this is important for creating/denoting new codes. The vocabulary does not need to be in English or any particular language. You can use whatever words or phrases capture what it is you are seeing in the data. ↵

A first-cycle coding process in which gerunds are used to identify conceptual actions, often for the purpose of tracing change and development over time.  Widely used in the Grounded Theory approach.

A first-cycle coding process in which terms or phrases used by the participants become the code applied to a particular passage.  It is also known as “verbatim coding,” “indigenous coding,” “natural coding,” “emic coding,” and “inductive coding,” depending on the tradition of inquiry of the researcher.  It is common in Grounded Theory approaches and has even given its name to one of the primary CAQDAS programs (“NVivo”).

Computer-assisted qualitative data-analysis software.  These are software packages that can serve as a repository for qualitative data and that enable coding, memoing, and other tools of data analysis.  See chapter 17 for particular recommendations.

The purposeful selection of some data to prove a preexisting expectation or desired point of the researcher where other data exists that would contradict the interpretation offered.  Note that it is not cherry picking to select a quote that typifies the main finding of a study, although it would be cherry picking to select a quote that is atypical of a body of interviews and then present it as if it is typical.

A preliminary stage of coding in which the researcher notes particular aspects of interest in the data set and begins creating codes.  Later stages of coding refine these preliminary codes.  Note: in Grounded Theory , open coding has a more specific meaning and is often called initial coding : data are broken down into substantive codes in a line-by-line manner, and incidents are compared with one another for similarities and differences until the core category is found.  See also closed coding .

A set of codes, definitions, and examples used as a guide to help analyze interview data.  Codebooks are particularly helpful and necessary when research analysis is shared among members of a research team, as codebooks allow for standardization of shared meanings and code attributions.

The final stages of coding after the refinement of codes has created a complete list or codebook in which all the data is coded using this refined list or codebook.  Compare to open coding .

A first-cycle coding process in which emotions and emotionally salient passages are tagged.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

data analysis in qualitative research process

The Ultimate Guide to Qualitative Research - Part 2: Handling Qualitative Data

data analysis in qualitative research process

  • Handling qualitative data
  • Transcripts
  • Field notes
  • Survey data and responses
  • Visual and audio data
  • Data organization
  • Data coding
  • Coding frame
  • Auto and smart coding
  • Organizing codes

What is qualitative data analysis?

Qualitative data analysis methods, how do you analyze qualitative data, content analysis, thematic analysis.

  • Thematic analysis vs. content analysis
  • Narrative research

Phenomenological research

Discourse analysis, grounded theory.

  • Deductive reasoning
  • Inductive reasoning
  • Inductive vs. deductive reasoning
  • Qualitative data interpretation
  • Qualitative analysis software

Qualitative data analysis

Analyzing qualitative data is the next step after you have completed the use of qualitative data collection methods . The qualitative analysis process aims to identify themes and patterns that emerge across the data.

data analysis in qualitative research process

In simplified terms, qualitative research methods involve non-numerical data collection followed by an explanation based on the attributes of the data . For example, if you are asked to explain in qualitative terms a thermal image displayed in multiple colors, then you would explain the color differences rather than the heat's numerical value. If you have a large amount of data (e.g., of group discussions or observations of real-life situations), the next step is to transcribe and prepare the raw data for subsequent analysis.

Researchers can conduct studies fully based on qualitative methodology, or researchers can preface a quantitative research study with a qualitative study to identify issues that were not originally envisioned but are important to the study. Quantitative researchers may also collect and analyze qualitative data following their quantitative analyses to better understand the meanings behind their statistical results.

Conducting qualitative research can especially help build an understanding of how and why certain outcomes were achieved (in addition to what was achieved). For example, qualitative data analysis is often used for policy and program evaluation research since it can answer certain important questions more efficiently and effectively than quantitative approaches.

data analysis in qualitative research process

Qualitative data analysis can also answer important questions about the relevance, unintended effects, and impact of programs, such as:

  • Were expectations reasonable?
  • Did processes operate as expected?
  • Were key players able to carry out their duties?
  • Were there any unintended effects of the program?

The importance of qualitative data analysis

Qualitative approaches have the advantage of allowing for more diversity in responses and the capacity to adapt to new developments or issues during the research process itself. While qualitative analysis of data can be demanding and time-consuming to conduct, many fields of research utilize qualitative software tools that have been specifically developed to provide more succinct, cost-efficient, and timely results.

data analysis in qualitative research process

Qualitative data analysis is an important part of research and building greater understanding across fields for a number of reasons. First, cases for qualitative data analysis can be selected purposefully according to whether they typify certain characteristics or contextual locations. In other words, qualitative data permits deep immersion into a topic, phenomenon, or area of interest. Rather than seeking generalizability to the population the sample of participants represent, qualitative research aims to construct an in-depth and nuanced understanding of the research topic.

Secondly, the role or position of the researcher in qualitative analysis of data is given greater critical attention. This is because, in qualitative data analysis, the possibility of the researcher taking a ‘neutral' or transcendent position is seen as more problematic in practical and/or philosophical terms. Hence, qualitative researchers are often exhorted to reflect on their role in the research process and make this clear in the analysis.

data analysis in qualitative research process

Thirdly, while qualitative data analysis can take a wide variety of forms, it largely differs from quantitative research in the focus on language, signs, experiences, and meaning. In addition, qualitative approaches to analysis are often holistic and contextual rather than analyzing the data in a piecemeal fashion or removing the data from its context. Qualitative approaches thus allow researchers to explore inquiries from directions that could not be accessed with only numerical quantitative data.

Establishing research rigor

Systematic and transparent approaches to the analysis of qualitative data are essential for rigor . For example, many qualitative research methods require researchers to carefully code data and discern and document themes in a consistent and credible way.

data analysis in qualitative research process

Perhaps the most traditional division in the way qualitative and quantitative research have been used in the social sciences is for qualitative methods to be used for exploratory purposes (e.g., to generate new theory or propositions) or to explain puzzling quantitative results, while quantitative methods are used to test hypotheses.

data analysis in qualitative research process

After you’ve collected relevant data , what is the best way to look at your data ? As always, it will depend on your research question . For instance, if you employed an observational research method to learn about a group’s shared practices, an ethnographic approach could be appropriate to explain the various dimensions of culture. If you collected textual data to understand how people talk about something, then a discourse analysis approach might help you generate key insights about language and communication.

data analysis in qualitative research process

The qualitative data coding process involves iterative categorization and recategorization, ensuring the evolution of the analysis to best represent the data. The procedure typically concludes with the interpretation of patterns and trends identified through the coding process.

To start off, let’s look at two broad approaches to data analysis.

Deductive analysis

Deductive analysis is guided by pre-existing theories or ideas. It starts with a theoretical framework , which is then used to code the data. The researcher can thus use this theoretical framework to interpret their data and answer their research question .

The key steps include coding the data based on the predetermined concepts or categories and using the theory to guide the interpretation of patterns among the codings. Deductive analysis is particularly useful when researchers aim to verify or extend an existing theory within a new context.

Inductive analysis

Inductive analysis involves the generation of new theories or ideas based on the data. The process starts without any preconceived theories or codes, and patterns, themes, and categories emerge out of the data.

data analysis in qualitative research process

The researcher codes the data to capture any concepts or patterns that seem interesting or important to the research question . These codes are then compared and linked, leading to the formation of broader categories or themes. The main goal of inductive analysis is to allow the data to 'speak for itself' rather than imposing pre-existing expectations or ideas onto the data.

Deductive and inductive approaches can be seen as sitting on opposite poles, and all research falls somewhere within that spectrum. Most often, qualitative analysis approaches blend both deductive and inductive elements to contribute to the existing conversation around a topic while remaining open to potential unexpected findings. To help you make informed decisions about which qualitative data analysis approach fits with your research objectives, let's look at some of the common approaches for qualitative data analysis.

Content analysis is a research method used to identify patterns and themes within qualitative data. This approach involves systematically coding and categorizing specific aspects of the content in the data to uncover trends and patterns. An often important part of content analysis is quantifying frequencies and patterns of words or characteristics present in the data .

It is a highly flexible technique that can be adapted to various data types , including text, images, and audiovisual content . While content analysis can be exploratory in nature, it is also common to use pre-established theories and follow a more deductive approach to categorizing and quantifying the qualitative data.

data analysis in qualitative research process

Thematic analysis is a method used to identify, analyze, and report patterns or themes within the data. This approach moves beyond counting explicit words or phrases and focuses on also identifying implicit concepts and themes within the data.

data analysis in qualitative research process

Researchers conduct detailed coding of the data to ascertain repeated themes or patterns of meaning. Codes can be categorized into themes, and the researcher can analyze how the themes relate to one another. Thematic analysis is flexible in terms of the research framework, allowing for both inductive (data-driven) and deductive (theory-driven) approaches. The outcome is a rich, detailed, and complex account of the data.

Grounded theory is a systematic qualitative research methodology that is used to inductively generate theory that is 'grounded' in the data itself. Analysis takes place simultaneously with data collection , and researchers iterate between data collection and analysis until a comprehensive theory is developed.

Grounded theory is characterized by simultaneous data collection and analysis, the development of theoretical codes from the data, purposeful sampling of participants, and the constant comparison of data with emerging categories and concepts. The ultimate goal is to create a theoretical explanation that fits the data and answers the research question .

Discourse analysis is a qualitative research approach that emphasizes the role of language in social contexts. It involves examining communication and language use beyond the level of the sentence, considering larger units of language such as texts or conversations.

data analysis in qualitative research process

Discourse analysts typically investigate how social meanings and understandings are constructed in different contexts, emphasizing the connection between language and power. It can be applied to texts of all kinds, including interviews , documents, case studies , and social media posts.

Phenomenological research focuses on exploring how human beings make sense of an experience and delves into the essence of this experience. It strives to understand people's perceptions, perspectives, and understandings of a particular situation or phenomenon.

data analysis in qualitative research process

It involves in-depth engagement with participants, often through interviews or conversations, to explore their lived experiences. The goal is to derive detailed descriptions of the essence of the experience and to interpret what insights or implications this may bear on our understanding of this phenomenon.

data analysis in qualitative research process

Whatever your data analysis approach, start with ATLAS.ti

Qualitative data analysis done quickly and intuitively with ATLAS.ti. Download a free trial today.

Now that we've summarized the major approaches to data analysis, let's look at the broader process of research and data analysis. Suppose you need to do some research to find answers to any kind of research question, be it an academic inquiry, business problem, or policy decision. In that case, you need to collect some data. There are many methods of collecting data: you can collect primary data yourself by conducting interviews, focus groups , or a survey , for instance. Another option is to use secondary data sources. These are data previously collected for other projects, historical records, reports, statistics – basically everything that exists already and can be relevant to your research.

data analysis in qualitative research process

The data you collect should always be a good fit for your research question . For example, if you are interested in how many people in your target population like your brand compared to others, it is no use to conduct interviews or a few focus groups . The sample will be too small to get a representative picture of the population. If your questions are about "how many….", "what is the spread…" etc., you need to conduct quantitative research . If you are interested in why people like different brands, their motives, and their experiences, then conducting qualitative research can provide you with the answers you are looking for.

Let's describe the important steps involved in conducting research.

Step 1: Planning the research

As the saying goes: "Garbage in, garbage out." Suppose you find out after you have collected data that

  • you talked to the wrong people
  • asked the wrong questions
  • a couple of focus groups sessions would have yielded better results because of the group interaction, or
  • a survey including a few open-ended questions sent to a larger group of people would have been sufficient and required less effort.

Think thoroughly about sampling, the questions you will be asking, and in which form. If you conduct a focus group or an interview, you are the research instrument, and your data collection will only be as good as you are. If you have never done it before, seek some training and practice. If you have other people do it, make sure they have the skills.

data analysis in qualitative research process

Step 2: Preparing the data

When you conduct focus groups or interviews, think about how to transcribe them. Do you want to run them online or offline? If online, check out which tools can serve your needs, both in terms of functionality and cost. For any audio or video recordings , you can consider using automatic transcription software or services. Automatically generated transcripts can save you time and money, but they still need to be checked. If you don't do this yourself, make sure that you instruct the person doing it on how to prepare the data.

  • How should the final transcript be formatted for later analysis?
  • Which names and locations should be anonymized?
  • What kind of speaker IDs to use?

What about survey data ? Some survey data programs will immediately provide basic descriptive-level analysis of the responses. ATLAS.ti will support you with the analysis of the open-ended questions. For this, you need to export your data as an Excel file. ATLAS.ti's survey import wizard will guide you through the process.

Other kinds of data such as images, videos, audio recordings, text, and more can be imported to ATLAS.ti. You can organize all your data into groups and write comments on each source of data to maintain a systematic organization and documentation of your data.

data analysis in qualitative research process

Step 3: Exploratory data analysis

You can run a few simple exploratory analyses to get to know your data. For instance, you can create a word list or word cloud of all your text data or compare and contrast the words in different documents. You can also let ATLAS.ti find relevant concepts for you. There are many tools available that can automatically code your text data, so you can also use these codings to explore your data and refine your coding.

data analysis in qualitative research process

For instance, you can get a feeling for the sentiments expressed in the data. Who is more optimistic, pessimistic, or neutral in their responses? ATLAS.ti can auto-code the positive, negative, and neutral sentiments in your data. Naturally, you can also simply browse through your data and highlight relevant segments that catch your attention or attach codes to begin condensing the data.

data analysis in qualitative research process

Step 4: Build a code system

Whether you start with auto-coding or manual coding, after having generated some first codes, you need to get some order in your code system to develop a cohesive understanding. You can build your code system by sorting codes into groups and creating categories and subcodes. As this process requires reading and re-reading your data, you will become very familiar with your data. Counting on a tool like ATLAS.ti qualitative data analysis software will support you in the process and make it easier to review your data, modify codings if necessary, change code labels, and write operational definitions to explain what each code means.

data analysis in qualitative research process

Step 5: Query your coded data and write up the analysis

Once you have coded your data, it is time to take the analysis a step further. When using software for qualitative data analysis , it is easy to compare and contrast subsets in your data, such as groups of participants or sets of themes.

data analysis in qualitative research process

For instance, you can query the various opinions of female vs. male respondents. Is there a difference between consumers from rural or urban areas or among different age groups or educational levels? Which codes occur together throughout the data set? Are there relationships between various concepts, and if so, why?

Step 6: Data visualization

Data visualization brings your data to life. It is a powerful way of seeing patterns and relationships in your data. For instance, diagrams allow you to see how your codes are distributed across documents or specific subpopulations in your data.

data analysis in qualitative research process

Exploring coded data on a canvas, moving around code labels in a virtual space, linking codes and other elements of your data set, and thinking about how they are related and why – all of these will advance your analysis and spur further insights. Visuals are also great for communicating results to others.

Step 7: Data presentation

The final step is to summarize the analysis in a written report . You can now put together the memos you have written about the various topics, select some salient quotes that illustrate your writing, and add visuals such as tables and diagrams. If you follow the steps above, you will already have all the building blocks, and you just have to put them together in a report or presentation.

When preparing a report or a presentation, keep your audience in mind. Does your audience better understand numbers than long sections of detailed interpretations? If so, add more tables, charts, and short supportive data quotes to your report or presentation. If your audience loves a good interpretation, add your full-length memos and walk your audience through your conceptual networks and illustrative data quotes.

data analysis in qualitative research process

Qualitative data analysis begins with ATLAS.ti

For tools that can make the most out of your data, check out ATLAS.ti with a free trial.

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  • Sentiment Analysis
  • Surveys & Feedback Collection
  • Try Thematic

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data analysis in qualitative research process

Qualitative Data Analysis: Step-by-Step Guide (Manual vs. Automatic)

When we conduct qualitative methods of research, need to explain changes in metrics or understand people's opinions, we always turn to qualitative data. Qualitative data is typically generated through:

  • Interview transcripts
  • Surveys with open-ended questions
  • Contact center transcripts
  • Texts and documents
  • Audio and video recordings
  • Observational notes

Compared to quantitative data, which captures structured information, qualitative data is unstructured and has more depth. It can answer our questions, can help formulate hypotheses and build understanding.

It's important to understand the differences between quantitative data & qualitative data . But unfortunately, analyzing qualitative data is difficult. While tools like Excel, Tableau and PowerBI crunch and visualize quantitative data with ease, there are a limited number of mainstream tools for analyzing qualitative data . The majority of qualitative data analysis still happens manually.

That said, there are two new trends that are changing this. First, there are advances in natural language processing (NLP) which is focused on understanding human language. Second, there is an explosion of user-friendly software designed for both researchers and businesses. Both help automate the qualitative data analysis process.

In this post we want to teach you how to conduct a successful qualitative data analysis. There are two primary qualitative data analysis methods; manual & automatic. We will teach you how to conduct the analysis manually, and also, automatically using software solutions powered by NLP. We’ll guide you through the steps to conduct a manual analysis, and look at what is involved and the role technology can play in automating this process.

More businesses are switching to fully-automated analysis of qualitative customer data because it is cheaper, faster, and just as accurate. Primarily, businesses purchase subscriptions to feedback analytics platforms so that they can understand customer pain points and sentiment.

Overwhelming quantity of feedback

We’ll take you through 5 steps to conduct a successful qualitative data analysis. Within each step we will highlight the key difference between the manual, and automated approach of qualitative researchers. Here's an overview of the steps:

The 5 steps to doing qualitative data analysis

  • Gathering and collecting your qualitative data
  • Organizing and connecting into your qualitative data
  • Coding your qualitative data
  • Analyzing the qualitative data for insights
  • Reporting on the insights derived from your analysis

What is Qualitative Data Analysis?

Qualitative data analysis is a process of gathering, structuring and interpreting qualitative data to understand what it represents.

Qualitative data is non-numerical and unstructured. Qualitative data generally refers to text, such as open-ended responses to survey questions or user interviews, but also includes audio, photos and video.

Businesses often perform qualitative data analysis on customer feedback. And within this context, qualitative data generally refers to verbatim text data collected from sources such as reviews, complaints, chat messages, support centre interactions, customer interviews, case notes or social media comments.

How is qualitative data analysis different from quantitative data analysis?

Understanding the differences between quantitative & qualitative data is important. When it comes to analyzing data, Qualitative Data Analysis serves a very different role to Quantitative Data Analysis. But what sets them apart?

Qualitative Data Analysis dives into the stories hidden in non-numerical data such as interviews, open-ended survey answers, or notes from observations. It uncovers the ‘whys’ and ‘hows’ giving a deep understanding of people’s experiences and emotions.

Quantitative Data Analysis on the other hand deals with numerical data, using statistics to measure differences, identify preferred options, and pinpoint root causes of issues.  It steps back to address questions like "how many" or "what percentage" to offer broad insights we can apply to larger groups.

In short, Qualitative Data Analysis is like a microscope,  helping us understand specific detail. Quantitative Data Analysis is like the telescope, giving us a broader perspective. Both are important, working together to decode data for different objectives.

Qualitative Data Analysis methods

Once all the data has been captured, there are a variety of analysis techniques available and the choice is determined by your specific research objectives and the kind of data you’ve gathered.  Common qualitative data analysis methods include:

Content Analysis

This is a popular approach to qualitative data analysis. Other qualitative analysis techniques may fit within the broad scope of content analysis. Thematic analysis is a part of the content analysis.  Content analysis is used to identify the patterns that emerge from text, by grouping content into words, concepts, and themes. Content analysis is useful to quantify the relationship between all of the grouped content. The Columbia School of Public Health has a detailed breakdown of content analysis .

Narrative Analysis

Narrative analysis focuses on the stories people tell and the language they use to make sense of them.  It is particularly useful in qualitative research methods where customer stories are used to get a deep understanding of customers’ perspectives on a specific issue. A narrative analysis might enable us to summarize the outcomes of a focused case study.

Discourse Analysis

Discourse analysis is used to get a thorough understanding of the political, cultural and power dynamics that exist in specific situations.  The focus of discourse analysis here is on the way people express themselves in different social contexts. Discourse analysis is commonly used by brand strategists who hope to understand why a group of people feel the way they do about a brand or product.

Thematic Analysis

Thematic analysis is used to deduce the meaning behind the words people use. This is accomplished by discovering repeating themes in text. These meaningful themes reveal key insights into data and can be quantified, particularly when paired with sentiment analysis . Often, the outcome of thematic analysis is a code frame that captures themes in terms of codes, also called categories. So the process of thematic analysis is also referred to as “coding”. A common use-case for thematic analysis in companies is analysis of customer feedback.

Grounded Theory

Grounded theory is a useful approach when little is known about a subject. Grounded theory starts by formulating a theory around a single data case. This means that the theory is “grounded”. Grounded theory analysis is based on actual data, and not entirely speculative. Then additional cases can be examined to see if they are relevant and can add to the original grounded theory.

