interview research method psychology

The Interview Method In Psychology

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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Interviews involve a conversation with a purpose, but have some distinct features compared to ordinary conversation, such as being scheduled in advance, having an asymmetry in outcome goals between interviewer and interviewee, and often following a question-answer format.

Interviews are different from questionnaires as they involve social interaction. Unlike questionnaire methods, researchers need training in interviewing (which costs money).

How Do Interviews Work?

Researchers can ask different types of questions, generating different types of data . For example, closed questions provide people with a fixed set of responses, whereas open questions allow people to express what they think in their own words.

The researcher will often record interviews, and the data will be written up as a transcript (a written account of interview questions and answers) which can be analyzed later.

It should be noted that interviews may not be the best method for researching sensitive topics (e.g., truancy in schools, discrimination, etc.) as people may feel more comfortable completing a questionnaire in private.

There are different types of interviews, with a key distinction being the extent of structure. Semi-structured is most common in psychology research. Unstructured interviews have a free-flowing style, while structured interviews involve preset questions asked in a particular order.

Structured Interview

A structured interview is a quantitative research method where the interviewer a set of prepared closed-ended questions in the form of an interview schedule, which he/she reads out exactly as worded.

Interviews schedules have a standardized format, meaning the same questions are asked to each interviewee in the same order (see Fig. 1).

   Figure 1. An example of an interview schedule

The interviewer will not deviate from the interview schedule (except to clarify the meaning of the question) or probe beyond the answers received.  Replies are recorded on a questionnaire, and the order and wording of questions, and sometimes the range of alternative answers, is preset by the researcher.

A structured interview is also known as a formal interview (like a job interview).

• Structured interviews are easy to replicate as a fixed set of closed questions are used, which are easy to quantify – this means it is easy to test for reliability .
• Structured interviews are fairly quick to conduct which means that many interviews can take place within a short amount of time. This means a large sample can be obtained, resulting in the findings being representative and having the ability to be generalized to a large population.

Limitations

• Structured interviews are not flexible. This means new questions cannot be asked impromptu (i.e., during the interview), as an interview schedule must be followed.
• The answers from structured interviews lack detail as only closed questions are asked, which generates quantitative data . This means a researcher won’t know why a person behaves a certain way.

Unstructured Interview

Unstructured interviews do not use any set questions, instead, the interviewer asks open-ended questions based on a specific research topic, and will try to let the interview flow like a natural conversation. The interviewer modifies his or her questions to suit the candidate’s specific experiences.

Unstructured interviews are sometimes referred to as ‘discovery interviews’ and are more like a ‘guided conservation’ than a strictly structured interview. They are sometimes called informal interviews.

Unstructured interviews are most useful in qualitative research to analyze attitudes and values. Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective points of view.

Interviewer Self-Disclosure

Interviewer self-disclosure involves the interviewer revealing personal information or opinions during the research interview. This may increase rapport but risks changing dynamics away from a focus on facilitating the interviewee’s account.

In unstructured interviews, the informal conversational style may deliberately include elements of interviewer self-disclosure, mirroring ordinary conversation dynamics.

Interviewer self-disclosure risks changing the dynamics away from facilitation of interviewee accounts. It should not be ruled out entirely but requires skillful handling informed by reflection.

• An informal interviewing style with some interviewer self-disclosure may increase rapport and participant openness. However, it also increases the chance of the participant converging opinions with the interviewer.
• Complete interviewer neutrality is unlikely. However, excessive informality and self-disclosure risk the interview becoming more of an ordinary conversation and producing consensus accounts.
• Overly personal disclosures could also be seen as irrelevant and intrusive by participants. They may invite increased intimacy on uncomfortable topics.
• The safest approach seems to be to avoid interviewer self-disclosures in most cases. Where an informal style is used, disclosures require careful judgment and substantial interviewing experience.
• If asked for personal opinions during an interview, the interviewer could highlight the defined roles and defer that discussion until after the interview.
• Unstructured interviews are more flexible as questions can be adapted and changed depending on the respondents’ answers. The interview can deviate from the interview schedule.
• Unstructured interviews generate qualitative data through the use of open questions. This allows the respondent to talk in some depth, choosing their own words. This helps the researcher develop a real sense of a person’s understanding of a situation.
• They also have increased validity because it gives the interviewer the opportunity to probe for a deeper understanding, ask for clarification & allow the interviewee to steer the direction of the interview, etc. Interviewers have the chance to clarify any questions of participants during the interview.
• It can be time-consuming to conduct an unstructured interview and analyze the qualitative data (using methods such as thematic analysis).
• Employing and training interviewers is expensive and not as cheap as collecting data via questionnaires . For example, certain skills may be needed by the interviewer. These include the ability to establish rapport and knowing when to probe.
• Interviews inevitably co-construct data through researchers’ agenda-setting and question-framing. Techniques like open questions provide only limited remedies.

Focus Group Interview

Focus group interview is a qualitative approach where a group of respondents are interviewed together, used to gain an in‐depth understanding of social issues.

This type of interview is often referred to as a focus group because the job of the interviewer ( or moderator ) is to bring the group to focus on the issue at hand. Initially, the goal was to reach a consensus among the group, but with the development of techniques for analyzing group qualitative data, there is less emphasis on consensus building.

The method aims to obtain data from a purposely selected group of individuals rather than from a statistically representative sample of a broader population.

The role of the interview moderator is to make sure the group interacts with each other and do not drift off-topic. Ideally, the moderator will be similar to the participants in terms of appearance, have adequate knowledge of the topic being discussed, and exercise mild unobtrusive control over dominant talkers and shy participants.