Methods of qualitative data analysis; approaches and techniques to qualitative data analysis

Challenges of Qualitative Data Analysis

While Qualitative Data Analysis offers rich insights, it comes with its challenges. Each unique QDA method has its unique hurdles. Let’s take a look at the challenges researchers and analysts might face, depending on the chosen method.

  • Time and Effort (Narrative Analysis): Narrative analysis, which focuses on personal stories, demands patience. Sifting through lengthy narratives to find meaningful insights can be time-consuming, requires dedicated effort.
  • Being Objective (Grounded Theory): Grounded theory, building theories from data, faces the challenges of personal biases. Staying objective while interpreting data is crucial, ensuring conclusions are rooted in the data itself.
  • Complexity (Thematic Analysis): Thematic analysis involves identifying themes within data, a process that can be intricate. Categorizing and understanding themes can be complex, especially when each piece of data varies in context and structure. Thematic Analysis software can simplify this process.
  • Generalizing Findings (Narrative Analysis): Narrative analysis, dealing with individual stories, makes drawing broad challenging. Extending findings from a single narrative to a broader context requires careful consideration.
  • Managing Data (Thematic Analysis): Thematic analysis involves organizing and managing vast amounts of unstructured data, like interview transcripts. Managing this can be a hefty task, requiring effective data management strategies.
  • Skill Level (Grounded Theory): Grounded theory demands specific skills to build theories from the ground up. Finding or training analysts with these skills poses a challenge, requiring investment in building expertise.

Benefits of qualitative data analysis

Qualitative Data Analysis (QDA) is like a versatile toolkit, offering a tailored approach to understanding your data. The benefits it offers are as diverse as the methods. Let’s explore why choosing the right method matters.

  • Tailored Methods for Specific Needs: QDA isn't one-size-fits-all. Depending on your research objectives and the type of data at hand, different methods offer unique benefits. If you want emotive customer stories, narrative analysis paints a strong picture. When you want to explain a score, thematic analysis reveals insightful patterns
  • Flexibility with Thematic Analysis: thematic analysis is like a chameleon in the toolkit of QDA. It adapts well to different types of data and research objectives, making it a top choice for any qualitative analysis.
  • Deeper Understanding, Better Products: QDA helps you dive into people's thoughts and feelings. This deep understanding helps you build products and services that truly matches what people want, ensuring satisfied customers
  • Finding the Unexpected: Qualitative data often reveals surprises that we miss in quantitative data. QDA offers us new ideas and perspectives, for insights we might otherwise miss.
  • Building Effective Strategies: Insights from QDA are like strategic guides. They help businesses in crafting plans that match people’s desires.
  • Creating Genuine Connections: Understanding people’s experiences lets businesses connect on a real level. This genuine connection helps build trust and loyalty, priceless for any business.

How to do Qualitative Data Analysis: 5 steps

Now we are going to show how you can do your own qualitative data analysis. We will guide you through this process step by step. As mentioned earlier, you will learn how to do qualitative data analysis manually , and also automatically using modern qualitative data and thematic analysis software.

To get best value from the analysis process and research process, it’s important to be super clear about the nature and scope of the question that’s being researched. This will help you select the research collection channels that are most likely to help you answer your question.

Depending on if you are a business looking to understand customer sentiment, or an academic surveying a school, your approach to qualitative data analysis will be unique.

Once you’re clear, there’s a sequence to follow. And, though there are differences in the manual and automatic approaches, the process steps are mostly the same.

The use case for our step-by-step guide is a company looking to collect data (customer feedback data), and analyze the customer feedback - in order to improve customer experience. By analyzing the customer feedback the company derives insights about their business and their customers. You can follow these same steps regardless of the nature of your research. Let’s get started.

Step 1: Gather your qualitative data and conduct research (Conduct qualitative research)

The first step of qualitative research is to do data collection. Put simply, data collection is gathering all of your data for analysis. A common situation is when qualitative data is spread across various sources.

Classic methods of gathering qualitative data

Most companies use traditional methods for gathering qualitative data: conducting interviews with research participants, running surveys, and running focus groups. This data is typically stored in documents, CRMs, databases and knowledge bases. It’s important to examine which data is available and needs to be included in your research project, based on its scope.

Using your existing qualitative feedback

As it becomes easier for customers to engage across a range of different channels, companies are gathering increasingly large amounts of both solicited and unsolicited qualitative feedback.

Most organizations have now invested in Voice of Customer programs , support ticketing systems, chatbot and support conversations, emails and even customer Slack chats.

These new channels provide companies with new ways of getting feedback, and also allow the collection of unstructured feedback data at scale.

The great thing about this data is that it contains a wealth of valubale insights and that it’s already there! When you have a new question about user behavior or your customers, you don’t need to create a new research study or set up a focus group. You can find most answers in the data you already have.

Typically, this data is stored in third-party solutions or a central database, but there are ways to export it or connect to a feedback analysis solution through integrations or an API.

Utilize untapped qualitative data channels

There are many online qualitative data sources you may not have considered. For example, you can find useful qualitative data in social media channels like Twitter or Facebook. Online forums, review sites, and online communities such as Discourse or Reddit also contain valuable data about your customers, or research questions.

If you are considering performing a qualitative benchmark analysis against competitors - the internet is your best friend. Gathering feedback in competitor reviews on sites like Trustpilot, G2, Capterra, Better Business Bureau or on app stores is a great way to perform a competitor benchmark analysis.

Customer feedback analysis software often has integrations into social media and review sites, or you could use a solution like DataMiner to scrape the reviews. reviews of the product Airtable. You could pull reviews from G2 for your analysis.

Step 2: Connect & organize all your qualitative data

Now you all have this qualitative data but there’s a problem, the data is unstructured. Before feedback can be analyzed and assigned any value, it needs to be organized in a single place. Why is this important? Consistency!

If all data is easily accessible in one place and analyzed in a consistent manner, you will have an easier time summarizing and making decisions based on this data.

The manual approach to organizing your data

The classic method of structuring qualitative data is to plot all the raw data you’ve gathered into a spreadsheet.

Typically, research and support teams would share large Excel sheets and different business units would make sense of the qualitative feedback data on their own. Each team collects and organizes the data in a way that best suits them, which means the feedback tends to be kept in separate silos.

An alternative and a more robust solution is to store feedback in a central database, like Snowflake or Amazon Redshift .

Keep in mind that when you organize your data in this way, you are often preparing it to be imported into another software. If you go the route of a database, you would need to use an API to push the feedback into a third-party software.

Computer-assisted qualitative data analysis software (CAQDAS)

Traditionally within the manual analysis approach (but not always), qualitative data is imported into CAQDAS software for coding.

In the early 2000s, CAQDAS software was popularised by developers such as ATLAS.ti, NVivo and MAXQDA and eagerly adopted by researchers to assist with the organizing and coding of data.  

The benefits of using computer-assisted qualitative data analysis software:

  • Assists in the organizing of your data
  • Opens you up to exploring different interpretations of your data analysis
  • Allows you to share your dataset easier and allows group collaboration (allows for secondary analysis)

However you still need to code the data, uncover the themes and do the analysis yourself. Therefore it is still a manual approach.

The user interface of CAQDAS software 'NVivo'

Organizing your qualitative data in a feedback repository

Another solution to organizing your qualitative data is to upload it into a feedback repository where it can be unified with your other data , and easily searchable and taggable. There are a number of software solutions that act as a central repository for your qualitative research data. Here are a couple solutions that you could investigate:  

  • Dovetail: Dovetail is a research repository with a focus on video and audio transcriptions. You can tag your transcriptions within the platform for theme analysis. You can also upload your other qualitative data such as research reports, survey responses, support conversations, and customer interviews. Dovetail acts as a single, searchable repository. And makes it easier to collaborate with other people around your qualitative research.
  • EnjoyHQ: EnjoyHQ is another research repository with similar functionality to Dovetail. It boasts a more sophisticated search engine, but it has a higher starting subscription cost.

Organizing your qualitative data in a feedback analytics platform

If you have a lot of qualitative customer or employee feedback, from the likes of customer surveys or employee surveys, you will benefit from a feedback analytics platform. A feedback analytics platform is a software that automates the process of both sentiment analysis and thematic analysis . Companies use the integrations offered by these platforms to directly tap into their qualitative data sources (review sites, social media, survey responses, etc.). The data collected is then organized and analyzed consistently within the platform.

If you have data prepared in a spreadsheet, it can also be imported into feedback analytics platforms.

Once all this rich data has been organized within the feedback analytics platform, it is ready to be coded and themed, within the same platform. Thematic is a feedback analytics platform that offers one of the largest libraries of integrations with qualitative data sources.

Some of qualitative data integrations offered by Thematic

Step 3: Coding your qualitative data

Your feedback data is now organized in one place. Either within your spreadsheet, CAQDAS, feedback repository or within your feedback analytics platform. The next step is to code your feedback data so we can extract meaningful insights in the next step.

Coding is the process of labelling and organizing your data in such a way that you can then identify themes in the data, and the relationships between these themes.

To simplify the coding process, you will take small samples of your customer feedback data, come up with a set of codes, or categories capturing themes, and label each piece of feedback, systematically, for patterns and meaning. Then you will take a larger sample of data, revising and refining the codes for greater accuracy and consistency as you go.

If you choose to use a feedback analytics platform, much of this process will be automated and accomplished for you.

The terms to describe different categories of meaning (‘theme’, ‘code’, ‘tag’, ‘category’ etc) can be confusing as they are often used interchangeably.  For clarity, this article will use the term ‘code’.

To code means to identify key words or phrases and assign them to a category of meaning. “I really hate the customer service of this computer software company” would be coded as “poor customer service”.

How to manually code your qualitative data

  • Decide whether you will use deductive or inductive coding. Deductive coding is when you create a list of predefined codes, and then assign them to the qualitative data. Inductive coding is the opposite of this, you create codes based on the data itself. Codes arise directly from the data and you label them as you go. You need to weigh up the pros and cons of each coding method and select the most appropriate.
  • Read through the feedback data to get a broad sense of what it reveals. Now it’s time to start assigning your first set of codes to statements and sections of text.
  • Keep repeating step 2, adding new codes and revising the code description as often as necessary.  Once it has all been coded, go through everything again, to be sure there are no inconsistencies and that nothing has been overlooked.
  • Create a code frame to group your codes. The coding frame is the organizational structure of all your codes. And there are two commonly used types of coding frames, flat, or hierarchical. A hierarchical code frame will make it easier for you to derive insights from your analysis.
  • Based on the number of times a particular code occurs, you can now see the common themes in your feedback data. This is insightful! If ‘bad customer service’ is a common code, it’s time to take action.

We have a detailed guide dedicated to manually coding your qualitative data .

Example of a hierarchical coding frame in qualitative data analysis

Using software to speed up manual coding of qualitative data

An Excel spreadsheet is still a popular method for coding. But various software solutions can help speed up this process. Here are some examples.

  • CAQDAS / NVivo - CAQDAS software has built-in functionality that allows you to code text within their software. You may find the interface the software offers easier for managing codes than a spreadsheet.
  • Dovetail/EnjoyHQ - You can tag transcripts and other textual data within these solutions. As they are also repositories you may find it simpler to keep the coding in one platform.
  • IBM SPSS - SPSS is a statistical analysis software that may make coding easier than in a spreadsheet.
  • Ascribe - Ascribe’s ‘Coder’ is a coding management system. Its user interface will make it easier for you to manage your codes.

Automating the qualitative coding process using thematic analysis software

In solutions which speed up the manual coding process, you still have to come up with valid codes and often apply codes manually to pieces of feedback. But there are also solutions that automate both the discovery and the application of codes.

Advances in machine learning have now made it possible to read, code and structure qualitative data automatically. This type of automated coding is offered by thematic analysis software .

Automation makes it far simpler and faster to code the feedback and group it into themes. By incorporating natural language processing (NLP) into the software, the AI looks across sentences and phrases to identify common themes meaningful statements. Some automated solutions detect repeating patterns and assign codes to them, others make you train the AI by providing examples. You could say that the AI learns the meaning of the feedback on its own.

Thematic automates the coding of qualitative feedback regardless of source. There’s no need to set up themes or categories in advance. Simply upload your data and wait a few minutes. You can also manually edit the codes to further refine their accuracy.  Experiments conducted indicate that Thematic’s automated coding is just as accurate as manual coding .

Paired with sentiment analysis and advanced text analytics - these automated solutions become powerful for deriving quality business or research insights.

You could also build your own , if you have the resources!

The key benefits of using an automated coding solution

Automated analysis can often be set up fast and there’s the potential to uncover things that would never have been revealed if you had given the software a prescribed list of themes to look for.

Because the model applies a consistent rule to the data, it captures phrases or statements that a human eye might have missed.

Complete and consistent analysis of customer feedback enables more meaningful findings. Leading us into step 4.

Step 4: Analyze your data: Find meaningful insights

Now we are going to analyze our data to find insights. This is where we start to answer our research questions. Keep in mind that step 4 and step 5 (tell the story) have some overlap . This is because creating visualizations is both part of analysis process and reporting.

The task of uncovering insights is to scour through the codes that emerge from the data and draw meaningful correlations from them. It is also about making sure each insight is distinct and has enough data to support it.

Part of the analysis is to establish how much each code relates to different demographics and customer profiles, and identify whether there’s any relationship between these data points.

Manually create sub-codes to improve the quality of insights

If your code frame only has one level, you may find that your codes are too broad to be able to extract meaningful insights. This is where it is valuable to create sub-codes to your primary codes. This process is sometimes referred to as meta coding.

Note: If you take an inductive coding approach, you can create sub-codes as you are reading through your feedback data and coding it.

While time-consuming, this exercise will improve the quality of your analysis. Here is an example of what sub-codes could look like.

Example of sub-codes

You need to carefully read your qualitative data to create quality sub-codes. But as you can see, the depth of analysis is greatly improved. By calculating the frequency of these sub-codes you can get insight into which  customer service problems you can immediately address.

Correlate the frequency of codes to customer segments

Many businesses use customer segmentation . And you may have your own respondent segments that you can apply to your qualitative analysis. Segmentation is the practise of dividing customers or research respondents into subgroups.

Segments can be based on:

  • Demographic
  • And any other data type that you care to segment by

It is particularly useful to see the occurrence of codes within your segments. If one of your customer segments is considered unimportant to your business, but they are the cause of nearly all customer service complaints, it may be in your best interest to focus attention elsewhere. This is a useful insight!

Manually visualizing coded qualitative data

There are formulas you can use to visualize key insights in your data. The formulas we will suggest are imperative if you are measuring a score alongside your feedback.

If you are collecting a metric alongside your qualitative data this is a key visualization. Impact answers the question: “What’s the impact of a code on my overall score?”. Using Net Promoter Score (NPS) as an example, first you need to:

  • Calculate overall NPS
  • Calculate NPS in the subset of responses that do not contain that theme
  • Subtract B from A

Then you can use this simple formula to calculate code impact on NPS .

Visualizing qualitative data: Calculating the impact of a code on your score

You can then visualize this data using a bar chart.

You can download our CX toolkit - it includes a template to recreate this.

Trends over time

This analysis can help you answer questions like: “Which codes are linked to decreases or increases in my score over time?”

We need to compare two sequences of numbers: NPS over time and code frequency over time . Using Excel, calculate the correlation between the two sequences, which can be either positive (the more codes the higher the NPS, see picture below), or negative (the more codes the lower the NPS).

Now you need to plot code frequency against the absolute value of code correlation with NPS. Here is the formula:

Analyzing qualitative data: Calculate which codes are linked to increases or decreases in my score

The visualization could look like this:

Visualizing qualitative data trends over time

These are two examples, but there are more. For a third manual formula, and to learn why word clouds are not an insightful form of analysis, read our visualizations article .

Using a text analytics solution to automate analysis

Automated text analytics solutions enable codes and sub-codes to be pulled out of the data automatically. This makes it far faster and easier to identify what’s driving negative or positive results. And to pick up emerging trends and find all manner of rich insights in the data.

Another benefit of AI-driven text analytics software is its built-in capability for sentiment analysis, which provides the emotive context behind your feedback and other qualitative textual data therein.

Thematic provides text analytics that goes further by allowing users to apply their expertise on business context to edit or augment the AI-generated outputs.

Since the move away from manual research is generally about reducing the human element, adding human input to the technology might sound counter-intuitive. However, this is mostly to make sure important business nuances in the feedback aren’t missed during coding. The result is a higher accuracy of analysis. This is sometimes referred to as augmented intelligence .

Codes displayed by volume within Thematic. You can 'manage themes' to introduce human input.

Step 5: Report on your data: Tell the story

The last step of analyzing your qualitative data is to report on it, to tell the story. At this point, the codes are fully developed and the focus is on communicating the narrative to the audience.

A coherent outline of the qualitative research, the findings and the insights is vital for stakeholders to discuss and debate before they can devise a meaningful course of action.

Creating graphs and reporting in Powerpoint

Typically, qualitative researchers take the tried and tested approach of distilling their report into a series of charts, tables and other visuals which are woven into a narrative for presentation in Powerpoint.

Using visualization software for reporting

With data transformation and APIs, the analyzed data can be shared with data visualisation software, such as Power BI or Tableau , Google Studio or Looker. Power BI and Tableau are among the most preferred options.

Visualizing your insights inside a feedback analytics platform

Feedback analytics platforms, like Thematic, incorporate visualisation tools that intuitively turn key data and insights into graphs.  This removes the time consuming work of constructing charts to visually identify patterns and creates more time to focus on building a compelling narrative that highlights the insights, in bite-size chunks, for executive teams to review.

Using a feedback analytics platform with visualization tools means you don’t have to use a separate product for visualizations. You can export graphs into Powerpoints straight from the platforms.

Two examples of qualitative data visualizations within Thematic

Conclusion - Manual or Automated?

There are those who remain deeply invested in the manual approach - because it’s familiar, because they’re reluctant to spend money and time learning new software, or because they’ve been burned by the overpromises of AI.  

For projects that involve small datasets, manual analysis makes sense. For example, if the objective is simply to quantify a simple question like “Do customers prefer X concepts to Y?”. If the findings are being extracted from a small set of focus groups and interviews, sometimes it’s easier to just read them

However, as new generations come into the workplace, it’s technology-driven solutions that feel more comfortable and practical. And the merits are undeniable.  Especially if the objective is to go deeper and understand the ‘why’ behind customers’ preference for X or Y. And even more especially if time and money are considerations.

The ability to collect a free flow of qualitative feedback data at the same time as the metric means AI can cost-effectively scan, crunch, score and analyze a ton of feedback from one system in one go. And time-intensive processes like focus groups, or coding, that used to take weeks, can now be completed in a matter of hours or days.

But aside from the ever-present business case to speed things up and keep costs down, there are also powerful research imperatives for automated analysis of qualitative data: namely, accuracy and consistency.

Finding insights hidden in feedback requires consistency, especially in coding.  Not to mention catching all the ‘unknown unknowns’ that can skew research findings and steering clear of cognitive bias.

Some say without manual data analysis researchers won’t get an accurate “feel” for the insights. However, the larger data sets are, the harder it is to sort through the feedback and organize feedback that has been pulled from different places.  And, the more difficult it is to stay on course, the greater the risk of drawing incorrect, or incomplete, conclusions grows.

Though the process steps for qualitative data analysis have remained pretty much unchanged since psychologist Paul Felix Lazarsfeld paved the path a hundred years ago, the impact digital technology has had on types of qualitative feedback data and the approach to the analysis are profound.  

If you want to try an automated feedback analysis solution on your own qualitative data, you can get started with Thematic .

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Qualitative Data Analysis: What is it, Methods + Examples

Explore qualitative data analysis with diverse methods and real-world examples. Uncover the nuances of human experiences with this guide.

In a world rich with information and narrative, understanding the deeper layers of human experiences requires a unique vision that goes beyond numbers and figures. This is where the power of qualitative data analysis comes to light.

In this blog, we’ll learn about qualitative data analysis, explore its methods, and provide real-life examples showcasing its power in uncovering insights.

Qualitative data analysis is a systematic process of examining non-numerical data to extract meaning, patterns, and insights.

In contrast to quantitative analysis, which focuses on numbers and statistical metrics, the qualitative study focuses on the qualitative aspects of data, such as text, images, audio, and videos. It seeks to understand every aspect of human experiences, perceptions, and behaviors by examining the data’s richness.

Companies frequently conduct this analysis on customer feedback. You can collect qualitative data from reviews, complaints, chat messages, interactions with support centers, customer interviews, case notes, or even social media comments. This kind of data holds the key to understanding customer sentiments and preferences in a way that goes beyond mere numbers.

Importance of Qualitative Data Analysis

Qualitative data analysis plays a crucial role in your research and decision-making process across various disciplines. Let’s explore some key reasons that underline the significance of this analysis:

In-Depth Understanding

It enables you to explore complex and nuanced aspects of a phenomenon, delving into the ‘how’ and ‘why’ questions. This method provides you with a deeper understanding of human behavior, experiences, and contexts that quantitative approaches might not capture fully.

Contextual Insight

You can use this analysis to give context to numerical data. It will help you understand the circumstances and conditions that influence participants’ thoughts, feelings, and actions. This contextual insight becomes essential for generating comprehensive explanations.

Theory Development

You can generate or refine hypotheses via qualitative data analysis. As you analyze the data attentively, you can form hypotheses, concepts, and frameworks that will drive your future research and contribute to theoretical advances.

Participant Perspectives

When performing qualitative research, you can highlight participant voices and opinions. This approach is especially useful for understanding marginalized or underrepresented people, as it allows them to communicate their experiences and points of view.

Exploratory Research

The analysis is frequently used at the exploratory stage of your project. It assists you in identifying important variables, developing research questions, and designing quantitative studies that will follow.

Types of Qualitative Data

When conducting qualitative research, you can use several qualitative data collection methods, and here you will come across many sorts of qualitative data that can provide you with unique insights into your study topic. These data kinds add new views and angles to your understanding and analysis.

Interviews and Focus Groups

Interviews and focus groups will be among your key methods for gathering qualitative data. Interviews are one-on-one talks in which participants can freely share their thoughts, experiences, and opinions.

Focus groups, on the other hand, are discussions in which members interact with one another, resulting in dynamic exchanges of ideas. Both methods provide rich qualitative data and direct access to participant perspectives.

Observations and Field Notes

Observations and field notes are another useful sort of qualitative data. You can immerse yourself in the research environment through direct observation, carefully documenting behaviors, interactions, and contextual factors.

These observations will be recorded in your field notes, providing a complete picture of the environment and the behaviors you’re researching. This data type is especially important for comprehending behavior in their natural setting.

Textual and Visual Data

Textual and visual data include a wide range of resources that can be qualitatively analyzed. Documents, written narratives, and transcripts from various sources, such as interviews or speeches, are examples of textual data.

Photographs, films, and even artwork provide a visual layer to your research. These forms of data allow you to investigate what is spoken and the underlying emotions, details, and symbols expressed by language or pictures.

When to Choose Qualitative Data Analysis over Quantitative Data Analysis

As you begin your research journey, understanding why the analysis of qualitative data is important will guide your approach to understanding complex events. If you analyze qualitative data, it will provide new insights that complement quantitative methodologies, which will give you a broader understanding of your study topic.

It is critical to know when to use qualitative analysis over quantitative procedures. You can prefer qualitative data analysis when:

  • Complexity Reigns: When your research questions involve deep human experiences, motivations, or emotions, qualitative research excels at revealing these complexities.
  • Exploration is Key: Qualitative analysis is ideal for exploratory research. It will assist you in understanding a new or poorly understood topic before formulating quantitative hypotheses.
  • Context Matters: If you want to understand how context affects behaviors or results, qualitative data analysis provides the depth needed to grasp these relationships.
  • Unanticipated Findings: When your study provides surprising new viewpoints or ideas, qualitative analysis helps you to delve deeply into these emerging themes.
  • Subjective Interpretation is Vital: When it comes to understanding people’s subjective experiences and interpretations, qualitative data analysis is the way to go.

You can make informed decisions regarding the right approach for your research objectives if you understand the importance of qualitative analysis and recognize the situations where it shines.

Qualitative Data Analysis Methods and Examples

Exploring various qualitative data analysis methods will provide you with a wide collection for making sense of your research findings. Once the data has been collected, you can choose from several analysis methods based on your research objectives and the data type you’ve collected.

There are five main methods for analyzing qualitative data. Each method takes a distinct approach to identifying patterns, themes, and insights within your qualitative data. They are:

Method 1: Content Analysis

Content analysis is a methodical technique for analyzing textual or visual data in a structured manner. In this method, you will categorize qualitative data by splitting it into manageable pieces and assigning the manual coding process to these units.

As you go, you’ll notice ongoing codes and designs that will allow you to conclude the content. This method is very beneficial for detecting common ideas, concepts, or themes in your data without losing the context.

Steps to Do Content Analysis

Follow these steps when conducting content analysis:

  • Collect and Immerse: Begin by collecting the necessary textual or visual data. Immerse yourself in this data to fully understand its content, context, and complexities.
  • Assign Codes and Categories: Assign codes to relevant data sections that systematically represent major ideas or themes. Arrange comparable codes into groups that cover the major themes.
  • Analyze and Interpret: Develop a structured framework from the categories and codes. Then, evaluate the data in the context of your research question, investigate relationships between categories, discover patterns, and draw meaning from these connections.

Benefits & Challenges

There are various advantages to using content analysis:

  • Structured Approach: It offers a systematic approach to dealing with large data sets and ensures consistency throughout the research.
  • Objective Insights: This method promotes objectivity, which helps to reduce potential biases in your study.
  • Pattern Discovery: Content analysis can help uncover hidden trends, themes, and patterns that are not always obvious.
  • Versatility: You can apply content analysis to various data formats, including text, internet content, images, etc.

However, keep in mind the challenges that arise:

  • Subjectivity: Even with the best attempts, a certain bias may remain in coding and interpretation.
  • Complexity: Analyzing huge data sets requires time and great attention to detail.
  • Contextual Nuances: Content analysis may not capture all of the contextual richness that qualitative data analysis highlights.

Example of Content Analysis

Suppose you’re conducting market research and looking at customer feedback on a product. As you collect relevant data and analyze feedback, you’ll see repeating codes like “price,” “quality,” “customer service,” and “features.” These codes are organized into categories such as “positive reviews,” “negative reviews,” and “suggestions for improvement.”

According to your findings, themes such as “price” and “customer service” stand out and show that pricing and customer service greatly impact customer satisfaction. This example highlights the power of content analysis for obtaining significant insights from large textual data collections.

Method 2: Thematic Analysis

Thematic analysis is a well-structured procedure for identifying and analyzing recurring themes in your data. As you become more engaged in the data, you’ll generate codes or short labels representing key concepts. These codes are then organized into themes, providing a consistent framework for organizing and comprehending the substance of the data.

The analysis allows you to organize complex narratives and perspectives into meaningful categories, which will allow you to identify connections and patterns that may not be visible at first.

Steps to Do Thematic Analysis

Follow these steps when conducting a thematic analysis:

  • Code and Group: Start by thoroughly examining the data and giving initial codes that identify the segments. To create initial themes, combine relevant codes.
  • Code and Group: Begin by engaging yourself in the data, assigning first codes to notable segments. To construct basic themes, group comparable codes together.
  • Analyze and Report: Analyze the data within each theme to derive relevant insights. Organize the topics into a consistent structure and explain your findings, along with data extracts that represent each theme.

Thematic analysis has various benefits:

  • Structured Exploration: It is a method for identifying patterns and themes in complex qualitative data.
  • Comprehensive knowledge: Thematic analysis promotes an in-depth understanding of the complications and meanings of the data.
  • Application Flexibility: This method can be customized to various research situations and data kinds.

However, challenges may arise, such as:

  • Interpretive Nature: Interpreting qualitative data in thematic analysis is vital, and it is critical to manage researcher bias.
  • Time-consuming: The study can be time-consuming, especially with large data sets.
  • Subjectivity: The selection of codes and topics might be subjective.

Example of Thematic Analysis

Assume you’re conducting a thematic analysis on job satisfaction interviews. Following your immersion in the data, you assign initial codes such as “work-life balance,” “career growth,” and “colleague relationships.” As you organize these codes, you’ll notice themes develop, such as “Factors Influencing Job Satisfaction” and “Impact on Work Engagement.”

Further investigation reveals the tales and experiences included within these themes and provides insights into how various elements influence job satisfaction. This example demonstrates how thematic analysis can reveal meaningful patterns and insights in qualitative data.

Method 3: Narrative Analysis

The narrative analysis involves the narratives that people share. You’ll investigate the histories in your data, looking at how stories are created and the meanings they express. This method is excellent for learning how people make sense of their experiences through narrative.

Steps to Do Narrative Analysis

The following steps are involved in narrative analysis:

  • Gather and Analyze: Start by collecting narratives, such as first-person tales, interviews, or written accounts. Analyze the stories, focusing on the plot, feelings, and characters.
  • Find Themes: Look for recurring themes or patterns in various narratives. Think about the similarities and differences between these topics and personal experiences.
  • Interpret and Extract Insights: Contextualize the narratives within their larger context. Accept the subjective nature of each narrative and analyze the narrator’s voice and style. Extract insights from the tales by diving into the emotions, motivations, and implications communicated by the stories.

There are various advantages to narrative analysis:

  • Deep Exploration: It lets you look deeply into people’s personal experiences and perspectives.
  • Human-Centered: This method prioritizes the human perspective, allowing individuals to express themselves.

However, difficulties may arise, such as:

  • Interpretive Complexity: Analyzing narratives requires dealing with the complexities of meaning and interpretation.
  • Time-consuming: Because of the richness and complexities of tales, working with them can be time-consuming.

Example of Narrative Analysis

Assume you’re conducting narrative analysis on refugee interviews. As you read the stories, you’ll notice common themes of toughness, loss, and hope. The narratives provide insight into the obstacles that refugees face, their strengths, and the dreams that guide them.

The analysis can provide a deeper insight into the refugees’ experiences and the broader social context they navigate by examining the narratives’ emotional subtleties and underlying meanings. This example highlights how narrative analysis can reveal important insights into human stories.

Method 4: Grounded Theory Analysis

Grounded theory analysis is an iterative and systematic approach that allows you to create theories directly from data without being limited by pre-existing hypotheses. With an open mind, you collect data and generate early codes and labels that capture essential ideas or concepts within the data.

As you progress, you refine these codes and increasingly connect them, eventually developing a theory based on the data. Grounded theory analysis is a dynamic process for developing new insights and hypotheses based on details in your data.

Steps to Do Grounded Theory Analysis

Grounded theory analysis requires the following steps:

  • Initial Coding: First, immerse yourself in the data, producing initial codes that represent major concepts or patterns.
  • Categorize and Connect: Using axial coding, organize the initial codes, which establish relationships and connections between topics.
  • Build the Theory: Focus on creating a core category that connects the codes and themes. Regularly refine the theory by comparing and integrating new data, ensuring that it evolves organically from the data.

Grounded theory analysis has various benefits:

  • Theory Generation: It provides a one-of-a-kind opportunity to generate hypotheses straight from data and promotes new insights.
  • In-depth Understanding: The analysis allows you to deeply analyze the data and reveal complex relationships and patterns.
  • Flexible Process: This method is customizable and ongoing, which allows you to enhance your research as you collect additional data.

However, challenges might arise with:

  • Time and Resources: Because grounded theory analysis is a continuous process, it requires a large commitment of time and resources.
  • Theoretical Development: Creating a grounded theory involves a thorough understanding of qualitative data analysis software and theoretical concepts.
  • Interpretation of Complexity: Interpreting and incorporating a newly developed theory into existing literature can be intellectually hard.

Example of Grounded Theory Analysis

Assume you’re performing a grounded theory analysis on workplace collaboration interviews. As you open code the data, you will discover notions such as “communication barriers,” “team dynamics,” and “leadership roles.” Axial coding demonstrates links between these notions, emphasizing the significance of efficient communication in developing collaboration.

You create the core “Integrated Communication Strategies” category through selective coding, which unifies new topics.

This theory-driven category serves as the framework for understanding how numerous aspects contribute to effective team collaboration. This example shows how grounded theory analysis allows you to generate a theory directly from the inherent nature of the data.

Method 5: Discourse Analysis

Discourse analysis focuses on language and communication. You’ll look at how language produces meaning and how it reflects power relations, identities, and cultural influences. This strategy examines what is said and how it is said; the words, phrasing, and larger context of communication.

The analysis is precious when investigating power dynamics, identities, and cultural influences encoded in language. By evaluating the language used in your data, you can identify underlying assumptions, cultural standards, and how individuals negotiate meaning through communication.

Steps to Do Discourse Analysis

Conducting discourse analysis entails the following steps:

  • Select Discourse: For analysis, choose language-based data such as texts, speeches, or media content.
  • Analyze Language: Immerse yourself in the conversation, examining language choices, metaphors, and underlying assumptions.
  • Discover Patterns: Recognize the dialogue’s reoccurring themes, ideologies, and power dynamics. To fully understand the effects of these patterns, put them in their larger context.

There are various advantages of using discourse analysis:

  • Understanding Language: It provides an extensive understanding of how language builds meaning and influences perceptions.
  • Uncovering Power Dynamics: The analysis reveals how power dynamics appear via language.
  • Cultural Insights: This method identifies cultural norms, beliefs, and ideologies stored in communication.

However, the following challenges may arise:

  • Complexity of Interpretation: Language analysis involves navigating multiple levels of nuance and interpretation.
  • Subjectivity: Interpretation can be subjective, so controlling researcher bias is important.
  • Time-Intensive: Discourse analysis can take a lot of time because careful linguistic study is required in this analysis.

Example of Discourse Analysis

Consider doing discourse analysis on media coverage of a political event. You notice repeating linguistic patterns in news articles that depict the event as a conflict between opposing parties. Through deconstruction, you can expose how this framing supports particular ideologies and power relations.

You can illustrate how language choices influence public perceptions and contribute to building the narrative around the event by analyzing the speech within the broader political and social context. This example shows how discourse analysis can reveal hidden power dynamics and cultural influences on communication.

How to do Qualitative Data Analysis with the QuestionPro Research suite?

QuestionPro is a popular survey and research platform that offers tools for collecting and analyzing qualitative and quantitative data. Follow these general steps for conducting qualitative data analysis using the QuestionPro Research Suite:

  • Collect Qualitative Data: Set up your survey to capture qualitative responses. It might involve open-ended questions, text boxes, or comment sections where participants can provide detailed responses.
  • Export Qualitative Responses: Export the responses once you’ve collected qualitative data through your survey. QuestionPro typically allows you to export survey data in various formats, such as Excel or CSV.
  • Prepare Data for Analysis: Review the exported data and clean it if necessary. Remove irrelevant or duplicate entries to ensure your data is ready for analysis.
  • Code and Categorize Responses: Segment and label data, letting new patterns emerge naturally, then develop categories through axial coding to structure the analysis.
  • Identify Themes: Analyze the coded responses to identify recurring themes, patterns, and insights. Look for similarities and differences in participants’ responses.
  • Generate Reports and Visualizations: Utilize the reporting features of QuestionPro to create visualizations, charts, and graphs that help communicate the themes and findings from your qualitative research.
  • Interpret and Draw Conclusions: Interpret the themes and patterns you’ve identified in the qualitative data. Consider how these findings answer your research questions or provide insights into your study topic.
  • Integrate with Quantitative Data (if applicable): If you’re also conducting quantitative research using QuestionPro, consider integrating your qualitative findings with quantitative results to provide a more comprehensive understanding.

Qualitative data analysis is vital in uncovering various human experiences, views, and stories. If you’re ready to transform your research journey and apply the power of qualitative analysis, now is the moment to do it. Book a demo with QuestionPro today and begin your journey of exploration.



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Data Analysis for Qualitative Research: 6 Step Guide

Data analysis for qualitative research is not intuitive. This is because qualitative data stands in opposition to traditional data analysis methodologies: while data analysis is concerned with quantities, qualitative data is by definition unquantified . But there is an easy, methodical approach that anyone can take use to get reliable results when performing data analysis for qualitative research. The process consists of 6 steps that I’ll break down in this article:

  • Perform interviews(if necessary )
  • Gather all documents and transcribe any non-paper records
  • Decide whether to either code analytical data, analyze word frequencies, or both
  • Decide what interpretive angle you want to take: content analysis , narrative analysis, discourse analysis, framework analysis, and/or grounded theory
  • Compile your data in a spreadsheet using document saving techniques (windows and mac)
  • Identify trends in words, themes, metaphors, natural patterns, and more

To complete these steps, you will need:

  • Microsoft word
  • Microsoft excel
  • Internet access

You can get the free Intro to Data Analysis eBook to cover the fundamentals and ensure strong progression in all your data endeavors.

What is qualitative research?

Qualitative research is not the same as quantitative research. In short, qualitative research is the interpretation of non-numeric data. It usually aims at drawing conclusions that explain why a phenomenon occurs, rather than that one does occur. Here’s a great quote from a nursing magazine about quantitative vs qualitative research:

“A traditional quantitative study… uses a predetermined (and auditable) set of steps to confirm or refute [a] hypothesis. “In contrast, qualitative research often takes the position that an interpretive understanding is only possible by way of uncovering or deconstructing the meanings of a phenomenon. Thus, a distinction between explaining how something operates (explanation) and why it operates in the manner that it does (interpretation) may be [an] effective way to distinguish quantitative from qualitative analytic processes involved in any particular study.” (bold added) (( EBN ))

Learn to Interpret Your Qualitative Data

This article explain what data analysis is and how to do it. To learn how to interpret the results, visualize, and write an insightful report, sign up for our handbook below.

data analysis in qualitative research process

Step 1a: Data collection methods and techniques in qualitative research: interviews and focus groups

Step 1 is collecting the data that you will need for the analysis. If you are not performing any interviews or focus groups to gather data, then you can skip this step. It’s for people who need to go into the field and collect raw information as part of their qualitative analysis.

Since the whole point of an interview and of qualitative analysis in general is to understand a research question better, you should start by making sure you have a specific, refined research question . Whether you’re a researcher by trade or a data analyst working on one-time project, you must know specifically what you want to understand in order to get results.

Good research questions are specific enough to guide action but open enough to leave room for insight and growth. Examples of good research questions include:

  • Good : To what degree does living in a city impact the quality of a person’s life? (open-ended, complex)
  • Bad : Does living in a city impact the quality of a person’s life? (closed, simple)

Once you understand the research question, you need to develop a list of interview questions. These questions should likewise be open-ended and provide liberty of expression to the responder. They should support the research question in an active way without prejudicing the response. Examples of good interview questions include:

  • Good : Tell me what it’s like to live in a city versus in the country. (open, not leading)
  • Bad : Don’t you prefer the city to the country because there are more people? (closed, leading)

Some additional helpful tips include:

  • Begin each interview with a neutral question to get the person relaxed
  • Limit each question to a single idea
  • If you don’t understand, ask for clarity
  • Do not pass any judgements
  • Do not spend more than 15m on an interview, lest the quality of responses drop
  • Focus groups

The alternative to interviews is focus groups. Focus groups are a great way for you to get an idea for how people communicate their opinions in a group setting, rather than a one-on-one setting as in interviews.

In short, focus groups are gatherings of small groups of people from representative backgrounds who receive instruction, or “facilitation,” from a focus group leader. Typically, the leader will ask questions to stimulate conversation, reformulate questions to bring the discussion back to focus, and prevent the discussion from turning sour or giving way to bad faith.

Focus group questions should be open-ended like their interview neighbors, and they should stimulate some degree of disagreement. Disagreement often leads to valuable information about differing opinions, as people tend to say what they mean if contradicted.

However, focus group leaders must be careful not to let disagreements escalate, as anger can make people lie to be hurtful or simply to win an argument. And lies are not helpful in data analysis for qualitative research.

Step 1b: Tools for qualitative data collection

When it comes to data analysis for qualitative analysis, the tools you use to collect data should align to some degree with the tools you will use to analyze the data.

As mentioned in the intro, you will be focusing on analysis techniques that only require the traditional Microsoft suite programs: Microsoft Excel and Microsoft Word . At the same time, you can source supplementary tools from various websites, like Text Analyzer and WordCounter.

In short, the tools for qualitative data collection that you need are Excel and Word , as well as web-based free tools like Text Analyzer and WordCounter . These online tools are helpful in the quantitative part of your qualitative research.

Step 2: Gather all documents & transcribe non-written docs

Once you have your interviews and/or focus group transcripts, it’s time to decide if you need other documentation. If you do, you’ll need to gather it all into one place first, then develop a strategy for how to transcribe any non-written documents.

When do you need documentation other than interviews and focus groups? Two situations usually call for documentation. First , if you have little funding , then you can’t afford to run expensive interviews and focus groups.

Second , social science researchers typically focus on documents since their research questions are less concerned with subject-oriented data, while hard science and business researchers typically focus on interviews and focus groups because they want to know what people think, and they want to know today.

Non-written records

Other factors at play include the type of research, the field, and specific research goal. For those who need documentation and to describe non-written records, there are some steps to follow:

  • Put all hard copy source documents into a sealed binder (I use plastic paper holders with elastic seals ).
  • If you are sourcing directly from printed books or journals, then you will need to digitalize them by scanning them and making them text readable by the computer. To do so, turn all PDFs into Word documents using online tools such as PDF to Word Converter . This process is never full-proof, and it may be a source of error in the data collection, but it’s part of the process.
  • If you are sourcing online documents, try as often as possible to get computer-readable PDF documents that you can easily copy/paste or convert. Locked PDFs are essentially a lost cause .
  • Transcribe any audio files into written documents. There are free online tools available to help with this, such as 360converter . If you run a test through the system, you’ll see that the output is not 100%. The best way to use this tool is as a first draft generator. You can then correct and complete it with old fashioned, direct transcription.