A researcher must be highly skilled to conduct a focus group interview. For example, the moderator may need certain skills, including the ability to establish rapport and know when to probe.

• Group interviews generate qualitative narrative data through the use of open questions. This allows the respondents to talk in some depth, choosing their own words. This helps the researcher develop a real sense of a person’s understanding of a situation. Qualitative data also includes observational data, such as body language and facial expressions.
• Group responses are helpful when you want to elicit perspectives on a collective experience, encourage diversity of thought, reduce researcher bias, and gather a wider range of contextualized views.
• They also have increased validity because some participants may feel more comfortable being with others as they are used to talking in groups in real life (i.e., it’s more natural).
• When participants have common experiences, focus groups allow them to build on each other’s comments to provide richer contextual data representing a wider range of views than individual interviews.
• Focus groups are a type of group interview method used in market research and consumer psychology that are cost – effective for gathering the views of consumers .
• The researcher must ensure that they keep all the interviewees” details confidential and respect their privacy. This is difficult when using a group interview. For example, the researcher cannot guarantee that the other people in the group will keep information private.
• Group interviews are less reliable as they use open questions and may deviate from the interview schedule, making them difficult to repeat.
• It is important to note that there are some potential pitfalls of focus groups, such as conformity, social desirability, and oppositional behavior, that can reduce the usefulness of the data collected.

For example, group interviews may sometimes lack validity as participants may lie to impress the other group members. They may conform to peer pressure and give false answers.

To avoid these pitfalls, the interviewer needs to have a good understanding of how people function in groups as well as how to lead the group in a productive discussion.

Semi-Structured Interview

Semi-structured interviews lie between structured and unstructured interviews. The interviewer prepares a set of same questions to be answered by all interviewees. Additional questions might be asked during the interview to clarify or expand certain issues.

In semi-structured interviews, the interviewer has more freedom to digress and probe beyond the answers. The interview guide contains a list of questions and topics that need to be covered during the conversation, usually in a particular order.

Semi-structured interviews are most useful to address the ‘what’, ‘how’, and ‘why’ research questions. Both qualitative and quantitative analyses can be performed on data collected during semi-structured interviews.

• Semi-structured interviews allow respondents to answer more on their terms in an informal setting yet provide uniform information making them ideal for qualitative analysis.
• The flexible nature of semi-structured interviews allows ideas to be introduced and explored during the interview based on the respondents’ answers.
• Semi-structured interviews can provide reliable and comparable qualitative data. Allows the interviewer to probe answers, where the interviewee is asked to clarify or expand on the answers provided.
• The data generated remain fundamentally shaped by the interview context itself. Analysis rarely acknowledges this endemic co-construction.
• They are more time-consuming (to conduct, transcribe, and analyze) than structured interviews.
• The quality of findings is more dependent on the individual skills of the interviewer than in structured interviews. Skill is required to probe effectively while avoiding biasing responses.

The Interviewer Effect

Face-to-face interviews raise methodological problems. These stem from the fact that interviewers are themselves role players, and their perceived status may influence the replies of the respondents.

Because an interview is a social interaction, the interviewer’s appearance or behavior may influence the respondent’s answers. This is a problem as it can bias the results of the study and make them invalid.

For example, the gender, ethnicity, body language, age, and social status of the interview can all create an interviewer effect. If there is a perceived status disparity between the interviewer and the interviewee, the results of interviews have to be interpreted with care. This is pertinent for sensitive topics such as health.

For example, if a researcher was investigating sexism amongst males, would a female interview be preferable to a male? It is possible that if a female interviewer was used, male participants might lie (i.e., pretend they are not sexist) to impress the interviewer, thus creating an interviewer effect.

Flooding interviews with researcher’s agenda

The interactional nature of interviews means the researcher fundamentally shapes the discourse, rather than just neutrally collecting it. This shapes what is talked about and how participants can respond.

• The interviewer’s assumptions, interests, and categories don’t just shape the specific interview questions asked. They also shape the framing, task instructions, recruitment, and ongoing responses/prompts.
• This flooding of the interview interaction with the researcher’s agenda makes it very difficult to separate out what comes from the participant vs. what is aligned with the interviewer’s concerns.
• So the participant’s talk ends up being fundamentally shaped by the interviewer rather than being a more natural reflection of the participant’s own orientations or practices.
• This effect is hard to avoid because interviews inherently involve the researcher setting an agenda. But it does mean the talk extracted may say more about the interview process than the reality it is supposed to reflect.

Interview Design

First, you must choose whether to use a structured or non-structured interview.

Characteristics of Interviewers

Next, you must consider who will be the interviewer, and this will depend on what type of person is being interviewed. There are several variables to consider:

• Gender and age : This can greatly affect respondents’ answers, particularly on personal issues.
• Personal characteristics : Some people are easier to get on with than others. Also, the interviewer’s accent and appearance (e.g., clothing) can affect the rapport between the interviewer and interviewee.
• Language : The interviewer’s language should be appropriate to the vocabulary of the group of people being studied. For example, the researcher must change the questions’ language to match the respondents’ social background” age / educational level / social class/ethnicity, etc.
• Ethnicity : People may have difficulty interviewing people from different ethnic groups.
• Interviewer expertise should match research sensitivity – inexperienced students should avoid interviewing highly vulnerable groups.

Interview Location

The location of a research interview can influence the way in which the interviewer and interviewee relate and may exaggerate a power dynamic in one direction or another. It is usual to offer interviewees a choice of location as part of facilitating their comfort and encouraging participation.