Step 3: Decide on the type of qualitative research

Before step 3 you should have collected your data, transcribed it all into written-word documents, and compiled it in one place. Now comes the interesting part. You need to decide what you want to get out of your research by choosing an analytic angle, or type of qualitative research.

The available types of qualitative research are as follows. Each of them takes a unique angle that you must choose to get what information you want from the analysis . In addition, each of them has a different impact on the data analysis for qualitative research (coding vs word frequency) that we use.

Narrative analysis, discourse analysis.

  • Framework analysis, and/or

Grounded theory

From a high level, content, narrative, and discourse analysis are actionable independent tactics, whereas framework analysis and grounded theory are ways of honing and applying the first three.

  • Definition : Content analysis is identify and labelling themes of any kind within a text.
  • Focus : Identifying any kind of pattern in written text, transcribed audio, or transcribed video. This could be thematic, word repetition, idea repetition. Most often, the patterns we find are idea that make up an argument.
  • Goal : To simplify, standardize, and quickly reference ideas from any given text. Content analysis is a way to pull the main ideas from huge documents for comparison. In this way, it’s more a means to an end.
  • Pros : The huge advantage of doing content analysis is that you can quickly process huge amounts of texts using simple coding and word frequency techniques we will look at below. To use a metaphore, it is to qualitative analysis documents what Spark notes are to books.
  • Cons : The downside to content analysis is that it’s quite general. If you have a very specific, narrative research question, then tracing “any and all ideas” will not be very helpful to you.
  • Definition : Narrative analysis is the reformulation and simplification of interview answers or documentation into small narrative components to identify story-like patterns.
  • Focus : Understanding the text based on its narrative components as opposed to themes or other qualities.
  • Goal : To reference the text from an angle closer to the nature of texts in order to obtain further insights.
  • Pros : Narrative analysis is very useful for getting perspective on a topic in which you’re extremely limited. It can be easy to get tunnel vision when you’re digging for themes and ideas from a reason-centric perspective. Turning to a narrative approach will help you stay grounded. More importantly, it helps reveal different kinds of trends.
  • Cons : Narrative analysis adds another layer of subjectivity to the instinctive nature of qualitative research. Many see it as too dependent on the researcher to hold any critical value.
  • Definition : Discourse analysis is the textual analysis of naturally occurring speech. Any oral expression must be transcribed before undergoing legitimate discourse analysis.
  • Focus : Understanding ideas and themes through language communicated orally rather than pre-processed on paper.
  • Goal : To obtain insights from an angle outside the traditional content analysis on text.
  • Pros : Provides a considerable advantage in some areas of study in order to understand how people communicate an idea, versus the idea itself. For example, discourse analysis is important in political campaigning. People rarely vote for the candidate who most closely corresponds to his/her beliefs, but rather for the person they like the most.
  • Cons : As with narrative analysis, discourse analysis is more subjective in nature than content analysis, which focuses on ideas and patterns. Some do not consider it rigorous enough to be considered a legitimate subset of qualitative analysis, but these people are few.

Framework analysis

  • Definition : Framework analysis is a kind of qualitative analysis that includes 5 ordered steps: coding, indexing, charting, mapping, and interpreting . In most ways, framework analysis is a synonym for qualitative analysis — the same thing. The significant difference is the importance it places on the perspective used in the analysis.
  • Focus : Understanding patterns in themes and ideas.
  • Goal : Creating one specific framework for looking at a text.
  • Pros : Framework analysis is helpful when the researcher clearly understands what he/she wants from the project, as it’s a limitation approach. Since each of its step has defined parameters, framework analysis is very useful for teamwork.
  • Cons : It can lead to tunnel vision.
  • Definition : The use of content, narrative, and discourse analysis to examine a single case, in the hopes that discoveries from that case will lead to a foundational theory used to examine other like cases.
  • Focus : A vast approach using multiple techniques in order to establish patterns.
  • Goal : To develop a foundational theory.
  • Pros : When successful, grounded theories can revolutionize entire fields of study.
  • Cons : It’s very difficult to establish ground theories, and there’s an enormous amount of risk involved.

Step 4: Coding, word frequency, or both

Coding in data analysis for qualitative research is the process of writing 2-5 word codes that summarize at least 1 paragraphs of text (not writing computer code). This allows researchers to keep track of and analyze those codes. On the other hand, word frequency is the process of counting the presence and orientation of words within a text, which makes it the quantitative element in qualitative data analysis.

Video example of coding for data analysis in qualitative research

In short, coding in the context of data analysis for qualitative research follows 2 steps (video below):

  • Reading through the text one time
  • Adding 2-5 word summaries each time a significant theme or idea appears

Let’s look at a brief example of how to code for qualitative research in this video:

Click here for a link to the source text. 1

Example of word frequency processing

And word frequency is the process of finding a specific word or identifying the most common words through 3 steps:

  • Decide if you want to find 1 word or identify the most common ones
  • Use word’s “Replace” function to find a word or phrase
  • Use Text Analyzer to find the most common terms

Here’s another look at word frequency processing and how you to do it. Let’s look at the same example above, but from a quantitative perspective.

Imagine we are already familiar with melanoma and KITs , and we want to analyze the text based on these keywords. One thing we can do is look for these words using the Replace function in word

  • Locate the search bar
  • Click replace
  • Type in the word
  • See the total results

Here’s a brief video example:

Another option is to use an online Text Analyzer. This methodology won’t help us find a specific word, but it will help us discover the top performing phrases and words. All you need to do it put in a link to a target page or paste a text. I pasted the abstract from our source text, and what turns up is as expected. Here’s a picture:

text analyzer example

Step 5: Compile your data in a spreadsheet

After you have some coded data in the word document, you need to get it into excel for analysis. This process requires saving the word doc as an .htm extension, which makes it a website. Once you have the website, it’s as simple as opening that page, scrolling to the bottom, and copying/pasting the comments, or codes, into an excel document.

You will need to wrangle the data slightly in order to make it readable in excel. I’ve made a video to explain this process and places it below.

Step 6: Identify trends & analyze!

There are literally thousands of different ways to analyze qualitative data, and in most situations, the best technique depends on the information you want to get out of the research.

Nevertheless, there are a few go-to techniques. The most important of this is occurrences . In this short video, we finish the example from above by counting the number of times our codes appear. In this way, it’s very similar to word frequency (discussed above).

A few other options include:

  • Ranking each code on a set of relevant criteria and clustering
  • Pure cluster analysis
  • Causal analysis

We cover different types of analysis like this on the website, so be sure to check out other articles on the home page .

How to analyze qualitative data from an interview

To analyze qualitative data from an interview , follow the same 6 steps for quantitative data analysis:

  • Perform the interviews
  • Transcribe the interviews onto paper
  • Decide whether to either code analytical data (open, axial, selective), analyze word frequencies, or both
  • Compile your data in a spreadsheet using document saving techniques (for windows and mac)
  • Source text [ ↩ ]

About the Author

Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.

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A Step-by-Step Guide on How to Analyze Qualitative Data for Effective Research

Qualitative data analysis is a crucial step in any research project. It involves interpreting and making sense of non-numerical data, such as interviews, focus groups, observations, and open-ended survey responses. By analyzing qualitative data, researchers can gain deep insights into people’s experiences, attitudes, and behaviors. In this article, we will provide you with a step-by-step guide on how to analyze qualitative data effectively.

Preparing for Data Analysis

Before diving into data analysis, it’s essential to adequately prepare yourself and your data. This section will outline the necessary steps to take before starting the analysis process.

Organize your data: Begin by organizing your qualitative data in a systematic manner. Transcribe interviews or group similar responses together to ensure easy access during the analysis phase.

Familiarize yourself with the data: Take time to read through your qualitative data multiple times. This familiarization process helps you understand the nuances within the responses and identify any recurring themes or patterns.

Develop a coding system: Coding is a fundamental aspect of qualitative data analysis. Create a coding system that aligns with your research objectives and allows you to categorize different themes or concepts within your data.

Coding and Categorizing Data

Once you have prepared your data for analysis, it’s time to start coding and categorizing the information collected. This section will guide you through the process of assigning codes and identifying meaningful categories within your qualitative dataset.

Open coding: Begin by engaging in open coding, where you assign initial codes to specific segments of your qualitative data without preconceived categories in mind. This process helps uncover emerging themes that may not have been anticipated during the research design phase.

Axial coding: After completing open coding, move on to axial coding, which involves organizing the initial codes into broader categories or themes. Look for connections between different codes and identify relationships that exist within your data.

Selective coding: Once you have established meaningful categories, engage in selective coding to further refine your analysis. Focus on the most relevant and significant themes that emerged during axial coding, and explore their implications within your research context.

Analyzing Themes and Patterns

With a well-defined coding system in place, it’s time to delve deeper into the analysis of themes and patterns within your qualitative data. This section will guide you through various techniques to uncover valuable insights from your dataset.

Compare and contrast: Look for similarities and differences across different participants or groups within your qualitative data. By comparing responses, you can identify commonalities or divergent perspectives that contribute to a more comprehensive understanding of the research topic.

Identify outliers: Pay attention to any outliers or unique responses that fall outside the established themes or patterns. These outliers may provide valuable insights, challenge existing assumptions, or suggest new areas for exploration.

Triangulation: To enhance the credibility of your findings, consider using triangulation techniques by incorporating multiple sources of data (e.g., interviews, observations) or involving multiple researchers in the analysis process. This approach helps ensure consistency and reliability in your qualitative data analysis.

Drawing Conclusions and Reporting Findings

The final section of this guide focuses on drawing conclusions from your qualitative analysis and effectively reporting your findings to a wider audience.

Synthesize key findings: Summarize the main themes, patterns, and insights that emerged from your qualitative data analysis. Highlight any unexpected discoveries or notable trends that contribute to a richer understanding of the research topic.

Provide supporting evidence: Back up your conclusions with relevant quotes or examples from the qualitative data itself. Including direct excerpts adds credibility to your findings and allows readers to connect with participants’ voices directly.

Communicate effectively: When reporting your qualitative analysis, consider the needs and preferences of your target audience. Use clear and concise language, visual aids (e.g., charts, graphs), and compelling storytelling techniques to effectively communicate your research findings.

In conclusion, analyzing qualitative data is a complex yet rewarding process that can provide valuable insights for effective research. By following this step-by-step guide, you’ll be equipped with the necessary tools and techniques to conduct a thorough analysis of your qualitative data and draw meaningful conclusions from it. Remember to approach data analysis with an open mind, allowing for unexpected discoveries that may enrich your research findings.

This text was generated using a large language model, and select text has been reviewed and moderated for purposes such as readability.


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Qualitative Data and Analysis Tools: Coding and Analysis Software

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What's a CAQDAS?

CAQDAS (pronounced like cactus) is a common acronym for Coding and Qualitative Data Analysis Software. It describes a range of tools that can be used to apply codes or tags to unstructured data (like text, audio, video, or images) and summarize or analyze the data using some combinations of the data itself and the applied codes.

Some CAQDAS is primarily or exclusively focused on manual coding (highlight a relevant section and select a tag) while others also include options to automatically apply tags based on words, phrases, or machine learning analysis. CAQDAS also vary in the range of mixed methods analysis tools and visualization for codes and text (such as code overlaps), with some also able to integrate data from surveys, citation managers, and other sources in analysis.

This page provides descriptions and links to some of the most common free and commercial CAQDAS tools below. If you are trying to decide on a tool, we recommend reading the box titled "Which CAQDAS is right for me?" first.

Does the Library or Virginia Tech provide access to qualitative software?

As a rule, no. Neither the University Libraries nor general campus computing labs provided licensed access options for paid CAQDAS, including NVivo, Dedoose, atlas.ti, MaxQDA, QDAMiner, or others. The only exception is that short-term access is available by arrangement for research use of atlas.ti in the Media Production Suite on the fourth floor of Newman Library (send inquiries to [email protected] ).

However, there are free options that are suitable for some users, and most paid CAQDAS provides free trials of 7-30 days for new users to test out the software. More information on licensing is below in the "Which CAQDAS is right for me?" section.

How can I learn more about this software?

The University Libraries offers workshops on NVivo, Dedoose, Atlas.ti and Taguette, as well as qualitative coding principles and collaborative coding in spreadsheets. Resources for self-learning can be found on the Qualitative Training and Consultation tab.

Which CAQDAS is right for me?

If you're new to qualitative data analysis, choosing a tool can be intimidating, particularly since the most widely used tools are commercial and not widely available in campus labs.

The questions below and the chart below can help you narrow your options for tools. Note that most paid tools have free trials available, and introductory workshops and consultation assistance are available for many through Data Services (see "Qualitative Training and Consultation" tab).

Simple projects can sometimes also be accomplished using tools already available to you, such as spreadsheets like Excel or Google Sheets, although they're not included in the chart because they don't share the same basic structure of CAQDAS. Additional tools not in the chart can be found in the sections below with brief descriptions and links.

What kind of sources can I analyze?

All major CAQDAS allows for analyzing text-based documents (Word, plain text, web page text, etc.) and most provide support for PDF files as well, though some do not support images in PDFs or older PDFs without text tagging. Some tools also support coding areas of images or directly coding time segments of audio or video (without first transcribing). Additionally, some tools allow importing spreadsheets, survey data, citation manager bibliographies, or other data types for use in mixed methods analysis (see "Analysis" section in chart).

Some tools also have options to import data directly from specific social media sites (X/Twitter, Facebook, YouTube, etc.), sometimes with special features. These are not listed in the chart because permissions and protocols change frequently and tend to break this functionality. If you are interested in working with qualitative data from a specific social media platform, please reach out to a  consultant  for assistance.

In most cases, coding and analysis are simplest if multimedia sources are first converted to text before importing to a project (see "Recording and Transcription" or "Qualitative Training and Consultation" tab), unless the visual or audio structure itself is being analyzed, beyond just the surface language.

How can I organize and apply codes?

All tools allow for manual selection and coding or tagging of the surface text of documents and (when the document types are supported) time segments of audio/visual sources and regions of images. Some tools also allow automatic coding of selected words or phrases when they appear in documents, often with tables or word clouds available to help find relevant phrases. Additionally, certain packages provide for automatic coding of text based on formatting or speaker names or even automatic identification of topics, sentiment (positive or negative), and named entities (people, places, groups, etc.) using machine learning or large language models. Both the level of necessary pre-processing and quality of these autocoding features vary widely, but they can be valuable tools to supplement manual coding or work across larger collections of sources.

Can I perform mixed methods analysis (and what kinds)?

All CAQDAS provide some basic functions for analysis, like the ability to count and view the subset of sources or text sections that have a specific tag applied to them. Most also provide for summaries of word counts in the text of one or more documents. However, support for various types of mixed methods analysis is one of the most significant differences between CAQDAS packages, so it is worth checking carefully 

What options are available to collaborate with other users?

Not all CAQDAS are equally good for projects with multiple coders, whether users are divvying up documents or coding each document multiple times. It is always possible to have different users of the same machine (or a cloud backup of the file with a service like OneDrive or Google Drive) open the same file and do work sequentially, but there are also two models that allow for multiple users to work on projects at the same time. Some use a synchronous cloud-based model, where all users can work on the same project simultaneously and changes are reflected in real time. Others have options to create multiple copies of a project that users can work on separately before merging them together at a later time.

Additionally, some packages with collaboration support also include extra features. Some allow for training and testing coders on a standard dataset to ensure pre-defined codes are applied consistently across users. Others also provide the ability to calculate measures of inter-rater reliability or coder agreement to measure how similar the final application of codes was across users. In all cases with collaboration, there are ways to choose a single final code from those applied by multiple users.

It's also worth pointing out that in choosing a CAQDAS package, it is worth consulting with likely collaborators about what (if any) packages they already use or have access to. What interoperability there is for qualitative data is generally limited to moving projects between packages, and currently only works well for the most common aspects of projects, such as textual data sources and code application, but not for unique or complex features such as coding multimedia or mixed methods analysis output. Therefore, it is strongly recommended to agree on a package with your entire research team early in the planning or data collection phases.

What does the software cost?

There are 3 basic licensing models for CAQDAS. Some tools are free and open source, although they tend to have a limited number of options. The remaining tools may be available as subscriptions (monthly or annual, typically with free upgrades), one-time purchases (major upgrades may require an additional fee), or both. Some tools have cloud-based data backup or syncing, either included in all licenses or as a paid add-on. All paid software is licensed by user, although some allow paying based on the number of users at one time, rather than the number of total users.

What if I want to mix and match features?

Each CAQDAS package stores data in its own proprietary project format, with limited or no interchangeability between software. Recently, however, major software companies agreed to support a qualitative interchange format called REFI-QDA (or sometimes QDPX) that allows for moving some elements of projects between tools by exporting text and XML-based files in a special format that the other tools can interpret.

This might allow, for example, coding documents using advanced auto-coding features in one package before exporting to analyze in a different package. However, advance pilot testing is important if you plan this kind of workflow, as even when data exported from one package can be imported to another, there may be losses or changes to structure because of differences in how the packages represent information internally. We recommend reaching out to a qualitative research  consultant for help if you think you may need features that are not available in a single tool.

Is CAQDAS accessible for the visually impaired?

All major CAQDAS include at least some accessibility features, most commonly screen reader compatibility and keyboard shortcuts, but vary widely in the range of available options. Atlas.ti also provides public information on WCAG 2.0 compliance. For more assistance with research software accessibility, please contact Accessible Technologies .

CAQDAS Package Features

CAQDAS Features Chart

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Research Method

Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

Qualitative Research

Qualitative research is a type of research methodology that focuses on exploring and understanding people’s beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Types of Qualitative Research

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

  • Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
  • Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
  • Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
  • Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
  • Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
  • Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
  • Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

  • Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
  • Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
  • Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
  • Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
  • Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
  • Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
  • Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

  • Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
  • Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
  • Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
  • Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
  • Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
  • Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
  • Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

  • Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
  • Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
  • Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
  • Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

  • Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
  • Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
  • Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
  • Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
  • Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

  • Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
  • Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
  • Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
  • Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
  • Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
  • Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

  • Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
  • Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
  • Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
  • Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
  • Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
  • Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

  • Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
  • Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
  • Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
  • Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
  • Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
  • Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

Also see Research Methods

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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Qualitative Research: Data Collection, Analysis, and Management

In an earlier paper, 1 we presented an introduction to using qualitative research methods in pharmacy practice. In this article, we review some principles of the collection, analysis, and management of qualitative data to help pharmacists interested in doing research in their practice to continue their learning in this area. Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. Whereas quantitative research methods can be used to determine how many people undertake particular behaviours, qualitative methods can help researchers to understand how and why such behaviours take place. Within the context of pharmacy practice research, qualitative approaches have been used to examine a diverse array of topics, including the perceptions of key stakeholders regarding prescribing by pharmacists and the postgraduation employment experiences of young pharmacists (see “Further Reading” section at the end of this article).

In the previous paper, 1 we outlined 3 commonly used methodologies: ethnography 2 , grounded theory 3 , and phenomenology. 4 Briefly, ethnography involves researchers using direct observation to study participants in their “real life” environment, sometimes over extended periods. Grounded theory and its later modified versions (e.g., Strauss and Corbin 5 ) use face-to-face interviews and interactions such as focus groups to explore a particular research phenomenon and may help in clarifying a less-well-understood problem, situation, or context. Phenomenology shares some features with grounded theory (such as an exploration of participants’ behaviour) and uses similar techniques to collect data, but it focuses on understanding how human beings experience their world. It gives researchers the opportunity to put themselves in another person’s shoes and to understand the subjective experiences of participants. 6 Some researchers use qualitative methodologies but adopt a different standpoint, and an example of this appears in the work of Thurston and others, 7 discussed later in this paper.

Qualitative work requires reflection on the part of researchers, both before and during the research process, as a way of providing context and understanding for readers. When being reflexive, researchers should not try to simply ignore or avoid their own biases (as this would likely be impossible); instead, reflexivity requires researchers to reflect upon and clearly articulate their position and subjectivities (world view, perspectives, biases), so that readers can better understand the filters through which questions were asked, data were gathered and analyzed, and findings were reported. From this perspective, bias and subjectivity are not inherently negative but they are unavoidable; as a result, it is best that they be articulated up-front in a manner that is clear and coherent for readers.


What qualitative study seeks to convey is why people have thoughts and feelings that might affect the way they behave. Such study may occur in any number of contexts, but here, we focus on pharmacy practice and the way people behave with regard to medicines use (e.g., to understand patients’ reasons for nonadherence with medication therapy or to explore physicians’ resistance to pharmacists’ clinical suggestions). As we suggested in our earlier article, 1 an important point about qualitative research is that there is no attempt to generalize the findings to a wider population. Qualitative research is used to gain insights into people’s feelings and thoughts, which may provide the basis for a future stand-alone qualitative study or may help researchers to map out survey instruments for use in a quantitative study. It is also possible to use different types of research in the same study, an approach known as “mixed methods” research, and further reading on this topic may be found at the end of this paper.

The role of the researcher in qualitative research is to attempt to access the thoughts and feelings of study participants. This is not an easy task, as it involves asking people to talk about things that may be very personal to them. Sometimes the experiences being explored are fresh in the participant’s mind, whereas on other occasions reliving past experiences may be difficult. However the data are being collected, a primary responsibility of the researcher is to safeguard participants and their data. Mechanisms for such safeguarding must be clearly articulated to participants and must be approved by a relevant research ethics review board before the research begins. Researchers and practitioners new to qualitative research should seek advice from an experienced qualitative researcher before embarking on their project.


Whatever philosophical standpoint the researcher is taking and whatever the data collection method (e.g., focus group, one-to-one interviews), the process will involve the generation of large amounts of data. In addition to the variety of study methodologies available, there are also different ways of making a record of what is said and done during an interview or focus group, such as taking handwritten notes or video-recording. If the researcher is audio- or video-recording data collection, then the recordings must be transcribed verbatim before data analysis can begin. As a rough guide, it can take an experienced researcher/transcriber 8 hours to transcribe one 45-minute audio-recorded interview, a process than will generate 20–30 pages of written dialogue.

Many researchers will also maintain a folder of “field notes” to complement audio-taped interviews. Field notes allow the researcher to maintain and comment upon impressions, environmental contexts, behaviours, and nonverbal cues that may not be adequately captured through the audio-recording; they are typically handwritten in a small notebook at the same time the interview takes place. Field notes can provide important context to the interpretation of audio-taped data and can help remind the researcher of situational factors that may be important during data analysis. Such notes need not be formal, but they should be maintained and secured in a similar manner to audio tapes and transcripts, as they contain sensitive information and are relevant to the research. For more information about collecting qualitative data, please see the “Further Reading” section at the end of this paper.


If, as suggested earlier, doing qualitative research is about putting oneself in another person’s shoes and seeing the world from that person’s perspective, the most important part of data analysis and management is to be true to the participants. It is their voices that the researcher is trying to hear, so that they can be interpreted and reported on for others to read and learn from. To illustrate this point, consider the anonymized transcript excerpt presented in Appendix 1 , which is taken from a research interview conducted by one of the authors (J.S.). We refer to this excerpt throughout the remainder of this paper to illustrate how data can be managed, analyzed, and presented.

Interpretation of Data

Interpretation of the data will depend on the theoretical standpoint taken by researchers. For example, the title of the research report by Thurston and others, 7 “Discordant indigenous and provider frames explain challenges in improving access to arthritis care: a qualitative study using constructivist grounded theory,” indicates at least 2 theoretical standpoints. The first is the culture of the indigenous population of Canada and the place of this population in society, and the second is the social constructivist theory used in the constructivist grounded theory method. With regard to the first standpoint, it can be surmised that, to have decided to conduct the research, the researchers must have felt that there was anecdotal evidence of differences in access to arthritis care for patients from indigenous and non-indigenous backgrounds. With regard to the second standpoint, it can be surmised that the researchers used social constructivist theory because it assumes that behaviour is socially constructed; in other words, people do things because of the expectations of those in their personal world or in the wider society in which they live. (Please see the “Further Reading” section for resources providing more information about social constructivist theory and reflexivity.) Thus, these 2 standpoints (and there may have been others relevant to the research of Thurston and others 7 ) will have affected the way in which these researchers interpreted the experiences of the indigenous population participants and those providing their care. Another standpoint is feminist standpoint theory which, among other things, focuses on marginalized groups in society. Such theories are helpful to researchers, as they enable us to think about things from a different perspective. Being aware of the standpoints you are taking in your own research is one of the foundations of qualitative work. Without such awareness, it is easy to slip into interpreting other people’s narratives from your own viewpoint, rather than that of the participants.

To analyze the example in Appendix 1 , we will adopt a phenomenological approach because we want to understand how the participant experienced the illness and we want to try to see the experience from that person’s perspective. It is important for the researcher to reflect upon and articulate his or her starting point for such analysis; for example, in the example, the coder could reflect upon her own experience as a female of a majority ethnocultural group who has lived within middle class and upper middle class settings. This personal history therefore forms the filter through which the data will be examined. This filter does not diminish the quality or significance of the analysis, since every researcher has his or her own filters; however, by explicitly stating and acknowledging what these filters are, the researcher makes it easer for readers to contextualize the work.

Transcribing and Checking

For the purposes of this paper it is assumed that interviews or focus groups have been audio-recorded. As mentioned above, transcribing is an arduous process, even for the most experienced transcribers, but it must be done to convert the spoken word to the written word to facilitate analysis. For anyone new to conducting qualitative research, it is beneficial to transcribe at least one interview and one focus group. It is only by doing this that researchers realize how difficult the task is, and this realization affects their expectations when asking others to transcribe. If the research project has sufficient funding, then a professional transcriber can be hired to do the work. If this is the case, then it is a good idea to sit down with the transcriber, if possible, and talk through the research and what the participants were talking about. This background knowledge for the transcriber is especially important in research in which people are using jargon or medical terms (as in pharmacy practice). Involving your transcriber in this way makes the work both easier and more rewarding, as he or she will feel part of the team. Transcription editing software is also available, but it is expensive. For example, ELAN (more formally known as EUDICO Linguistic Annotator, developed at the Technical University of Berlin) 8 is a tool that can help keep data organized by linking media and data files (particularly valuable if, for example, video-taping of interviews is complemented by transcriptions). It can also be helpful in searching complex data sets. Products such as ELAN do not actually automatically transcribe interviews or complete analyses, and they do require some time and effort to learn; nonetheless, for some research applications, it may be a valuable to consider such software tools.

All audio recordings should be transcribed verbatim, regardless of how intelligible the transcript may be when it is read back. Lines of text should be numbered. Once the transcription is complete, the researcher should read it while listening to the recording and do the following: correct any spelling or other errors; anonymize the transcript so that the participant cannot be identified from anything that is said (e.g., names, places, significant events); insert notations for pauses, laughter, looks of discomfort; insert any punctuation, such as commas and full stops (periods) (see Appendix 1 for examples of inserted punctuation), and include any other contextual information that might have affected the participant (e.g., temperature or comfort of the room).

Dealing with the transcription of a focus group is slightly more difficult, as multiple voices are involved. One way of transcribing such data is to “tag” each voice (e.g., Voice A, Voice B). In addition, the focus group will usually have 2 facilitators, whose respective roles will help in making sense of the data. While one facilitator guides participants through the topic, the other can make notes about context and group dynamics. More information about group dynamics and focus groups can be found in resources listed in the “Further Reading” section.

Reading between the Lines

During the process outlined above, the researcher can begin to get a feel for the participant’s experience of the phenomenon in question and can start to think about things that could be pursued in subsequent interviews or focus groups (if appropriate). In this way, one participant’s narrative informs the next, and the researcher can continue to interview until nothing new is being heard or, as it says in the text books, “saturation is reached”. While continuing with the processes of coding and theming (described in the next 2 sections), it is important to consider not just what the person is saying but also what they are not saying. For example, is a lengthy pause an indication that the participant is finding the subject difficult, or is the person simply deciding what to say? The aim of the whole process from data collection to presentation is to tell the participants’ stories using exemplars from their own narratives, thus grounding the research findings in the participants’ lived experiences.

Smith 9 suggested a qualitative research method known as interpretative phenomenological analysis, which has 2 basic tenets: first, that it is rooted in phenomenology, attempting to understand the meaning that individuals ascribe to their lived experiences, and second, that the researcher must attempt to interpret this meaning in the context of the research. That the researcher has some knowledge and expertise in the subject of the research means that he or she can have considerable scope in interpreting the participant’s experiences. Larkin and others 10 discussed the importance of not just providing a description of what participants say. Rather, interpretative phenomenological analysis is about getting underneath what a person is saying to try to truly understand the world from his or her perspective.

Once all of the research interviews have been transcribed and checked, it is time to begin coding. Field notes compiled during an interview can be a useful complementary source of information to facilitate this process, as the gap in time between an interview, transcribing, and coding can result in memory bias regarding nonverbal or environmental context issues that may affect interpretation of data.

Coding refers to the identification of topics, issues, similarities, and differences that are revealed through the participants’ narratives and interpreted by the researcher. This process enables the researcher to begin to understand the world from each participant’s perspective. Coding can be done by hand on a hard copy of the transcript, by making notes in the margin or by highlighting and naming sections of text. More commonly, researchers use qualitative research software (e.g., NVivo, QSR International Pty Ltd; ) to help manage their transcriptions. It is advised that researchers undertake a formal course in the use of such software or seek supervision from a researcher experienced in these tools.

Returning to Appendix 1 and reading from lines 8–11, a code for this section might be “diagnosis of mental health condition”, but this would just be a description of what the participant is talking about at that point. If we read a little more deeply, we can ask ourselves how the participant might have come to feel that the doctor assumed he or she was aware of the diagnosis or indeed that they had only just been told the diagnosis. There are a number of pauses in the narrative that might suggest the participant is finding it difficult to recall that experience. Later in the text, the participant says “nobody asked me any questions about my life” (line 19). This could be coded simply as “health care professionals’ consultation skills”, but that would not reflect how the participant must have felt never to be asked anything about his or her personal life, about the participant as a human being. At the end of this excerpt, the participant just trails off, recalling that no-one showed any interest, which makes for very moving reading. For practitioners in pharmacy, it might also be pertinent to explore the participant’s experience of akathisia and why this was left untreated for 20 years.

One of the questions that arises about qualitative research relates to the reliability of the interpretation and representation of the participants’ narratives. There are no statistical tests that can be used to check reliability and validity as there are in quantitative research. However, work by Lincoln and Guba 11 suggests that there are other ways to “establish confidence in the ‘truth’ of the findings” (p. 218). They call this confidence “trustworthiness” and suggest that there are 4 criteria of trustworthiness: credibility (confidence in the “truth” of the findings), transferability (showing that the findings have applicability in other contexts), dependability (showing that the findings are consistent and could be repeated), and confirmability (the extent to which the findings of a study are shaped by the respondents and not researcher bias, motivation, or interest).

One way of establishing the “credibility” of the coding is to ask another researcher to code the same transcript and then to discuss any similarities and differences in the 2 resulting sets of codes. This simple act can result in revisions to the codes and can help to clarify and confirm the research findings.

Theming refers to the drawing together of codes from one or more transcripts to present the findings of qualitative research in a coherent and meaningful way. For example, there may be examples across participants’ narratives of the way in which they were treated in hospital, such as “not being listened to” or “lack of interest in personal experiences” (see Appendix 1 ). These may be drawn together as a theme running through the narratives that could be named “the patient’s experience of hospital care”. The importance of going through this process is that at its conclusion, it will be possible to present the data from the interviews using quotations from the individual transcripts to illustrate the source of the researchers’ interpretations. Thus, when the findings are organized for presentation, each theme can become the heading of a section in the report or presentation. Underneath each theme will be the codes, examples from the transcripts, and the researcher’s own interpretation of what the themes mean. Implications for real life (e.g., the treatment of people with chronic mental health problems) should also be given.


In this final section of this paper, we describe some ways of drawing together or “synthesizing” research findings to represent, as faithfully as possible, the meaning that participants ascribe to their life experiences. This synthesis is the aim of the final stage of qualitative research. For most readers, the synthesis of data presented by the researcher is of crucial significance—this is usually where “the story” of the participants can be distilled, summarized, and told in a manner that is both respectful to those participants and meaningful to readers. There are a number of ways in which researchers can synthesize and present their findings, but any conclusions drawn by the researchers must be supported by direct quotations from the participants. In this way, it is made clear to the reader that the themes under discussion have emerged from the participants’ interviews and not the mind of the researcher. The work of Latif and others 12 gives an example of how qualitative research findings might be presented.

Planning and Writing the Report

As has been suggested above, if researchers code and theme their material appropriately, they will naturally find the headings for sections of their report. Qualitative researchers tend to report “findings” rather than “results”, as the latter term typically implies that the data have come from a quantitative source. The final presentation of the research will usually be in the form of a report or a paper and so should follow accepted academic guidelines. In particular, the article should begin with an introduction, including a literature review and rationale for the research. There should be a section on the chosen methodology and a brief discussion about why qualitative methodology was most appropriate for the study question and why one particular methodology (e.g., interpretative phenomenological analysis rather than grounded theory) was selected to guide the research. The method itself should then be described, including ethics approval, choice of participants, mode of recruitment, and method of data collection (e.g., semistructured interviews or focus groups), followed by the research findings, which will be the main body of the report or paper. The findings should be written as if a story is being told; as such, it is not necessary to have a lengthy discussion section at the end. This is because much of the discussion will take place around the participants’ quotes, such that all that is needed to close the report or paper is a summary, limitations of the research, and the implications that the research has for practice. As stated earlier, it is not the intention of qualitative research to allow the findings to be generalized, and therefore this is not, in itself, a limitation.

Planning out the way that findings are to be presented is helpful. It is useful to insert the headings of the sections (the themes) and then make a note of the codes that exemplify the thoughts and feelings of your participants. It is generally advisable to put in the quotations that you want to use for each theme, using each quotation only once. After all this is done, the telling of the story can begin as you give your voice to the experiences of the participants, writing around their quotations. Do not be afraid to draw assumptions from the participants’ narratives, as this is necessary to give an in-depth account of the phenomena in question. Discuss these assumptions, drawing on your participants’ words to support you as you move from one code to another and from one theme to the next. Finally, as appropriate, it is possible to include examples from literature or policy documents that add support for your findings. As an exercise, you may wish to code and theme the sample excerpt in Appendix 1 and tell the participant’s story in your own way. Further reading about “doing” qualitative research can be found at the end of this paper.


Qualitative research can help researchers to access the thoughts and feelings of research participants, which can enable development of an understanding of the meaning that people ascribe to their experiences. It can be used in pharmacy practice research to explore how patients feel about their health and their treatment. Qualitative research has been used by pharmacists to explore a variety of questions and problems (see the “Further Reading” section for examples). An understanding of these issues can help pharmacists and other health care professionals to tailor health care to match the individual needs of patients and to develop a concordant relationship. Doing qualitative research is not easy and may require a complete rethink of how research is conducted, particularly for researchers who are more familiar with quantitative approaches. There are many ways of conducting qualitative research, and this paper has covered some of the practical issues regarding data collection, analysis, and management. Further reading around the subject will be essential to truly understand this method of accessing peoples’ thoughts and feelings to enable researchers to tell participants’ stories.

Appendix 1. Excerpt from a sample transcript

The participant (age late 50s) had suffered from a chronic mental health illness for 30 years. The participant had become a “revolving door patient,” someone who is frequently in and out of hospital. As the participant talked about past experiences, the researcher asked:

  • What was treatment like 30 years ago?
  • Umm—well it was pretty much they could do what they wanted with you because I was put into the er, the er kind of system er, I was just on
  • endless section threes.
  • Really…
  • But what I didn’t realize until later was that if you haven’t actually posed a threat to someone or yourself they can’t really do that but I didn’t know
  • that. So wh-when I first went into hospital they put me on the forensic ward ’cause they said, “We don’t think you’ll stay here we think you’ll just
  • run-run away.” So they put me then onto the acute admissions ward and – er – I can remember one of the first things I recall when I got onto that
  • ward was sitting down with a er a Dr XXX. He had a book this thick [gestures] and on each page it was like three questions and he went through
  • all these questions and I answered all these questions. So we’re there for I don’t maybe two hours doing all that and he asked me he said “well
  • when did somebody tell you then that you have schizophrenia” I said “well nobody’s told me that” so he seemed very surprised but nobody had
  • actually [pause] whe-when I first went up there under police escort erm the senior kind of consultants people I’d been to where I was staying and
  • ermm so er [pause] I . . . the, I can remember the very first night that I was there and given this injection in this muscle here [gestures] and just
  • having dreadful side effects the next day I woke up [pause]
  • . . . and I suffered that akathesia I swear to you, every minute of every day for about 20 years.
  • Oh how awful.
  • And that side of it just makes life impossible so the care on the wards [pause] umm I don’t know it’s kind of, it’s kind of hard to put into words
  • [pause]. Because I’m not saying they were sort of like not friendly or interested but then nobody ever seemed to want to talk about your life [pause]
  • nobody asked me any questions about my life. The only questions that came into was they asked me if I’d be a volunteer for these student exams
  • and things and I said “yeah” so all the questions were like “oh what jobs have you done,” er about your relationships and things and er but
  • nobody actually sat down and had a talk and showed some interest in you as a person you were just there basically [pause] um labelled and you
  • know there was there was [pause] but umm [pause] yeah . . .

This article is the 10th in the CJHP Research Primer Series, an initiative of the CJHP Editorial Board and the CSHP Research Committee. The planned 2-year series is intended to appeal to relatively inexperienced researchers, with the goal of building research capacity among practising pharmacists. The articles, presenting simple but rigorous guidance to encourage and support novice researchers, are being solicited from authors with appropriate expertise.

Previous articles in this series:

Bond CM. The research jigsaw: how to get started. Can J Hosp Pharm . 2014;67(1):28–30.

Tully MP. Research: articulating questions, generating hypotheses, and choosing study designs. Can J Hosp Pharm . 2014;67(1):31–4.

Loewen P. Ethical issues in pharmacy practice research: an introductory guide. Can J Hosp Pharm. 2014;67(2):133–7.

Tsuyuki RT. Designing pharmacy practice research trials. Can J Hosp Pharm . 2014;67(3):226–9.

Bresee LC. An introduction to developing surveys for pharmacy practice research. Can J Hosp Pharm . 2014;67(4):286–91.

Gamble JM. An introduction to the fundamentals of cohort and case–control studies. Can J Hosp Pharm . 2014;67(5):366–72.

Austin Z, Sutton J. Qualitative research: getting started. C an J Hosp Pharm . 2014;67(6):436–40.

Houle S. An introduction to the fundamentals of randomized controlled trials in pharmacy research. Can J Hosp Pharm . 2014; 68(1):28–32.

Charrois TL. Systematic reviews: What do you need to know to get started? Can J Hosp Pharm . 2014;68(2):144–8.

Competing interests: None declared.

Further Reading

Examples of qualitative research in pharmacy practice.

  • Farrell B, Pottie K, Woodend K, Yao V, Dolovich L, Kennie N, et al. Shifts in expectations: evaluating physicians’ perceptions as pharmacists integrated into family practice. J Interprof Care. 2010; 24 (1):80–9. [ PubMed ] [ Google Scholar ]
  • Gregory P, Austin Z. Postgraduation employment experiences of new pharmacists in Ontario in 2012–2013. Can Pharm J. 2014; 147 (5):290–9. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Marks PZ, Jennnings B, Farrell B, Kennie-Kaulbach N, Jorgenson D, Pearson-Sharpe J, et al. “I gained a skill and a change in attitude”: a case study describing how an online continuing professional education course for pharmacists supported achievement of its transfer to practice outcomes. Can J Univ Contin Educ. 2014; 40 (2):1–18. [ Google Scholar ]
  • Nair KM, Dolovich L, Brazil K, Raina P. It’s all about relationships: a qualitative study of health researchers’ perspectives on interdisciplinary research. BMC Health Serv Res. 2008; 8 :110. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Pojskic N, MacKeigan L, Boon H, Austin Z. Initial perceptions of key stakeholders in Ontario regarding independent prescriptive authority for pharmacists. Res Soc Adm Pharm. 2014; 10 (2):341–54. [ PubMed ] [ Google Scholar ]

Qualitative Research in General

  • Breakwell GM, Hammond S, Fife-Schaw C. Research methods in psychology. Thousand Oaks (CA): Sage Publications; 1995. [ Google Scholar ]
  • Given LM. 100 questions (and answers) about qualitative research. Thousand Oaks (CA): Sage Publications; 2015. [ Google Scholar ]
  • Miles B, Huberman AM. Qualitative data analysis. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]
  • Patton M. Qualitative research and evaluation methods. Thousand Oaks (CA): Sage Publications; 2002. [ Google Scholar ]
  • Willig C. Introducing qualitative research in psychology. Buckingham (UK): Open University Press; 2001. [ Google Scholar ]

Group Dynamics in Focus Groups

  • Farnsworth J, Boon B. Analysing group dynamics within the focus group. Qual Res. 2010; 10 (5):605–24. [ Google Scholar ]

Social Constructivism

  • Social constructivism. Berkeley (CA): University of California, Berkeley, Berkeley Graduate Division, Graduate Student Instruction Teaching & Resource Center; [cited 2015 June 4]. Available from: [ Google Scholar ]

Mixed Methods

  • Creswell J. Research design: qualitative, quantitative, and mixed methods approaches. Thousand Oaks (CA): Sage Publications; 2009. [ Google Scholar ]

Collecting Qualitative Data

  • Arksey H, Knight P. Interviewing for social scientists: an introductory resource with examples. Thousand Oaks (CA): Sage Publications; 1999. [ Google Scholar ]
  • Guest G, Namey EE, Mitchel ML. Collecting qualitative data: a field manual for applied research. Thousand Oaks (CA): Sage Publications; 2013. [ Google Scholar ]

Constructivist Grounded Theory

  • Charmaz K. Grounded theory: objectivist and constructivist methods. In: Denzin N, Lincoln Y, editors. Handbook of qualitative research. 2nd ed. Thousand Oaks (CA): Sage Publications; 2000. pp. 509–35. [ Google Scholar ]


Qualitative data refers to non-numeric information such as interview transcripts, notes, video and audio recordings, images and text documents. Qualitative data analysis can be divided into the following five categories:

1. Content analysis . This refers to the process of categorizing verbal or behavioural data to classify, summarize and tabulate the data.