However, the safety of the interviewer is an overriding consideration and, as mentioned, a minimal requirement should be that a responsible person knows where the interviewer has gone and when they are due back.

Remote Interviews

The COVID-19 pandemic necessitated remote interviewing for research continuity. However online interview platforms provide increased flexibility even under normal conditions.

They enable access to participant groups across geographical distances without travel costs or arrangements. Online interviews can be efficiently scheduled to align with researcher and interviewee availability.

There are practical considerations in setting up remote interviews. Interviewees require access to internet and an online platform such as Zoom, Microsoft Teams or Skype through which to connect.

Certain modifications help build initial rapport in the remote format. Allowing time at the start of the interview for casual conversation while testing audio/video quality helps participants settle in. Minor delays can disrupt turn-taking flow, so alerting participants to speak slightly slower than usual minimizes accidental interruptions.

Keeping remote interviews under an hour avoids fatigue for stare at a screen. Seeking advanced ethical clearance for verbal consent at the interview start saves participant time. Adapting to the remote context shows care for interviewees and aids rich discussion.

However, it remains important to critically reflect on how removing in-person dynamics may shape the co-created data. Perhaps some nuances of trust and disclosure differ over video.

Vulnerable Groups

The interviewer must ensure that they take special care when interviewing vulnerable groups, such as children. For example, children have a limited attention span, so lengthy interviews should be avoided.

Developing an Interview Schedule

An interview schedule is a list of pre-planned, structured questions that have been prepared, to serve as a guide for interviewers, researchers and investigators in collecting information or data about a specific topic or issue.

• List the key themes or topics that must be covered to address your research questions. This will form the basic content.
• Organize the content logically, such as chronologically following the interviewee’s experiences. Place more sensitive topics later in the interview.
• Develop the list of content into actual questions and prompts. Carefully word each question – keep them open-ended, non-leading, and focused on examples.
• Add prompts to remind you to cover areas of interest.
• Pilot test the interview schedule to check it generates useful data and revise as needed.
• Be prepared to refine the schedule throughout data collection as you learn which questions work better.
• Practice skills like asking follow-up questions to get depth and detail. Stay flexible to depart from the schedule when needed.
• Keep questions brief and clear. Avoid multi-part questions that risk confusing interviewees.
• Listen actively during interviews to determine which pre-planned questions can be skipped based on information the participant has already provided.

The key is balancing preparation with the flexibility to adapt questions based on each interview interaction. With practice, you’ll gain skills to conduct productive interviews that obtain rich qualitative data.

The Power of Silence

Strategic use of silence is a key technique to generate interviewee-led data, but it requires judgment about appropriate timing and duration to maintain mutual understanding.

• Unlike ordinary conversation, the interviewer aims to facilitate the interviewee’s contribution without interrupting. This often means resisting the urge to speak at the end of the interviewee’s turn construction units (TCUs).
• Leaving a silence after a TCU encourages the interviewee to provide more material without being led by the interviewer. However, this simple technique requires confidence, as silence can feel socially awkward.
• Allowing longer silences (e.g. 24 seconds) later in interviews can work well, but early on even short silences may disrupt rapport if they cause misalignment between speakers.
• Silence also allows interviewees time to think before answering. Rushing to re-ask or amend questions can limit responses.
• Blunt backchannels like “mm hm” also avoid interrupting flow. Interruptions, especially to finish an interviewee’s turn, are problematic as they make the ownership of perspectives unclear.
• If interviewers incorrectly complete turns, an upside is it can produce extended interviewee narratives correcting the record. However, silence would have been better to let interviewees shape their own accounts.

Interviewing Children

By understanding the unique challenges and employing these solutions, interviewers can create a more child-friendly and effective interview process, promoting accurate and reliable information gathering from young witnesses.

Pitfalls to Avoid

• Speculation and misinterpretation : Children may misinterpret questions due to immature language and memory skills, leading them to speculate or provide inaccurate information . For example, children might not fully grasp the meaning of words like “before” and “after” .
• Limited information : Directive questions often result in brief answers, restricting the child’s opportunity to provide a full and detailed account of the event.
• Children’s desire to cooperate: Children are generally inclined to answer any question posed by an authority figure, even if they don’t understand the question or know the answer. This can result in inaccurate responses or speculation to please the interviewer.
• Language development : Young children may not understand complex vocabulary, grammatical structures, or abstract concepts . For instance, words like “aunt” and “uncle” can be challenging for children because their meaning shifts depending on the speaker.
• Memory capabilities : Children’s memory abilities are still developing, making them more susceptible to memory errors and suggestibility . Questions that repeatedly cue specific details can inadvertently alter their recollections .
• Attention span and fatigue : Children, especially younger ones, have shorter attention spans and tire more easily than adults.  Lengthy interviews or those with excessive questioning can lead to fatigue and reduce the quality of their responses.
• Reluctance to talk: Children may be hesitant to talk due to various reasons, such as fear of getting into trouble, an inhibited temperament, distrust of unfamiliar adults, or feeling overwhelmed. This can make it challenging to obtain information from them.
• Adopt a child-centered approach: Interviewers prioritize creating a supportive and less intimidating environment by using techniques tailored to the child’s developmental level and cultural background. This includes using developmentally appropriate language, building rapport, and employing a patient and non-judgmental demeanor.
• Emphasize open-ended questions: Instead of relying heavily on focused questions, interviewers prioritize open-ended prompts that encourage children to provide detailed narratives in their own words. They use open-ended invitations like “Tell me everything that happened” or “What happened next?” to elicit more comprehensive and accurate responses.
• Use a questioning cycle: Interviewers combine open-ended invitations with focused prompts to gather specific details while maintaining a conversational flow. This involves cycling back to broader, open-ended prompts after asking focused questions to ensure a thorough understanding of the situation.
• Provide ample time to respond: Recognizing that children may need more time to process information, interviewers provide sufficient wait time, allowing children to respond at their own pace. This patience helps to reduce pressure and encourages more thoughtful responses.
• Build rapport: Establishing rapport is crucial for creating a safe and comfortable space for children to share their experiences. Interviewers achieve this by engaging in neutral conversations, showing genuine interest in the child’s life, and using a calm and reassuring tone.
• Deliver clear interview instructions: Interviewers provide clear and concise instructions, explaining the ground rules of the interview, emphasizing honesty, and encouraging the child to ask for clarification if needed.
• Use interview aids cautiously: While tools like drawings or diagrams can be helpful, interviewers use them with caution, ensuring they don’t lead or suggest information to the child. These aids are primarily used to clarify or elaborate on information the child has already provided verbally.
• Be prepared for multiple interviews: It’s often necessary to conduct multiple interviews to allow children time to process information and recall additional details. Subsequent interviews provide an opportunity to clarify information and explore inconsistencies.

Recording & Transcription

Design choices.

Design choices around recording and engaging closely with transcripts influence analytic insights, as well as practical feasibility. Weighing up relevant tradeoffs is key.

• Audio recording is standard, but video better captures contextual details, which is useful for some topics/analysis approaches. Participants may find video invasive for sensitive research.
• Digital formats enable the sharing of anonymized clips. Additional microphones reduce audio issues.
• Doing all transcription is time-consuming. Outsourcing can save researcher effort but needs confidentiality assurances. Always carefully check outsourced transcripts.
• Online platform auto-captioning can facilitate rapid analysis, but accuracy limitations mean full transcripts remain ideal. Software cleans up caption file formatting.
• Verbatim transcripts best capture nuanced meaning, but the level of detail needed depends on the analysis approach. Referring back to recordings is still advisable during analysis.
• Transcripts versus recordings highlight different interaction elements. Transcripts make overt disagreements clearer through the wording itself. Recordings better convey tone affiliativeness.

Transcribing Interviews & Focus Groups

Here are the steps for transcribing interviews:

• Play back audio/video files to develop an overall understanding of the interview
• Format the transcription document:
• Add line numbers
• Separate interviewer questions and interviewee responses
• Use formatting like bold, italics, etc. to highlight key passages
• Provide sentence-level clarity in the interviewee’s responses while preserving their authentic voice and word choices
• Break longer passages into smaller paragraphs to help with coding
• If translating the interview to another language, use qualified translators and back-translate where possible
• Select a notation system to indicate pauses, emphasis, laughter, interruptions, etc., and adapt it as needed for your data
• Insert screenshots, photos, or documents discussed in the interview at the relevant point in the transcript
• Read through multiple times, revising formatting and notations
• Double-check the accuracy of transcription against audio/videos
• De-identify transcript by removing identifying participant details

The goal is to produce a formatted written record of the verbal interview exchange that captures the meaning and highlights important passages ready for the coding process. Careful transcription is the vital first step in analysis.

Coding Transcripts

The goal of transcription and coding is to systematically transform interview responses into a set of codes and themes that capture key concepts, experiences and beliefs expressed by participants. Taking care with transcription and coding procedures enhances the validity of qualitative analysis .

• Read through the transcript multiple times to become immersed in the details
• Identify manifest/obvious codes and latent/underlying meaning codes
• Highlight insightful participant quotes that capture key concepts (in vivo codes)
• Create a codebook to organize and define codes with examples
• Use an iterative cycle of inductive (data-driven) coding and deductive (theory-driven) coding
• Refine codebook with clear definitions and examples as you code more transcripts
• Collaborate with other coders to establish the reliability of codes

Ethical Issues

Informed consent.

The participant information sheet must give potential interviewees a good idea of what is involved if taking part in the research.

This will include the general topics covered in the interview, where the interview might take place, how long it is expected to last, how it will be recorded, the ways in which participants’ anonymity will be managed, and incentives offered.

It might be considered good practice to consider true informed consent in interview research to require two distinguishable stages:

• Consent to undertake and record the interview and
• Consent to use the material in research after the interview has been conducted and the content known, or even after the interviewee has seen a copy of the transcript and has had a chance to remove sections, if desired.

Power and Vulnerability

• Early feminist views that sensitivity could equalize power differences are likely naive. The interviewer and interviewee inhabit different knowledge spheres and social categories, indicating structural disparities.
• Power fluctuates within interviews. Researchers rely on participation, yet interviewees control openness and can undermine data collection. Assumptions should be avoided.
• Interviews on sensitive topics may feel like quasi-counseling. Interviewers must refrain from dual roles, instead supplying support service details to all participants.
• Interviewees recruited for trauma experiences may reveal more than anticipated. While generating analytic insights, this risks leaving them feeling exposed.
• Ultimately, power balances resist reconciliation. But reflexively analyzing operations of power serves to qualify rather than nullify situtated qualitative accounts.

Some groups, like those with mental health issues, extreme views, or criminal backgrounds, risk being discredited – treated skeptically by researchers.

This creates tensions with qualitative approaches, often having an empathetic ethos seeking to center subjective perspectives. Analysis should balance openness to offered accounts with critically examining stakes and motivations behind them.