2. Narrative analysis . This method involves the reformulation of stories presented by respondents taking into account context of each case and different experiences of each respondent. In other words, narrative analysis is the revision of primary qualitative data by researcher.

3. Discourse analysis . A method of analysis of naturally occurring talk and all types of written text.

4. Framework analysis . This is more advanced method that consists of several stages such as familiarization, identifying a thematic framework, coding, charting, mapping and interpretation.

5. Grounded theory . This method of qualitative data analysis starts with an analysis of a single case to formulate a theory. Then, additional cases are examined to see if they contribute to the theory.

Qualitative data analysis can be conducted through the following three steps:

Step 1: Developing and Applying Codes . Coding can be explained as categorization of data. A ‘code’ can be a word or a short phrase that represents a theme or an idea. All codes need to be assigned meaningful titles. A wide range of non-quantifiable elements such as events, behaviours, activities, meanings etc. can be coded.

There are three types of coding:

  • Open coding . The initial organization of raw data to try to make sense of it.
  • Axial coding . Interconnecting and linking the categories of codes.
  • Selective coding . Formulating the story through connecting the categories.

Coding can be done manually or using qualitative data analysis software such as

 NVivo,  Atlas ti 6.0,  HyperRESEARCH 2.8,  Max QDA and others.

When using manual coding you can use folders, filing cabinets, wallets etc. to gather together materials that are examples of similar themes or analytic ideas. Manual method of coding in qualitative data analysis is rightly considered as labour-intensive, time-consuming and outdated.

In computer-based coding, on the other hand, physical files and cabinets are replaced with computer based directories and files. When choosing software for qualitative data analysis you need to consider a wide range of factors such as the type and amount of data you need to analyse, time required to master the software and cost considerations.

Moreover, it is important to get confirmation from your dissertation supervisor prior to application of any specific qualitative data analysis software.

The following table contains examples of research titles, elements to be coded and identification of relevant codes:

 Qualitative data coding

Step 2: Identifying themes, patterns and relationships . Unlike quantitative methods , in qualitative data analysis there are no universally applicable techniques that can be applied to generate findings. Analytical and critical thinking skills of researcher plays significant role in data analysis in qualitative studies. Therefore, no qualitative study can be repeated to generate the same results.

Nevertheless, there is a set of techniques that you can use to identify common themes, patterns and relationships within responses of sample group members in relation to codes that have been specified in the previous stage.

Specifically, the most popular and effective methods of qualitative data interpretation include the following:

  • Word and phrase repetitions – scanning primary data for words and phrases most commonly used by respondents, as well as, words and phrases used with unusual emotions;
  • Primary and secondary data comparisons – comparing the findings of interview/focus group/observation/any other qualitative data collection method with the findings of literature review and discussing differences between them;
  • Search for missing information – discussions about which aspects of the issue was not mentioned by respondents, although you expected them to be mentioned;
  • Metaphors and analogues – comparing primary research findings to phenomena from a different area and discussing similarities and differences.

Step 3: Summarizing the data . At this last stage you need to link research findings to hypotheses or research aim and objectives. When writing data analysis chapter, you can use noteworthy quotations from the transcript in order to highlight major themes within findings and possible contradictions.

It is important to note that the process of qualitative data analysis described above is general and different types of qualitative studies may require slightly different methods of data analysis.

My  e-book,  The Ultimate Guide to Writing a Dissertation in Business Studies: a step by step approach  contains a detailed, yet simple explanation of qualitative data analysis methods . The e-book explains all stages of the research process starting from the selection of the research area to writing personal reflection. Important elements of dissertations such as research philosophy, research approach, research design, methods of data collection and data analysis are explained in simple words. John Dudovskiy

Qualitative Data Analysis

How to Analyse Qualitative Data: Methods, Steps, and Process

10 min read

Wondering how to analyse qualitative data and get actionable insights? Search no further!

This article will help you analyse qualitative data and fuel your product growth . We’ll walk you through the following steps:

  • 5 qualitative data analysis methods

5 steps to analysing qualitative data

  • How to act on research findings

Let’s get started!

  • Qualitative data analysis is the process of turning qualitative data into insights. This could be anything from customer feedback , surveys, website recordings, or in-depth interviews.

Qualitative data is often seen as more “rich” and “human” than quantitative data, which is why product teams use it to refine customer acquisition and retention strategies and uncover product growth opportunities.

  • As opposed to qualitative data, quantitative data analysis is the process of analysing numerical data to locate patterns and trends.
  • Content analysis is a method of qualitative data analysis that involves systematically analysing a text to identify certain features or patterns.
  • Thematic analysis is used to identify patterns and themes in data.
  • Narrative analysis involves identifying, analysing, and interpreting the stories that customers or research participants tell.
  • A grounded theory analysis involves the constant comparative method, which means that qualitative researchers analyse and code the data on the fly.
  • Discourse analysis is about understanding how people communicate with each other. It’s used to analyse written or spoken language.

Here are 5 steps to analysing qualitative data: 1. Define your research questions to guide the analysis. 2. Collect qualitative data from user feedback , NPS follow-up questions, interviews, and open-ended questions. 3. Organize and categorize qualitative data to detect patterns and group them more easily. 4. Identify common themes, patterns, and relationships. 5. Summarize the qualitative analysis process.

  • Userpilot will help you collect data with in-app microsurveys . It also offers a range of features to help you analyse that data, including sentiment analysis , and user segmentation .

Qualitative data analysis is the process of turning qualitative data — information that can’t be measured numerically — into insights.

This could be anything from customer feedback, surveys , website recordings, or in-depth interviews.

Quantitative data analysis vs. Qualitative data analysis

Here let’s understand the difference between qualitative and quantitative data analysis.

Quantitative data analysis is the process of analysing numerical data in order to locate patterns and trends. Quantitative research deals with numbers and statistics to systematically measure variables and test hypotheses.

Qualitative data analysis, on the other hand, is the process of analysing non-numerical, textual data to derive actionable insights from it. This data type is often more “open-ended” and can be harder to draw conclusions from.

However, qualitative data can provide insights that quantitative data cannot. For example, qualitative data can help you understand how customers feel about your product, their unmet needs, and what motivates them.

What are the 5 qualitative data analysis methods?

There are 5 main methods of qualitative data analysis. Which one you choose will depend on the type of data you collect, your preferences, and your research goals.

Content analysis method

Content analysis is a qualitative data analysis method that systematically analyses a text to identify specific features or patterns. This could be anything from a customer interview transcript to survey responses, social media posts, or customer success calls.

The data is first coded, which means assigning it labels or categories.

For example, if you were looking at customer feedback , you might code all mentions of “price” as “P,” all mentions of “quality” as “Q,” and so on. Once manual coding is done, start looking for patterns and trends in the codes.

Content analysis is a prevalent qualitative data analysis method, as it is relatively quick and easy to do and can be done by anyone with a good understanding of the data.

The advantages of content analysis

  • Rich insights : Content analysis can provide rich, in-depth insights into how customers feel about your product, what their unmet needs are, and their motives.
  • Easily replicable : Once you have developed a coding system, content analysis is relatively quick and easy because it’s a systematic process.
  • Affordable : Content analysis requires very little investment since all you need is a good understanding of the data, and it doesn’t require any special software.

However, content analysis has its drawbacks too:

  • Time-consuming : Coding the data is time-consuming, particularly if you have a large amount of data to analyse.
  • Ignores context: Content analysis can ignore the context in which the data was collected which may lead to misinterpretations.
  • Reductive approach : Some people argue that content analysis is a reductive approach to qualitative data because it involves breaking the data down into smaller pieces.

Thematic analysis method

Thematic analysis is a popular qualitative data analysis method that identifies patterns and themes in data. The process of thematic analysis involves coding the data, which means assigning it labels or categories.

It can be paired with sentiment analysis to determine whether a piece of writing is positive, negative, or neutral. This can be done using a lexicon (i.e., a list of words and their associated sentiment scores).

A common use case for thematic analysis in SaaS companies is customer feedback analysis with NPS surveys and NPS tagging to identify patterns among your customer base.

The advantages of thematic analysis:

  • Doesn’t require training : Anyone with little training on how to label the data can perform thematic analysis.
  • It’s easy to draw important information from raw data : Surveys or customer interviews can be easily converted into insights and quantitative data with the help of labeling.
  • An effective way to process large amounts of data if done automatically: you will need AI tools for this.

The drawbacks of thematic analysis:

  • Doesn’t capture complex narratives : If the data isn’t coded correctly, it can be difficult to identify themes since it’s a phrase-based method.
  • Difficult to implement from scratch because a perfect approach must be able to merge and organize themes in a meaningful way, producing a set of themes that are not too generic and not too large.

Narrative analysis method

Analysing qualitative data with narrative analysis involves identifying, analysing, and interpreting customer or research participants’ stories. The input can be in the form of customer interviews, testimonials, or other text data.

Narrative analysis helps product managers to understand customers’ feelings toward the product and identify trends in customer behavior and personalize their in-app experiences .

The advantages of narrative analysis:

  • Provide a rich form of data: The stories people tell give a deep understanding of customers’ needs and pain points.
  • Collects unique, in-depth data based on customer interviews or testimonials.

The drawbacks of narrative analysis:

  • Hard to implement in studies of large numbers.
  • Time-consuming: Transcribing customer interviews or testimonials is labor-intensive.
  • Hard to reproduce since it relies on customer stories, which are unique.

Grounded theory analysis method

Grounded theory analysis is a method that involves the constant comparative method, meaning qualitative researchers analyse and code the data on the fly.

The grounded theory approach is useful for product managers who want to understand how customers interact with their products . It can also be used to generate hypotheses about how customers will behave in the future.

Suppose product teams want to understand the reasons behind the high churn rate , they can use customer surveys and grounded theory to analyse responses and develop hypotheses about why users churn and how to reengage inactive ones .

You can filter the disengaged/inactive user segment to make analysis easier.

The advantages of grounded theory:

  • Based on actual data , qualitative analysis is more accurate than other methods that rely on assumptions.
  • Analyse poorly researched topics by generating hypotheses.
  • Reduces the bias in interpreting qualitative data as it’s analysed and coded as it’s collected.

The drawbacks of grounded theory:

  • Overly theoretical
  • Requires a lot of objectivity, creativity, and critical thinking

Discourse analysis method

Discourse analysis is about understanding how people communicate with each other. It can be used to analyse written or spoken language. For instance, product teams can use discourse analysis to understand how customers talk about their products on the web.

The advantages of discourse analysis:

  • Uncovers motivation behind customers’ words
  • Gives insights into customer data

The drawbacks of discourse analysis:

  • Takes a large amount of time and effort as the process is highly specialized and requires training and practice. There’s no “right” way to do it
  • Focuses solely on language

With all that theory above, we’ve decided to elicit the essential steps of qualitative research methods and designed a super simple guide for gathering qualitative data.

Let’s dive in!

Step 1: Define your research questions

The first step in qualitative data analysis is to define your research questions . It’s important to be as specific as possible, as this will guide the rest of your analysis.

Examples are:

  • What are the primary reasons customers are dissatisfied with our product?
  • How does X group of users feel about our new feature?
  • What are our customers’ needs, and how do they vary by segment?
  • How do our products fit into our customers’ lives?
  • What factors influence the low feature usage rate of the new feature ?

Step 2: Data collection

Now, you decide what type of data collection to use based on previously defined goals. Here are 5 methods to collect qualitative data for product companies:

  • User feedback
  • NPS follow-up questions
  • Open-ended questions in surveys
  • User interviews

We recommend using a mix of in-app surveys and in-person interviews. The former helps to collect rich data automatically and on an ongoing basis. You can collect user feedback through in-product surveys, NPS platforms, or use Zoom for live interviews.

The latter enables you to understand the customer experience in the business context as you can ask clarifying questions during the interviews.

Step 3: Organize and categorize qualitative data

Before analysing customer feedback and assigning any value, data needs to be organized in a single place. This will help you detect patterns and similar themes more easily.

One way to do this is to create a spreadsheet with all the data organized by research questions. Then, arrange the data by theme or category within each research question.

You can also organize NPS responses with Userpilot . This will allow you to quickly calculate scores and see how many promoters, passives, and detractors there are for each research question.

Userpilot’s NPS dashboard.

Step 4: Identify themes, patterns, and relationships

Themes are the building blocks of analysis and help you understand how your data fits together.

For product teams, an NPS survey might reveal the following themes: product defect, pricing, and customer service. Thus, the main themes in SaaS will be around identifying friction points, usability issues, UI issues, UX issues, missing features, etc.

You need to define specific themes and then identify how often they occur. In turn, the pattern is a relationship between 2 or multiple elements (e.g. users who have specific JTBD complain of a specific missing feature).

Pair themes with in-app customer behavior and product usage data to understand whether different user segments fall under specific feedback themes.

To track user behavior, use Userpilot’s features like Goals, Feature tagging (these monitor product usage), and advanced analytics to divide users by shared characteristics.

Following this step, you will get enough data to improve customer loyalty .

Step 5: Summarize the qualitative analysis process

The last step in qualitative research is to analyse the data collected to find insights. Segment your users based on in-app behavior, user type, company size, or job to be done to draw meaningful decisions.

For instance, you may notice that negative feedback stems from the customer segment that recently engaged with XYZ features. Just like that, you can pinpoint friction points and the strongest sides of your product to capitalize on.

Analysing qualitative data with Userpilot

Userpilot is a product growth platform that helps product managers collect and analyse qualitative data. It offers a suite of features to make it easy to understand how users interact with your product, their needs, and how you can improve user experience.

For qualitative research, Userpilot will help you collect data with in-app microsurveys and the NPS tagging feature.

It also offers a range of features to help you analyse that data, including sentiment analysis, journey mapping, and user segmentation.

You can run contextual microsurveys that are tied back to specific user actions. This helps to collect highly relevant feedback without impeding the customer experience.

Once you have collected NPS responses, you can tag them to find reasons why users are happy/unhappy about the product (aka conduct the coding process). The example below shows that users lack the needed features and complain about the price.

Tag NPS responses with Userpilot .

With this tool in your arsenal, you’ll be able to make informed decisions about how to improve your product and grow your business.

The qualitative data analysis process is iterative and should be revisited as new data is collected. The goal is to constantly refine your understanding of your customer base and how they interact with your product.

Want to get started with qualitative analysis? Get a Userpilot Demo and automate the data collection process. Save time on mundane work and understand your customer better!

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Brief research report article.

This article is part of the research topic.

Tools, Frameworks, and Approaches for Enhancing Research Methods Teaching

Teaching advanced statistical methods to postgraduate novices: A case example

  • 1 School of Learning, Development & Professional Practice, Faculty of Education & Social Work, The University of Auckland, New Zealand

The final, formatted version of the article will be published soon.

Higher degree research students in education are largely underprepared for understanding or employing statistical data analysis methods. This is despite their need to read literature in their field which will indubitably include such research. This weakness may result in students choosing to use qualitative or interpretivist methodologies, even though education data are highly complex requiring sophisticated analysis techniques to properly evaluate the impact of nested data, multi-collinear factors, missing data, and changes over time. This paper describes a research methods course at a research-intensive university designed for students in a thesisonly degree program. The course emphasises the logic and conceptual function of statistical methods and exposes students to hands-on tutorials in which students are required to conduct analyses with open-access data. The first half of the 12-week course focuses on core knowledge, normally taught in first-year probability and statistics courses. The second half focuses on introducing and modeling advanced statistical methods needed to handle complex problems and data. The course outline is provided along with descriptions of teaching and assessments. This exemplar functions as a potential model of how relative novices in statistical methods can be introduced to a conceptual use of statistical methods to raise the credibility of research.

Keywords: Teaching, doctoral study, Novice learners, Course design and curricula, Quantitative Methods

Received: 29 Sep 2023; Accepted: 13 Dec 2023.

Copyright: © 2023 Brown. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Prof. Gavin T. Brown, The University of Auckland, School of Learning, Development & Professional Practice, Faculty of Education & Social Work, Auckland, 1142, New Zealand

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  • Equitable and accessible informed healthcare consent process for people with intellectual disability: a systematic literature review
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  • Manjekah Dunn 1 , 2 ,
  • Iva Strnadová 3 , 4 , 5 ,
  • Jackie Leach Scully 4 ,
  • Jennifer Hansen 3 ,
  • Julie Loblinzk 3 , 5 ,
  • Skie Sarfaraz 5 ,
  • Chloe Molnar 1 ,
  • Elizabeth Emma Palmer 1 , 2
  • 1 Faculty of Medicine & Health , University of New South Wales , Sydney , New South Wales , Australia
  • 2 The Sydney Children's Hospitals Network , Sydney , New South Wales , Australia
  • 3 School of Education , University of New South Wales , Sydney , New South Wales , Australia
  • 4 Disability Innovation Institute , University of New South Wales , Sydney , New South Wales , Australia
  • 5 Self Advocacy Sydney , Sydney , New South Wales , Australia
  • Correspondence to Dr Manjekah Dunn, Paediatrics & Child Health, University of New South Wales Medicine & Health, Sydney, New South Wales, Australia; manjekah.dunn{at}

Objective To identify factors acting as barriers or enablers to the process of healthcare consent for people with intellectual disability and to understand how to make this process equitable and accessible.

Data sources Databases: Embase, MEDLINE, PsychINFO, PubMed, SCOPUS, Web of Science and CINAHL. Additional articles were obtained from an ancestral search and hand-searching three journals.

Eligibility criteria Peer-reviewed original research about the consent process for healthcare interventions, published after 1990, involving adult participants with intellectual disability.

Synthesis of results Inductive thematic analysis was used to identify factors affecting informed consent. The findings were reviewed by co-researchers with intellectual disability to ensure they reflected lived experiences, and an easy read summary was created.

Results Twenty-three studies were included (1999 to 2020), with a mix of qualitative (n=14), quantitative (n=6) and mixed-methods (n=3) studies. Participant numbers ranged from 9 to 604 people (median 21) and included people with intellectual disability, health professionals, carers and support people, and others working with people with intellectual disability. Six themes were identified: (1) health professionals’ attitudes and lack of education, (2) inadequate accessible health information, (3) involvement of support people, (4) systemic constraints, (5) person-centred informed consent and (6) effective communication between health professionals and patients. Themes were barriers (themes 1, 2 and 4), enablers (themes 5 and 6) or both (theme 3).

Conclusions Multiple reasons contribute to poor consent practices for people with intellectual disability in current health systems. Recommendations include addressing health professionals’ attitudes and lack of education in informed consent with clinician training, the co-production of accessible information resources and further inclusive research into informed consent for people with intellectual disability.

PROSPERO registration CRD42021290548.

  • Decision making
  • Healthcare quality improvement
  • Patient-centred care
  • Quality improvement
  • Standards of care

Data availability statement

Data are available upon reasonable request. Additional data and materials such as data collection forms, data extraction and analysis templates and QualSyst assessment data can be obtained by contacting the corresponding author.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: .

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What is already known on this topic

People with intellectual disability are frequently excluded from decision-making processes and not provided equal opportunity for informed consent, despite protections outlined in the United Nations Convention on the Rights of Persons with Disabilities.

People with intellectual disability have the capacity and desire to make informed medical decisions, which can improve their well-being, health satisfaction and health outcomes.

What this review study adds

Health professionals lack adequate training in valid informed consent and making reasonable adjustments for people with intellectual disability, and continue to perpetuate assumptions of incapacity.

Health information provided to people with intellectual disability is often inaccessible and insufficient for them to make informed decisions about healthcare.

The role of support people, systemic constraints, a person-centred approach and ineffective healthcare communication also affect informed consent.

How this review might affect research, practice or policy

Health professionals need additional training on how to provide a valid informed consent process for people with intellectual disability, specifically in using accessible health information, making reasonable adjustments (e.g., longer/multiple appointments, options of a support person attending or not, using plain English), involving the individual in discussions, and communicating effectively with them.