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Interview Method

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The rationale of research interviews is to gain people’s knowledge, views, and experiences, which are meaningful in understanding social realities. Although some research interviews are time-consuming, researchers can interact and communicate while developing a rapport with people to find out these facts—something observations or surveys can never do. How a response from an interview is made (tone of voice, facial expression, hesitation) can feed information that a written response would conceal. Having a good audio quality recorder would be of great assistance. However, if the respondent refuses to be recorded, researchers should practise note-taking. Researchers need to be careful of what and how to ask, as some information may be controversial and confidential. Interviews are a highly subjective method, and the danger of bias always exists.

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Hussein, H. (2022). Interview Method. In: Islam, M.R., Khan, N.A., Baikady, R. (eds) Principles of Social Research Methodology. Springer, Singapore. https://doi.org/10.1007/978-981-19-5441-2_14

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• Types of Interviews in Research | Guide & Examples

Types of Interviews in Research | Guide & Examples

Published on 4 May 2022 by Tegan George . Revised on 10 October 2022.

An interview is a qualitative research method that relies on asking questions in order to collect data . Interviews involve two or more people, one of whom is the interviewer asking the questions.

There are several types of interviews, often differentiated by their level of structure. Structured interviews have predetermined questions asked in a predetermined order. Unstructured interviews are more free-flowing, and semi-structured interviews fall in between.

Interviews are commonly used in market research, social science, and ethnographic research.

Table of contents

What is a structured interview, what is a semi-structured interview, what is an unstructured interview, what is a focus group, examples of interview questions, advantages and disadvantages of interviews, frequently asked questions about types of interviews.

Structured interviews have predetermined questions in a set order. They are often closed-ended, featuring dichotomous (yes/no) or multiple-choice questions. While open-ended structured interviews exist, they are much less common. The types of questions asked make structured interviews a predominantly quantitative tool.

Asking set questions in a set order can help you see patterns among responses, and it allows you to easily compare responses between participants while keeping other factors constant. This can mitigate biases and lead to higher reliability and validity. However, structured interviews can be overly formal, as well as limited in scope and flexibility.

• You feel very comfortable with your topic. This will help you formulate your questions most effectively.
• You have limited time or resources. Structured interviews are a bit more straightforward to analyse because of their closed-ended nature, and can be a doable undertaking for an individual.
• Your research question depends on holding environmental conditions between participants constant

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Semi-structured interviews are a blend of structured and unstructured interviews. While the interviewer has a general plan for what they want to ask, the questions do not have to follow a particular phrasing or order.

Semi-structured interviews are often open-ended, allowing for flexibility, but follow a predetermined thematic framework, giving a sense of order. For this reason, they are often considered ‘the best of both worlds’.

However, if the questions differ substantially between participants, it can be challenging to look for patterns, lessening the generalisability and validity of your results.

• You have prior interview experience. It’s easier than you think to accidentally ask a leading question when coming up with questions on the fly. Overall, spontaneous questions are much more difficult than they may seem.
• Your research question is exploratory in nature. The answers you receive can help guide your future research.

An unstructured interview is the most flexible type of interview. The questions and the order in which they are asked are not set. Instead, the interview can proceed more spontaneously, based on the participant’s previous answers.

Unstructured interviews are by definition open-ended. This flexibility can help you gather detailed information on your topic, while still allowing you to observe patterns between participants.

However, so much flexibility means that they can be very challenging to conduct properly. You must be very careful not to ask leading questions, as biased responses can lead to lower reliability or even invalidate your research.

• You have a solid background in your research topic and have conducted interviews before
• Your research question is exploratory in nature, and you are seeking descriptive data that will deepen and contextualise your initial hypotheses
• Your research necessitates forming a deeper connection with your participants, encouraging them to feel comfortable revealing their true opinions and emotions

A focus group brings together a group of participants to answer questions on a topic of interest in a moderated setting. Focus groups are qualitative in nature and often study the group’s dynamic and body language in addition to their answers. Responses can guide future research on consumer products and services, human behaviour, or controversial topics.

Focus groups can provide more nuanced and unfiltered feedback than individual interviews and are easier to organise than experiments or large surveys. However, their small size leads to low external validity and the temptation as a researcher to ‘cherry-pick’ responses that fit your hypotheses.

• Your research focuses on the dynamics of group discussion or real-time responses to your topic
• Your questions are complex and rooted in feelings, opinions, and perceptions that cannot be answered with a ‘yes’ or ‘no’
• Your topic is exploratory in nature, and you are seeking information that will help you uncover new questions or future research ideas

Depending on the type of interview you are conducting, your questions will differ in style, phrasing, and intention. Structured interview questions are set and precise, while the other types of interviews allow for more open-endedness and flexibility.

Here are some examples.

• Semi-structured
• Unstructured
• Focus group
• Do you like dogs? Yes/No
• Do you associate dogs with feeling: happy; somewhat happy; neutral; somewhat unhappy; unhappy
• If yes, name one attribute of dogs that you like.
• If no, name one attribute of dogs that you don’t like.
• What feelings do dogs bring out in you?
• When you think more deeply about this, what experiences would you say your feelings are rooted in?

Interviews are a great research tool. They allow you to gather rich information and draw more detailed conclusions than other research methods, taking into consideration nonverbal cues, off-the-cuff reactions, and emotional responses.

However, they can also be time-consuming and deceptively challenging to conduct properly. Smaller sample sizes can cause their validity and reliability to suffer, and there is an inherent risk of interviewer effect arising from accidentally leading questions.

Here are some advantages and disadvantages of each type of interview that can help you decide if you’d like to utilise this research method.

The four most common types of interviews are:

• Structured interviews : The questions are predetermined in both topic and order.
• Semi-structured interviews : A few questions are predetermined, but other questions aren’t planned.
• Unstructured interviews : None of the questions are predetermined.
• Focus group interviews : The questions are presented to a group instead of one individual.

A structured interview is a data collection method that relies on asking questions in a set order to collect data on a topic. They are often quantitative in nature. Structured interviews are best used when:

• You already have a very clear understanding of your topic. Perhaps significant research has already been conducted, or you have done some prior research yourself, but you already possess a baseline for designing strong structured questions.
• You are constrained in terms of time or resources and need to analyse your data quickly and efficiently
• Your research question depends on strong parity between participants, with environmental conditions held constant

More flexible interview options include semi-structured interviews , unstructured interviews , and focus groups .

A semi-structured interview is a blend of structured and unstructured types of interviews. Semi-structured interviews are best used when:

• You have prior interview experience. Spontaneous questions are deceptively challenging, and it’s easy to accidentally ask a leading question or make a participant uncomfortable.
• Your research question is exploratory in nature. Participant answers can guide future research questions and help you develop a more robust knowledge base for future research.

An unstructured interview is the most flexible type of interview, but it is not always the best fit for your research topic.

Unstructured interviews are best used when:

• You are an experienced interviewer and have a very strong background in your research topic, since it is challenging to ask spontaneous, colloquial questions
• Your research question is exploratory in nature. While you may have developed hypotheses, you are open to discovering new or shifting viewpoints through the interview process.
• You are seeking descriptive data, and are ready to ask questions that will deepen and contextualise your initial thoughts and hypotheses
• Your research depends on forming connections with your participants and making them feel comfortable revealing deeper emotions, lived experiences, or thoughts

The interviewer effect is a type of bias that emerges when a characteristic of an interviewer (race, age, gender identity, etc.) influences the responses given by the interviewee.

There is a risk of an interviewer effect in all types of interviews , but it can be mitigated by writing really high-quality interview questions.

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• Published: 15 September 2022

Interviews in the social sciences

• Eleanor Knott   ORCID: orcid.org/0000-0002-9131-3939 1 ,
• Aliya Hamid Rao   ORCID: orcid.org/0000-0003-0674-4206 1 ,
• Kate Summers   ORCID: orcid.org/0000-0001-9964-0259 1 &
• Chana Teeger   ORCID: orcid.org/0000-0002-5046-8280 1  

Nature Reviews Methods Primers volume  2 , Article number:  73 ( 2022 ) Cite this article

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• Interdisciplinary studies

In-depth interviews are a versatile form of qualitative data collection used by researchers across the social sciences. They allow individuals to explain, in their own words, how they understand and interpret the world around them. Interviews represent a deceptively familiar social encounter in which people interact by asking and answering questions. They are, however, a very particular type of conversation, guided by the researcher and used for specific ends. This dynamic introduces a range of methodological, analytical and ethical challenges, for novice researchers in particular. In this Primer, we focus on the stages and challenges of designing and conducting an interview project and analysing data from it, as well as strategies to overcome such challenges.

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Introduction.

In-depth interviews are a qualitative research method that follow a deceptively familiar logic of human interaction: they are conversations where people talk with each other, interact and pose and answer questions 1 . An interview is a specific type of interaction in which — usually and predominantly — a researcher asks questions about someone’s life experience, opinions, dreams, fears and hopes and the interview participant answers the questions 1 .

Interviews will often be used as a standalone method or combined with other qualitative methods, such as focus groups or ethnography, or quantitative methods, such as surveys or experiments. Although interviewing is a frequently used method, it should not be viewed as an easy default for qualitative researchers 2 . Interviews are also not suited to answering all qualitative research questions, but instead have specific strengths that should guide whether or not they are deployed in a research project. Whereas ethnography might be better suited to trying to observe what people do, interviews provide a space for extended conversations that allow the researcher insights into how people think and what they believe. Quantitative surveys also give these kinds of insights, but they use pre-determined questions and scales, privileging breadth over depth and often overlooking harder-to-reach participants.

In-depth interviews can take many different shapes and forms, often with more than one participant or researcher. For example, interviews might be highly structured (using an almost survey-like interview guide), entirely unstructured (taking a narrative and free-flowing approach) or semi-structured (using a topic guide ). Researchers might combine these approaches within a single project depending on the purpose of the interview and the characteristics of the participant. Whatever form the interview takes, researchers should be mindful of the dynamics between interviewer and participant and factor these in at all stages of the project.

In this Primer, we focus on the most common type of interview: one researcher taking a semi-structured approach to interviewing one participant using a topic guide. Focusing on how to plan research using interviews, we discuss the necessary stages of data collection. We also discuss the stages and thought-process behind analysing interview material to ensure that the richness and interpretability of interview material is maintained and communicated to readers. The Primer also tracks innovations in interview methods and discusses the developments we expect over the next 5–10 years.

We wrote this Primer as researchers from sociology, social policy and political science. We note our disciplinary background because we acknowledge that there are disciplinary differences in how interviews are approached and understood as a method.

Experimentation

Here we address research design considerations and data collection issues focusing on topic guide construction and other pragmatics of the interview. We also explore issues of ethics and reflexivity that are crucial throughout the research project.

Research design

Participant selection.