Inclusive research is needed to hear the voices and opinions of people with intellectual disability about healthcare decision-making and about informed consent practices in specific healthcare settings.

Approximately 1% of the world’s population have intellectual disability. 1 Intellectual disability is medically defined as a group of neurodevelopmental conditions beginning in childhood, with below average cognitive functioning and adaptive behaviour, including limitations in conceptual, social and practical skills. 2 People with intellectual disability prefer an alternative strength-based definition, reflected in the comment by Robert Strike OAM (Order of Australia Medal): ‘We can learn if the way of teaching matches how the person learns’, 3 reinforcing the importance of providing information tailored to the needs of a person with intellectual disability. A diagnosis of intellectual disability is associated with significant disparities in health outcomes. 4–7 Person-centred decision-making and better communication have been shown to improve patient satisfaction, 8 9 the physician–patient relationship 10 and overall health outcomes 11 for the wider population. Ensuring people with intellectual disability experience informed decision-making and accessible healthcare can help address the ongoing health disparities and facilitate equal access to healthcare.

Bodily autonomy is an individual’s power and agency to make decisions about their own body. 12 Informed consent for healthcare enables a person to practice bodily autonomy and is protected, for example, by the National Safety and Quality Health Service Standards (Australia), 13 Mental Capacity Act (UK) 14 and the Joint Commission Standards (USA). 15 In this article, we define informed consent according to three requirements: (1) the person is provided with information they understand, (2) the decision is free of coercion and (3) the person must have capacity. 16 For informed consent to be valid, this process must be suited to the individual’s needs so that they can understand and communicate effectively. Capacity is the ability to give informed consent for a medical intervention, 17 18 and the Mental Capacity Act outlines that ‘a person must be assumed to have capacity unless it is established that he lacks capacity’ and that incapacity can only be established if ‘all practicable steps’ to support capacity have been attempted without success. 14 These assumptions of capacity are also decision-specific, meaning an individual’s ability to consent can change depending on the situation, the choice itself and other factors. 17

Systemic issues with healthcare delivery systems have resulted in access barriers for people with intellectual disability, 19 despite the disability discrimination legislation in many countries who are signatories to the United Nations (UN) Convention on the Rights of Persons with Disabilities. 20 Patients with intellectual disability are not provided the reasonable adjustments that would enable them to give informed consent for medical procedures or interventions, 21 22 despite evidence that many people with intellectual disability have both the capacity and the desire to make their own healthcare decisions. 21 23

To support people with intellectual disability to make independent health decisions, an equitable and accessible informed consent process is needed. 24 However, current health systems have consistently failed to provide this. 21 25 To address this gap, we must first understand the factors that contribute to inequitable and inaccessible consent. To the best of our knowledge, the only current review of informed consent for people with intellectual disability is an integrative review by Goldsmith et al . 26 Many of the included articles focused on assessment of capacity 27–29 and research consent. 30–32 The review’s conclusion supported the functional approach to assess capacity, with minimal focus on how the informed consent processes can be improved. More recently, there has been a move towards ensuring that the consent process is accessible for all individuals, including elderly patients 33 and people with aphasia. 34 However, there remains a paucity of literature about the informed consent process for people with intellectual disability, with no systematic reviews summarising the factors influencing the healthcare consent process for people with intellectual disability.

To identify barriers to and enablers of the informed healthcare consent process for people with intellectual disability, and to understand how this can be made equitable and accessible.

A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) systematic literature review protocol. 35 The PRISMA 2020 checklist 36 and ENhancing Transparency in REporting the synthesis of Qualitative research (ENTREQ) reporting guidelines were also followed. 37 The full study protocol is included in online supplemental appendix 1 .

Supplemental material

No patients or members of the public were involved in this research for this manuscript.

Search strategy

A search strategy was developed to identify articles about intellectual disability, consent and healthcare interventions, described in online supplemental appendix 2 . Multiple databases were searched for articles published between January 1990 to January 2022 (Embase, MEDLINE, PsychINFO, PubMed, SCOPUS, Web of Science and CINAHL). These databases include healthcare and psychology databases that best capture relevant literature on this topic, including medical, nursing, social sciences and bioethical literature. The search was limited to studies published from 1990 as understandings of consent have changed since then. 38 39 This yielded 4853 unique papers which were imported into Covidence, a specialised programme for conducting systematic reviews. 40

Study selection

Citation screening by abstract and titles was completed by two independent researchers (MD and EEP). Included articles had to:

Examine the informed consent process for a healthcare intervention for people with intellectual disability.

Have collected more than 50% of its data from relevant stakeholders, including adults with intellectual disability, families or carers of a person with intellectual disability, and professionals who engage with people with intellectual disability.

Report empirical data from primary research methodology.

Be published in a peer-reviewed journal after January 1990.

Be available in English.

Full text screening was completed by two independent researchers (MD and EEP). Articles were excluded if consent was only briefly discussed or if it focused on consent for research, capacity assessment, or participant knowledge or comprehension. Any conflicts were resolved through discussion with an independent third researcher (IS).

Additional studies were identified through an ancestral search and by hand-searching three major journals relevant to intellectual disability research. Journals were selected if they had published more than one included article for this review or in previous literature reviews conducted by the research team.

Quality assessment

Two independent researchers (MD and IS) assessed study quality with the QualSyst tool, 41 which can assess both qualitative and quantitative research papers. After evaluating the distribution of scores, a threshold value of 55% was used, as suggested by QualSyst 41 to exclude poor-quality studies but capture enough studies overall. Any conflicts between the quality assessment scores were resolved by a third researcher (EEP). For mixed-method studies, both qualitative and quantitative quality scores were calculated, and the higher value used.

Data collection

Two independent researchers (MD and JH) reviewed each study and extracted relevant details, including study size, participant demographics, year, country of publication, study design, data analysis and major outcomes reported. Researchers used standardised data collection forms designed, with input from senior researchers with expertise in qualitative research (IS and EEP), to extract data relevant to the review’s research aims. The form was piloted on one study, and a second iteration made based on feedback. These forms captured data on study design, methods, participants, any factors affecting the process of informed consent and study limitations. Data included descriptions and paragraphs outlining key findings, the healthcare context, verbatim participant quotes and any quantitative analyses or statistics. Missing or unclear data were noted.

Data analysis

A pilot literature search showed significant heterogeneity in methodology of studies, limiting the applicability of traditional quantitative analysis (ie, meta-analysis). Instead, inductive thematic analysis was chosen as an alternative methodology 42 43 that has been used in recent systematic reviews examining barriers and enablers of other health processes. 44 45 The six-phase approach described by Braun and Clarke was used. 46 47 A researcher (MD) independently coded the extracted data of each study line-by-line, with subsequent data grouped into pre-existing codes or new concepts when necessary. Codes were reviewed iteratively and grouped into categories, subthemes and themes framed around the research question. Another independent researcher (JH) collated and analysed the data on study demographics, methods and limitations. The themes were reviewed by two senior researchers (EEP and IS).

Qualitative methods of effect size calculations have been described in the literature, 48 49 which was captured in this review by the number of studies that identified each subtheme, with an assigned frequency rating to compare their relative significance. Subthemes were given a frequency rating of A, B, C or D if they were identified by >10, 7–9, 4–6 or <3 articles, respectively. The overall significance of each theme was estimated by the number of studies that mentioned it and the GRADE framework, a stepwise approach to quality assessment using a four-tier rating system. Each study was evaluated for risk of bias, inconsistency, indirectness, imprecision and publication bias. 50 51 Study sensitivity was assessed by counting the number of distinct subthemes included. 52 The quality of findings was designated high, moderate or low depending on the frequency ratings, the QualSyst score and the GRADE scores of studies supporting the finding. Finally, the relative contributions of each study were evaluated by the number of subthemes described, guided by previously reported methods for qualitative reviews. 52


The findings were reviewed by two co-researchers with intellectual disability (JL and SS), with over 30 years combined experience as members and employees of a self-advocacy organisation. Guidance on the findings and an easy read summary was produced in line with best-practice inclusive research 53 54 over multiple discussions. Input from two health professional researchers (MD and EEP) provided data triangulation and sense-checking of findings.

Twenty-three articles were identified ( figure 1 ): 14 qualitative, 6 quantitative and 3 mixed-methods. Two papers included the same population of study participants: McCarthy 55 and McCarthy, 56 but had different research questions. Fovargue et al 57 was excluded due to a quality score of 35%. Common quality limitations were a lack of verification procedures to establish credibility and limited researcher reflexivity. No studies were excluded due to language requirements (as all were in English) or age restrictions (all studies had majority adult participants).

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PRISMA 2020 flowchart for the systematic review. 36

Studies were published from 1999 to 2020 and involved participant populations from the UK (n=18), USA (n=3), Sweden (n=1) and Ireland (n=1). Participant numbers ranged from 9 to 604 (median 21), and participants included people with intellectual disability (n=817), health professionals (n=272), carers and support people (n=48), and other professionals that work with people with intellectual disability (n=137, community service agency directors, social workers, administrative staff and care home staff). Ages of participants ranged from 8 to 84 years, though only Aman et al 58 included participants <18 years of age. This study was included as the article states very few children were included. Studies examined consent in different contexts, including contraception and sexual health (6/23 articles), 58–60 medications (5/23 articles), 58–62 emergency healthcare, 63 cervical screening, 64 community referrals, 58–61 65 mental health, 66 hydrotherapy, 64 blood collection 67 and broad decision-making consent without a specific context. 65 68–71 A detailed breakdown of each study is included in online supplemental appendix 3 .

Six major themes were identified from the studies, summarised in figure 2 . An overview of included studies showing study sensitivity, effect size, QualSyst and GRADE scores is given in online supplemental appendix 4 . Studies with higher QualSyst and GRADE scores contributed more to this review’s findings and tended to include more subthemes; specifically, Rogers et al , 66 Sowney and Barr, 63 Höglund and Larsson, 72 and McCarthy 55 and McCarthy. 56 Figure 3 gives the easy read version of theme 1, with the full easy read summary in online supplemental appendix 5 .

Summary of the identified six themes and subthemes.

Theme 1 of the easy read summary.

Theme 1—Health professionals’ attitudes and lack of education about informed consent

Health professionals’ attitudes and practices were frequently (18/21) identified as factors affecting the informed consent process, with substantial evidence supporting this theme. Studies noted the lack of training for health professionals in supporting informed consent for people with intellectual disability, their desire for further education, and stereotypes and discrimination perpetuated by health professionals.

Lack of health professional education on informed consent and disability discrimination legislation

Multiple studies reported inconsistent informed consent practices, for various reasons: some reported that health professionals ‘forgot’ to or ‘did not realise consent was necessary’, 63 73 but inconsistent consent practices were also attributed to healthcare providers’ unfamiliarity with consent guidelines and poor education on this topic. Carlson et al 73 reported that only 44% of general practitioners (GPs) were aware of consent guidelines, and there was the misconception that consent was unnecessary for people with intellectual disability. Similarly, studies of psychologists 66 and nurses 63 found that many were unfamiliar with their obligations to obtain consent, despite the existence of anti-discrimination legislation. People with intellectual disability describe feeling discriminated against by health professionals, reflected in comments such as ‘I can tell, my doctor just thinks I’m stupid – I'm nothing to him’. 74 Poor consent practices by health professionals were observed in Goldsmith et al , 67 while health professionals surveyed by McCarthy 56 were unaware of their responsibility to provide accessible health information to women with intellectual disability. Improving health professional education and training was suggested by multiple studies as a way to remove this barrier. 63 65–67 69 73

Lack of training on best practices for health professions caring for people with intellectual disability

A lack of training in caring for and communicating with people with intellectual disability was also described by midwives, 72 psychologists, 66 nurses, 63 pharmacists 61 and GPs. 56 72 75 Health professionals lacked knowledge about best practice approaches to providing equitable healthcare consent processes through reasonable adjustments such as accessible health information, 56 60 66 longer appointments times, 60 72 simple English 62 67 and flexible approaches to patient needs. 63 72

Health professionals’ stereotyping and assumptions of incapacity

Underlying stereotypes contributed to some health professionals’ (including nurses, 63 GPs 56 and physiotherapists 64 ) belief that people with intellectual disability lack capacity and therefore, do not require opportunities for informed consent. 56 64 In a survey of professionals referring people with intellectual disability to a disability service, the second most common reason for not obtaining consent was ‘patient unable to understand’. 73

Proxy consent as an inappropriate alternative

People with intellectual disability are rarely the final decision-maker in their medical choices, with many health providers seeking proxy consent from carers, support workers and family members, despite its legal invalidity. In McCarthy’s study (2010), 18/23 women with intellectual disability said the decision to start contraception was made by someone else. Many GPs appeared unaware that proxy consent is invalid in the UK. 56 Similar reports came from people with intellectual disability, 55 56 60 64 69 76 health professionals (nurses, doctors, allied health, psychologists), 56 63 64 66 77 support people 64 77 and non-medical professionals, 65 73 and capacity was rarely documented. 56 62 77

Exclusion of people with intellectual disability from decision-making discussions

Studies described instances where health professionals made decisions for their patients with intellectual disability or coerced patients into a choice. 55 72 74 76 77 In Ledger et al 77 , only 62% of women with intellectual disability were involved in the discussion about contraception, and only 38% made the final decision, and others stated in Wiseman and Ferrie 74 : ‘I was not given the opportunity to explore the different options. I was told what one I should take’. Three papers outlined instances where the choices of people with intellectual disability were ignored despite possessing capacity 65 66 69 and when a procedure continued despite them withdrawing consent. 69

Theme 2—Inadequate accessible health information

Lack of accessible health information.

The lack of accessible health information was the most frequently identified subtheme (16/23 studies). Some studies reported that health professionals provided information to carers instead, 60 avoided providing easy read information due to concerns about ‘offending’ patients 75 or only provided verbal information. 56 67 Informed consent was supported when health professionals recognised the importance of providing medical information 64 and when it was provided in an accessible format. 60 Alternative approaches to health information were explored, including virtual reality 68 and in-person education sessions, 59 with varying results. Overall, the need to provide information in different formats tailored to an individual’s communication needs, rather than a ‘one size fits all’ approach, was emphasised by both people with intellectual disability 60 and health professionals. 66

Insufficient information provided

Studies described situations where insufficient information was provided to people with intellectual disability to make informed decisions. For example, some people felt the information from their GP was often too basic to be helpful (Fish et al 60 ) and wanted additional information on consent forms (Rose et al 78 ).

Theme 3—The involvement of support people

Support people (including carers, family members and group home staff) were identified in 11 articles as both enablers of and barriers to informed consent. The antagonistic nature of these findings and lower frequency of subthemes are reflected in the lower quality assessments of evidence.

Support people facilitated communication with health professionals

Some studies reported carers bridging communication barriers with health to support informed consent. 63 64 McCarthy 56 found 21/23 of women with intellectual disability preferred to see doctors with a support person due to perceived benefits: ‘Sometimes I don’t understand it, so they have to explain it to my carer, so they can explain it to me easier’. Most GPs in this study (93%) also agreed that support people aided communication.

Support people helped people with intellectual disability make decisions

By advocating for people with intellectual disability, carers encouraged decision-making, 64 74 provided health information, 74 77 emotional support 76 and assisted with reading or remembering health information. 55 58 76 Some people with intellectual disability explicitly appreciated their support person’s involvement, 60 such as in McCarthy’s 55 study where 18/23 participants felt supported and safer when a support person was involved.

Support people impeded individual autonomy

The study by Wiseman and Ferrie 74 found that while younger participants with intellectual disability felt family members empowered their decision-making, older women felt family members impaired their ability to give informed consent. This was reflected in interviews with carers who questioned the capacity of the person with intellectual disability they supported and stated they would guide them to pick the ‘best choice’ or even over-ride their choices. 64 Studies of psychologists and community service directors described instances where the decision of family or carers was prioritised over the wishes of the person with intellectual disability. 65 66 Some women with intellectual disability in McCarthy’s studies (2010, 2009) 55 56 appeared to have been coerced into using contraception by parental pressures or fear of losing group home support.

Theme 4—Systemic constraints within healthcare systems

Time restraints affect informed consent and accessible healthcare.

Resource limitations create time constraints that impair the consent process and have been identified as a barrier by psychologists, 66 GPs, 56 hospital nurses 63 and community disability workers. 73 Rogers et al 66 highlighted that a personalised approach that could improve informed decision-making is restricted by inflexible medical models. Only two studies described flexible patient-centred approaches to consent. 60 72 A survey of primary care practices in 2007 reported that most did not modify their cervical screening information for patients with intellectual disability because it was not practical. 75

Inflexible models of consent

Both people with intellectual disability 76 and health professionals 66 recognised that consent is traditionally obtained through one-off interactions prior to an intervention. Yet, for people with intellectual disability, consent should ideally be an ongoing process that begins before an appointment and continues between subsequent ones. Other studies have tended to describe one-off interactions where decision-making was not revisited at subsequent appointments. 56 60 72 76

Lack of systemic supports

In one survey, self-advocates highlighted a lack of information on medication for people with intellectual disability and suggested a telephone helpline and a centralised source of information to support consent. 60 Health professionals also want greater systemic support, such as a health professional specialised in intellectual disability care to support other staff, 72 or a pharmacist specifically to help patients with intellectual disability. 61 Studies highlighted a lack of guidelines about healthcare needs of people with intellectual disabilities such as contraceptive counselling 72 or primary care. 75

Theme 5—Person-centred informed consent

Ten studies identified factors related to a person-centred approach to informed consent, grouped below into three subthemes. Health professionals should tailor their practice when obtaining informed consent from people with intellectual disability by considering how these subthemes relate to the individual. Each subtheme was described five times in the literature with a relative frequency rating of ‘C’, contributing to overall lower quality scores.

Previous experience with decision-making

Arscott et al 71 found that the ability of people with intellectual disability to consent changed with their verbal and memory skills and in different clinical vignettes, supporting the view of ‘functional’ capacity specific to the context of the medical decision. Although previous experiences with decision-making did not influence informed consent in this paper, other studies suggest that people with intellectual disability accustomed to independent decision-making were more able to make informed medical decisions, 66 70 and those who live independently were more likely to make independent healthcare decisions. 56 Health professionals should be aware that their patients with intellectual disability will have variable experience with decision-making and provide individualised support to meet their needs.

Variable awareness about healthcare rights

Consent processes should be tailored to the health literacy of patients, including emphasising available choices and the option to refuse treatment. In some studies, medical decisions were not presented to people with intellectual disability as a choice, 64 and people with intellectual disability were not informed of their legal right to accessible health information. 56

Power differences and acquiescence

Acquiescence by people with intellectual disability due to common and repeated experiences of trauma—that is, their tendency to agree with suggestions made by carers and health professionals, often to avoid upsetting others—was identified as an ongoing barrier. In McCarthy’s (2009) interviews with women with intellectual disability, some participants implicitly rejected the idea that they might make their own healthcare decisions: ‘They’re the carers, they have responsibility for me’. Others appeared to have made decisions to appease their carers: ‘I have the jab (contraceptive injection) so I can’t be blamed for getting pregnant’. 55 Two studies highlighted that health professionals need to be mindful of power imbalances when discussing consent with people with intellectual disability to ensure the choices are truly autonomous. 61 66

Theme 6—Effective communication between health professionals and patients

Implementation of reasonable adjustments for verbal and written information.

Simple language was always preferred by people with intellectual disability. 60 67 Other communication aids used in decision-making included repetition, short sentences, models, pictures and easy read brochures. 72 Another reasonable adjustment is providing the opportunity to ask questions, which women with intellectual disability in McCarthy’s (2009) study reported did not occur. 55

Tailored communication methods including non-verbal communication

Midwives noted that continuity of care allows them to develop rapport and understand the communication preferences of people with intellectual disability. 72 This is not always possible; for emergency nurses, the lack of background information about patients with intellectual disability made it challenging to understand their communication preferences. 63 The use of non-verbal communication, such as body language, was noted as underutilised 62 66 and people with intellectual disability supported the use of hearing loops, braille and sign language. 60

To the best of our knowledge, this is the first systematic review investigating the barriers and enablers of the informed consent process for healthcare procedures for people with intellectual disability. The integrative review by Goldsmith et al 26 examined capacity assessment and shares only three articles with this systematic review. 69 71 73 Since the 2000s, there has been a paradigm shift in which capacity is no longer considered a fixed ability that only some individuals possess 38 39 but instead as ‘functional’: a flexible ability that changes over time and in different contexts, 79 reflected in Goldsmith’s review. An individual’s capacity can be supported through various measures, including how information is communicated and how the decision-making process is approached. 18 80 By recognising the barriers and enablers identified in this review, physicians can help ensure the consent process for their patients with intellectual disability is both valid and truly informed. This review has highlighted the problems of inaccessible health information, insufficient clinical education on how to make reasonable adjustments and lack of person-centred trauma-informed care.