Participants can be selected and recruited in various ways for in-depth interview studies. The researcher must first decide what defines the people or social groups being studied. Often, this means moving from an abstract theoretical research question to a more precise empirical one. For example, the researcher might be interested in how people talk about race in contexts of diversity. Empirical settings in which this issue could be studied could include schools, workplaces or adoption agencies. The best research designs should clearly explain why the particular setting was chosen. Often there are both intrinsic and extrinsic reasons for choosing to study a particular group of people at a specific time and place 3 . Intrinsic motivations relate to the fact that the research is focused on an important specific social phenomenon that has been understudied. Extrinsic motivations speak to the broader theoretical research questions and explain why the case at hand is a good one through which to address them empirically.

Next, the researcher needs to decide which types of people they would like to interview. This decision amounts to delineating the inclusion and exclusion criteria for the study. The criteria might be based on demographic variables, like race or gender, but they may also be context-specific, for example, years of experience in an organization. These should be decided based on the research goals. Researchers should be clear about what characteristics would make an individual a candidate for inclusion in the study (and what would exclude them).

The next step is to identify and recruit the study’s sample . Usually, many more people fit the inclusion criteria than can be interviewed. In cases where lists of potential participants are available, the researcher might want to employ stratified sampling , dividing the list by characteristics of interest before sampling.

When there are no lists, researchers will often employ purposive sampling . Many researchers consider purposive sampling the most useful mode for interview-based research since the number of interviews to be conducted is too small to aim to be statistically representative 4 . Instead, the aim is not breadth, via representativeness, but depth via rich insights about a set of participants. In addition to purposive sampling, researchers often use snowball sampling . Both purposive and snowball sampling can be combined with quota sampling . All three types of sampling aim to ensure a variety of perspectives within the confines of a research project. A goal for in-depth interview studies can be to sample for range, being mindful of recruiting a diversity of participants fitting the inclusion criteria.

Study design

The total number of interviews depends on many factors, including the population studied, whether comparisons are to be made and the duration of interviews. Studies that rely on quota sampling where explicit comparisons are made between groups will require a larger number of interviews than studies focused on one group only. Studies where participants are interviewed over several hours, days or even repeatedly across years will tend to have fewer participants than those that entail a one-off engagement.

Researchers often stop interviewing when new interviews confirm findings from earlier interviews with no new or surprising insights (saturation) 4 , 5 , 6 . As a criterion for research design, saturation assumes that data collection and analysis are happening in tandem and that researchers will stop collecting new data once there is no new information emerging from the interviews. This is not always possible. Researchers rarely have time for systematic data analysis during data collection and they often need to specify their sample in funding proposals prior to data collection. As a result, researchers often draw on existing reports of saturation to estimate a sample size prior to data collection. These suggest between 12 and 20 interviews per category of participant (although researchers have reported saturation with samples that are both smaller and larger than this) 7 , 8 , 9 . The idea of saturation has been critiqued by many qualitative researchers because it assumes that meaning inheres in the data, waiting to be discovered — and confirmed — once saturation has been reached 7 . In-depth interview data are often multivalent and can give rise to different interpretations. The important consideration is, therefore, not merely how many participants are interviewed, but whether one’s research design allows for collecting rich and textured data that provide insight into participants’ understandings, accounts, perceptions and interpretations.

Sometimes, researchers will conduct interviews with more than one participant at a time. Researchers should consider the benefits and shortcomings of such an approach. Joint interviews may, for example, give researchers insight into how caregivers agree or debate childrearing decisions. At the same time, they may be less adaptive to exploring aspects of caregiving that participants may not wish to disclose to each other. In other cases, there may be more than one person interviewing each participant, such as when an interpreter is used, and so it is important to consider during the research design phase how this might shape the dynamics of the interview.

Data collection

Semi-structured interviews are typically organized around a topic guide comprised of an ordered set of broad topics (usually 3–5). Each topic includes a set of questions that form the basis of the discussion between the researcher and participant (Fig.  1 ). These topics are organized around key concepts that the researcher has identified (for example, through a close study of prior research, or perhaps through piloting a small, exploratory study) 5 .

a | Elaborated topics the researcher wants to cover in the interview and example questions. b | An example topic arc. Using such an arc, one can think flexibly about the order of topics. Considering the main question for each topic will help to determine the best order for the topics. After conducting some interviews, the researcher can move topics around if a different order seems to make sense.

Topic guide

One common way to structure a topic guide is to start with relatively easy, open-ended questions (Table  1 ). Opening questions should be related to the research topic but broad and easy to answer, so that they help to ease the participant into conversation.

After these broad, opening questions, the topic guide may move into topics that speak more directly to the overarching research question. The interview questions will be accompanied by probes designed to elicit concrete details and examples from the participant (see Table  1 ).

Abstract questions are often easier for participants to answer once they have been asked more concrete questions. In our experience, for example, questions about feelings can be difficult for some participants to answer, but when following probes concerning factual experiences these questions can become less challenging. After the main themes of the topic guide have been covered, the topic guide can move onto closing questions. At this stage, participants often repeat something they have said before, although they may sometimes introduce a new topic.

Interviews are especially well suited to gaining a deeper insight into people’s experiences. Getting these insights largely depends on the participants’ willingness to talk to the researcher. We recommend designing open-ended questions that are more likely to elicit an elaborated response and extended reflection from participants rather than questions that can be answered with yes or no.