Health professionals require training in the informed consent process for people with intellectual disability, particularly in effective and respectful communication, reasonable adjustments and trauma-informed care. Reasonable adjustments include offering longer or multiple appointments, using accessible resources (such as easy read information or shared decision-making tools) and allowing patient choices (such as to record a consultation or involve a support person). Co-researchers reported that many people with intellectual disability prefer to go without a support person because they find it difficult to challenge their decisions and feel ignored if the health professional only talks to the support person. People with intellectual disability also feel they cannot seek second opinions before making medical decisions or feel pressured to provide consent, raising the possibility of coercion. These experiences contribute to healthcare trauma. Co-researchers raised the importance of building rapport with the person with intellectual disability and of making reasonable adjustments, such as actively advocating for the person’s autonomy, clearly stating all options including the choice to refuse treatment, providing opportunities to contribute to discussions and multiple appointments to ask questions and understand information. They felt that without these efforts to support consent, health professionals can reinforce traumatic healthcare experiences for people with intellectual disability. Co-researchers noted instances where choices were made by doctors without discussion and where they were only given a choice after requesting one and expressed concern that these barriers are greater for those with higher support needs.

Co-researchers showed how these experiences contributed to mistrust of health professionals and poorer health outcomes. In one situation, a co-researcher was not informed of a medication’s withdrawal effects, resulting in significant side-effects when it was ceased. Many people with intellectual disability describe a poor relationship with their health professionals, finding it difficult to trust health information provided due to previous traumatic experiences of disrespect, coercion, lack of choice and inadequate support. Many feel they cannot speak up due to the power imbalance and fear of retaliation. Poor consent practices and lack of reasonable adjustments directly harm therapeutic alliances by reducing trust, contribute to healthcare trauma and lead to poorer health outcomes for people with intellectual disability.

Additional education and training for health professionals is urgently needed in the areas of informed consent, reasonable adjustments and effective communication with people with intellectual disability. The experiences of health professionals within the research team confirmed that there is limited training in providing high-quality healthcare for people with intellectual disability, including reasonable adjustments and accessible health information. Co-researchers also suggested that education should be provided to carers and support people to help them better advocate for people with intellectual disability.

Health information should be provided in a multimodal format, including written easy read information. Many countries have regulation protecting the right to accessible health information and communication support to make an informed choice, such as UK’s Accessible Information Standard, 81 and Australia’s Charter of Health Care Rights, 24 yet these are rarely observed. Steps to facilitate this include routinely asking patients about information requirements, system alerts for an individual’s needs or routinely providing reasonable adjustments. 82 Co-researchers agreed that there is a lack of accessible health information, particularly about medications, and that diagrams and illustrations are underutilised. There is a critical need for more inclusive and accessible resources to help health professionals support informed consent in a safe and high-quality health system. These resources should be created through methods of inclusive research, such as co-production, actively involving people with intellectual disability in the planning, creation, and feedback process. 53

Strengths and limitations

This systematic review involved two co-researchers with intellectual disability in sense-checking findings and co-creating the easy read summary. Two co-authors who are health professionals provided additional sense-checking of findings from a different stakeholder perspective. In future research, this could be extended by involving people with intellectual disability in the design and planning of the study as per recommendations for best-practice inclusive research. 53 83

The current literature is limited by low use of inclusive research practices in research involving people with intellectual disability, increasing vulnerability to external biases (eg, inaccessible questionnaires, involvement of carers in data collection, overcompliance or acquiescence and absence of researcher reflexivity). Advisory groups or co-research with people with intellectual disability were only used in five studies. 58 60 68 74 76 Other limitations include unclear selection criteria, low sample sizes, missing data, using gatekeepers in patient selection and predominance of UK-based studies—increasing the risk of bias and reducing transferability. Nine studies (out of 15 involving people with intellectual disability) explicitly excluded those with severe or profound intellectual disability, reflecting a selection bias; only one study specifically focused on people with intellectual disability with higher support needs. Studies were limited to a few healthcare contexts, with a focus on consent about sexual health, contraception and medications.

The heterogeneity and qualitative nature of studies made it challenging to apply traditional meta-analysis. However, to promote consistency in qualitative research, the PRISMA and ENTREQ guidelines were followed. 36 37 Although no meta-analyses occurred, the duplication of study populations in McCarthy 2009 and 2010 likely contributed to increased significance of findings reported in both studies. Most included studies (13/23) were published over 10 years ago, reducing the current relevance of this review’s findings. Nonetheless, the major findings reflect underlying systemic issues within the health system, which are unlikely to have been resolved since the articles were published, as the just-released final report of the Australian Royal Commission into Violence, Abuse, Neglect and Exploitation of People with Disability highlights. 84 There is an urgent need for more inclusive studies to explore the recommendations and preferences of people with intellectual disability about healthcare choices.

Informed consent processes for people with intellectual disability should include accessible information and reasonable adjustments, be tailored to individuals’ needs and comply with consent and disability legislation. Resources, guidelines and healthcare education are needed and should cover how to involve carers and support people, address systemic healthcare problems, promote a person-centred approach and ensure effective communication. These resources and future research must use principles of inclusive co-production—involving people with intellectual disability at all stages. Additionally, research is needed on people with higher support needs and in specific contexts where informed consent is vital but under-researched, such as cancer screening, palliative care, prenatal and newborn screening, surgical procedures, genetic medicine and advanced therapeutics such as gene-based therapies.

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

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Contributors MD, EEP and IS conceived the idea for the systematic review. MD drafted the search strategy which was refined by EEP and IS. MD and EEP completed article screening. MD and IS completed quality assessments of included articles. MD and JH completed data extraction. MD drafted the original manuscript. JL and SS were co-researchers who sense-checked findings and were consulted to formulate dissemination plans. JL and SS co-produced the easy read summary with MD, CM, JH, EEP and IS. MD, JLS, EEP and IS reviewed manuscript wording. All authors critically reviewed the manuscript and approved it for publication. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. MD is the guarantor responsible for the overall content of this manuscript.

Funding This systematic literature review was funded by the National Health & Medical Research Council (NHMRC), Targeted Call for Research (TCR) into Improving health of people with intellectual disability. Research grant title "GeneEQUAL: equitable and accessible genomic healthcare for people with intellectual disability". NHMRC application ID: 2022/GNT2015753.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Qualitative data analysis is a process of gathering, structuring and interpreting qualitative data to understand what it represents. Qualitative data is non-numerical and unstructured.

Step 1a: Data collection methods and techniques in qualitative research: interviews and focus groups Step 1 is collecting the data that you will need for the analysis. If you are not performing any interviews or focus groups to gather data, then you can skip this step.

Data analysis in qualitative research is defined as the process of systematically searching and arranging the interview transcripts, observation notes, or other non-textual materials that the researcher accumulates to increase the understanding of the phenomenon. 7 The process of analysing qualitative data predominantly involves coding or catego...

In conclusion, analyzing qualitative data is a complex yet rewarding process that can provide valuable insights for effective research. By following this step-by-step guide, you'll be equipped with the necessary tools and techniques to conduct a thorough analysis of your qualitative data and draw meaningful conclusions from it.

Qualitative research is a type of research methodology that focuses on exploring and understanding people's beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data.

In the previous paper, 1 we outlined 3 commonly used methodologies: ethnography 2, grounded theory 3, and phenomenology. 4 Briefly, ethnography involves researchers using direct observation to study participants in their "real life" environment, sometimes over extended periods.

Data analysis in qualitative research is an iterative and complex process. The focus of analysis is Data Analysis in Qualitative Research : Indian Journal of Continuing Nursing Education You may be trying to access this site from a secured browser on the server. Please enable scripts and reload this page. Log in or Register

Qualitative data analysis can be conducted through the following three steps: Step 1: Developing and Applying Codes. Coding can be explained as categorization of data. A 'code' can be a word or a short phrase that represents a theme or an idea. All codes need to be assigned meaningful titles.

After reading this chapter, you should be able to: • recognise the major styles of qualitative data analysis • describe common processes involved with coding qualitative data • clarify...

View. Show abstract. ... As postulated by qualitative researchers, Data Logging, Anecdotes, Vignettes, Data Coding, and Thematic Analysis are the five steps in analyzing qualitative data. However ...

Here are 5 steps to analysing qualitative data: 1. Define your research questions to guide the analysis. 2. Collect qualitative data from user feedback, NPS follow-up questions, interviews, and open-ended questions. 3. Organize and categorize qualitative data to detect patterns and group them more easily. 4.

Higher degree research students in education are largely underprepared for understanding or employing statistical data analysis methods. This is despite their need to read literature in their field which will indubitably include such research. This weakness may result in students choosing to use qualitative or interpretivist methodologies, even though education data are highly complex ...

Massachusetts Institute of Technology This is to certify that Carlos Alejandro Flores Tapia has successfully completed Qualitative Research Methods: Data Coding and Analysis Nov. 1, 2022 - Oct. 16, 2023. Susan Silbey. Leon and Anne Goldberg Professor of Humanities, Sociology and Anthropology. Massachusetts Institute of Technology. Chris Capozzola.

The author of this research suggests a technique for detecting dysarthria that takes use of the acoustic characteristics of speech. ... The category of dysarthria is analyzed using a number of different Exploratory Data Analysis (EDA) methods, such as the pair plot, which is a bivariate data analysis method, and t-Distributed Stochastic ...

Eligibility criteria Peer-reviewed original research about the consent process for healthcare interventions, published after 1990, involving adult participants with intellectual disability. ... data extraction and analysis templates and QualSyst assessment data can be obtained by contacting the corresponding author. ... Qualitative methods of ...

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An Introduction to Krabi Posted by sasha on Mar 14, 2016 in Travel

Located in the south of Thailand on the Andaman Sea, the province of Krabi (กระบี่ grà-bèe ) has become one of the most popular tourist destinations in Thailand. With crystal clear waters, white sandy beaches, epic limestone karst peaks, and world class scuba diving and rock climbing, it’s no surprise that visitors are flocking here in greater numbers every year. There’s a lot to see and do here and many different places you can stay. Here’s a short introduction to this beautiful corner of Thailand, highlighting some of the main areas you’ll probably be interested in visiting:

While there isn’t much for tourists on offer in the town, chances are you’ll end up here at some point by necessity. The airport is here and most long-distance buses stop here before transferring you to one of the beach towns or islands. Should you end up choosing to spend a night here for logistical reasons, you could take a quick trip out to the Tiger Cave Temple (วัดถ้ำเสือ). Scale all 1,237 steps to the top to admire the large gold Buddha statue and enjoy the panoramic views of Krabi. The name comes from what appear to be tiger paw prints in the stone.

Phi Phi Islands (หมู่เกาะพีพี)

Ko Phi Phi Don

The view from the hill on Ko Phi Phi Don.

A small archipelago of six islands, these have become a top destination in Thailand. This is thanks in large part to their appearance in the 2000 film The Beach , based on the cult classic backpacker novel. Every day, tons of tour boats pull up to Maya Bay where the film was shot. If you don’t want to share the beach with hundreds of people, consider visiting very early or later on in the day. This is located on Ko Phi Phi Leh, smaller of the two main islands and uninhabited save for a few security guards and bird’s nest harvesters.

The largest of the islands, Ko Phi Phi Don, is the only one that is populated, and this is where tourists stay as well. Much of the island was destroyed in a tsunami back in 2004, but redevelopment has been quick – perhaps too quick, according to some. The natural beauty of this island is its top draw, but many worry that this will be destroyed sooner than later if tourist numbers are not capped. Let’s hope that a more sustainable tourism industry can develop here so that people can enjoy these stunning islands for generations to come.

Ko Lanta (เกาะลันตา)

Whereas Phi Phi may be known as somewhat of a party island, Ko Lanta is a much more chilled out place. Days here can be spent lounging on one of the island’s many beaches, snorkeling, scuba diving, kayaking, fishing, or even trekking. The island is also home to a protected mangrove forest, waterfalls, caves, and a national park should you feel like doing some exploring. There are also plenty of options for yoga/meditation, Muay Thai, and even a few cooking classes. For a dose of culture, check out the Old Town, a mixture of Chinese merchants, Thai fishing families, and an ancient Sea Gypsy community. If you’re visiting in March, be sure to take part in the Laanta Lanta Festival there, with food, games, and live music.

Ao Nang (อ่าวนาง)

Ao Nang

A classic beach town.

This is the most accessible and most developed beach town in the province, and it’s also where you can catch a long-tail boat to some of the other popular destinations. The beachfront road is full of hotels, shops, restaurants, bars, and travel agents, ensuring you don’t ever have to go far. While the beach here isn’t the best, there are plenty of excellent ones nearby that make for easy day-trips.

Rai Leh (อ่าวไร่เล)

Rai Leh rock climbing

Great climbing abounds here.

Although it’s located on the mainland, Rai Leh sure feels like an island. As it’s surrounded by the ocean and mountains, it’s only accessible by boat. From Ao Nang, it’s a short 10-15 minute ride over depending on where you’re staying. This area features world-class rock climbing, boasting upwards of 700 bolted routes for all levels.

Rai Leh phallus cave

The phallus cave at Rai Leh.

There are four main beaches that make up Rai Leh. Phra Nang Cave Beach (หาดถ้ำพระนาง) features one of the most interesting sights in all of Krabi – a cave that local fishermen believe to be the home of a mythical sea princess. They make offerings to her to bring success and keep them from danger, and the offerings just so happen to be large, colorful, wooden phalluses. The other beaches are Rai Leh East, West, and Ton Sai.

Rai Leh view

Not a bad view…

We’ll be posting more detailed guides to a few of these places in Krabi over the next few weeks, so subscribe to the blog and our YouTube channel to make sure you don’t miss out!

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About the Author: sasha

Sasha is an English teacher, writer, photographer, and videographer from the great state of Michigan. Upon graduating from Michigan State University, he moved to China and spent 5+ years living, working, studying, and traveling there. He also studied Indonesian Language & Culture in Bali for a year. He and his wife run the travel blog Grateful Gypsies, and they're currently trying the digital nomad lifestyle across Latin America.


The Risky Words That Might Make School Admissions Suspect AI Wrote Your Essay

W hen the ChatGPT-mania kicked off last year, the first uproar emerged from the academia. Teachers were worried that students now had a potent tool to cheat on their assignments, and like clockwork, multiple AI plagiarism detectors popped up with variable degrees of accuracy. Students were worried that these AI plagiarism detectors could get them in trouble even if the error rate were low. Experts, on the other hand, opined that one needs to rely on intuition and natural language skills to detect signs of AI by looking for signatures such as repetitive phrases, an out-of-character use of words, a uniformly monotonous flow, and being more verbose than is needed in a regular human conversation.

No method is infallible, but the risk avenues keep spiraling out of control while the underlying large language models get even more nuanced in their word regurgitation skill. Among those avenues is the all-too-important essay required for college applications. According to a Forbes report, students are using AI tools to write their school and college essays, but academics and people on the admission committee have developed a knack for spotting AI word signatures. For example, one of the words that seems to pop up frequently in essays is "tapestry," which, honestly, is rarely ever used or heard in a conversation or even text-based material, save for poetry or works of English literature.

"I no longer believe there's a way to innocently use the word 'tapestry' in an essay; if the word 'tapestry' appears, it was generated by ChatGPT," one of the experts who edit college essays told Forbes. Unfortunately, he also warns that in the rare scenarios where an applicant inadvertently, and with good intentions, ends up using the word, they might face rejection by the admission committee over perceived plagiarism.

Read more: Major PC Monitor Brands Ranked Worst To Best

What To Avoid?

The Forbes report compiles responses from over 20 educational institutions, including top-tier names like Harvard and Princeton, about how exactly they are factoring AI while handling applications. While the institutions didn't provide any concrete answers in terms of a proper policy, members handling the task hinted that spotting AI usage in essays is pretty easy, both in terms of specific word selection, which they described as "thin, hollow, and flat," as well as the tone. Some independent editors have created an entire glossary of words and phrases that she often sees in essays and which she tweaks to give "human vibes" to the essays.

Some of the code-red AI signatures, which don't even require AI detection tools to spot them, include:

  • "leadership prowess"
  • "stems from a deep-seated passion"
  • "aligns seamlessly with my aspirations"
  • "commitment to continuous improvement and innovation"
  • "entrepreneurial/educational journey"

These are just a few giveaways of AI involvement. Moreover, they can change and may not even be relevant soon as more sophisticated models with better natural language capabilities arrive on the scene. Plus, people from non-academic domains appear to have established their own framework to detect AI-generated work. "If you have enough text, a really easy cue is the word 'the' occurs too many times," Google Brain scientist Daphne Ippolito said to MIT Technology Review . 

Ippolito also pointed out that generative AI models rarely make typos, which is a reverse-engineered way to assess if a piece of writing is the result of some AI tool. "A typo in the text is actually a really good indicator that it was human written," she notes. But it takes practice to be good at identifying the pattern, especially at reading aspects like unerring fluency and the lack of spontaneity.

It's All Still A Big Mess

An AI text generator is essentially a glorified parrot, which is exceptional at echoing but not so much at delivering surprises. Indeed, drafting an invitation email or shooting a message to your pals might seem like you're following a script, yet there's a whimsical flair to our human way of chatting that's quite the trick to nail down for an AI. Despite all the advancements that Google has made with its PaLM 2 or whatever it is that Meta or OpenAI continue to achieve with Llama 2 or GPT-4, it is simply not worth the risk to be using AI for college, work, or any other high-stakes task. 

One of the biggest reasons to avoid relying squarely on AI chatbots is their tendency to hallucinate, which is essentially an AI model cooking up an imaginary scenario and serving it as fact. Next, there is always a risk that the work can be flagged down the road, either by a keen human mind or the makers of these AI tools using some proprietary AI fingerprinting tool. There are already tools out there, such as GPTZero, that can spot AI plagiarism. However, those tools are also far from infallible , so there's a tangible risk that even an original work can be flagged as AI-generated garbage.

To avoid such a scenario, the best way is to enable a progress history feature , one that tracks how a piece of work moved ahead, one small at a time. For example, if you are into writing, products like Google Docs and Microsoft Word offer a version history system that essentially saves different versions of an ongoing work every time some change is made. The progress is saved, essentially creating a time-stamped proof of each stage. 

Read the original article on SlashGear .

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Taylor Swift performs at the Melbourne cricket ground: she is wearing a sparkly leotard-type garment with knee-length silver high-heeled boots, and is singing in front of a pink-lit backdrop

Singapore sought exclusivity deal over Taylor Swift concerts in south-east Asia, Thai PM alleges

Srettha Thavisin claims promoter told him Singaporean government offered ‘subsidies’ of $2m-$3m a show

Thailand’s prime minister has claimed that Singapore sought a deal with Taylor Swift to prevent her from playing elsewhere in south-east Asia on her Eras tour.

Srettha Thavisin said the concert promoter AEG had informed him that the Singaporean government offered subsidies of US $2m-$3m (£1.6m-£24m) a show as part of an exclusivity agreement.

Swift is playing six sold-out shows at the 55,000-seat National Stadium in Singapore in March.

“[AEG] didn’t tell me the exact figure but they said the Singapore government offers subsidies of between $2m and $3m,” Srettha said publicly at a business forum in Bangkok. “But the Singaporean government is clever. They told [organisers] not to hold any other shows in [south-east] Asia.”

AEG and the Singapore government did not immediately respond to a request for comment.

Swift’s fans across south-east Asia were bitterly disappointed when it was announced last year that she would skip most of the region and stop only in Singapore during her Eras tour. Even for those with the means to travel to see her, securing tickets was difficult; many fans enlisted family members and friends to register on their behalf and waited for hours in online queues.

In addition to Singapore, Japan and Australia are also included in the tour. Those lucky enough to have secured a ticket for Singapore have planned long and expensive journeys – in some cases involving boat, bus and plane – to see her. South-east Asia is home to many loyal Swift fans, with Quezon City in the Philippines once listed by Spotify as being home to the fifth-biggest number of her listeners in a ranking of global cities.

The Singapore concerts are expected to bring a major boost to the tourism sector, and Swift’s visit has been celebrated by officials. The minister for community, culture and youth, Edwin Tong, said when the tour dates were announced that it was an example of the calibre of events Singapore was targeting “to augment our offerings to Singaporeans and tourists alike”.

Elsewhere in south-east Asia, fans have previously blamed factors ranging from poor infrastructure to political instability and attitudes among conservative Muslim groups for the lack of tour dates.

Many Thai Swifties recall how the singer had to cancel her 2014 concert in Bangkok after the military coup by the former prime minister Prayuth Chan-ocha. In Malaysia, there are fears that it could become harder for foreign artists to perform , after an outcry over a same-sex kiss between members of the 1975 at a concert in July.

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