Questions should avoid foreclosing the possibility that the participant might disagree with the premise of the question. Take for example the question: “Do you support the new family-friendly policies?” This question minimizes the possibility of the participant disagreeing with the premise of this question, which assumes that the policies are ‘family-friendly’ and asks for a yes or no answer. Instead, asking more broadly how a participant feels about the specific policy being described as ‘family-friendly’ (for example, a work-from-home policy) allows them to express agreement, disagreement or impartiality and, crucially, to explain their reasoning 10 .

For an uninterrupted interview that will last between 90 and 120 minutes, the topic guide should be one to two single-spaced pages with questions and probes. Ideally, the researcher will memorize the topic guide before embarking on the first interview. It is fine to carry a printed-out copy of the topic guide but memorizing the topic guide ahead of the interviews can often make the interviewer feel well prepared in guiding the participant through the interview process.

Although the topic guide helps the researcher stay on track with the broad areas they want to cover, there is no need for the researcher to feel tied down by the topic guide. For instance, if a participant brings up a theme that the researcher intended to discuss later or a point the researcher had not anticipated, the researcher may well decide to follow the lead of the participant. The researcher’s role extends beyond simply stating the questions; it entails listening and responding, making split-second decisions about what line of inquiry to pursue and allowing the interview to proceed in unexpected directions.

Optimizing the interview

The ideal place for an interview will depend on the study and what is feasible for participants. Generally, a place where the participant and researcher can both feel relaxed, where the interview can be uninterrupted and where noise or other distractions are limited is ideal. But this may not always be possible and so the researcher needs to be prepared to adapt their plans within what is feasible (and desirable for participants).

Another key tool for the interview is a recording device (assuming that permission for recording has been given). Recording can be important to capture what the participant says verbatim. Additionally, it can allow the researcher to focus on determining what probes and follow-up questions they want to pursue rather than focusing on taking notes. Sometimes, however, a participant may not allow the researcher to record, or the recording may fail. If the interview is not recorded we suggest that the researcher takes brief notes during the interview, if feasible, and then thoroughly make notes immediately after the interview and try to remember the participant’s facial expressions, gestures and tone of voice. Not having a recording of an interview need not limit the researcher from getting analytical value from it.

As soon as possible after each interview, we recommend that the researcher write a one-page interview memo comprising three key sections. The first section should identify two to three important moments from the interview. What constitutes important is up to the researcher’s discretion 9 . The researcher should note down what happened in these moments, including the participant’s facial expressions, gestures, tone of voice and maybe even the sensory details of their surroundings. This exercise is about capturing ethnographic detail from the interview. The second part of the interview memo is the analytical section with notes on how the interview fits in with previous interviews, for example, where the participant’s responses concur or diverge from other responses. The third part consists of a methodological section where the researcher notes their perception of their relationship with the participant. The interview memo allows the researcher to think critically about their positionality and practice reflexivity — key concepts for an ethical and transparent research practice in qualitative methodology 11 , 12 .

Ethics and reflexivity

All elements of an in-depth interview can raise ethical challenges and concerns. Good ethical practice in interview studies often means going beyond the ethical procedures mandated by institutions 13 . While discussions and requirements of ethics can differ across disciplines, here we focus on the most pertinent considerations for interviews across the research process for an interdisciplinary audience.

Ethical considerations prior to interview

Before conducting interviews, researchers should consider harm minimization, informed consent, anonymity and confidentiality, and reflexivity and positionality. It is important for the researcher to develop their own ethical sensitivities and sensibilities by gaining training in interview and qualitative methods, reading methodological and field-specific texts on interviews and ethics and discussing their research plans with colleagues.

Researchers should map the potential harm to consider how this can be minimized. Primarily, researchers should consider harm from the participants’ perspective (Box  1 ). But, it is also important to consider and plan for potential harm to the researcher, research assistants, gatekeepers, future researchers and members of the wider community 14 . Even the most banal of research topics can potentially pose some form of harm to the participant, researcher and others — and the level of harm is often highly context-dependent. For example, a research project on religion in society might have very different ethical considerations in a democratic versus authoritarian research context because of how openly or not such topics can be discussed and debated 15 .

The researcher should consider how they will obtain and record informed consent (for example, written or oral), based on what makes the most sense for their research project and context 16 . Some institutions might specify how informed consent should be gained. Regardless of how consent is obtained, the participant must be made aware of the form of consent, the intentions and procedures of the interview and potential forms of harm and benefit to the participant or community before the interview commences. Moreover, the participant must agree to be interviewed before the interview commences. If, in addition to interviews, the study contains an ethnographic component, it is worth reading around this topic (see, for example, Murphy and Dingwall 17 ). Informed consent must also be gained for how the interview will be recorded before the interview commences. These practices are important to ensure the participant is contributing on a voluntary basis. It is also important to remind participants that they can withdraw their consent at any time during the interview and for a specified period after the interview (to be decided with the participant). The researcher should indicate that participants can ask for anything shared to be off the record and/or not disseminated.

In terms of anonymity and confidentiality, it is standard practice when conducting interviews to agree not to use (or even collect) participants’ names and personal details that are not pertinent to the study. Anonymizing can often be the safer option for minimizing harm to participants as it is hard to foresee all the consequences of de-anonymizing, even if participants agree. Regardless of what a researcher decides, decisions around anonymity must be agreed with participants during the process of gaining informed consent and respected following the interview.

Although not all ethical challenges can be foreseen or planned for 18 , researchers should think carefully — before the interview — about power dynamics, participant vulnerability, emotional state and interactional dynamics between interviewer and participant, even when discussing low-risk topics. Researchers may then wish to plan for potential ethical issues, for example by preparing a list of relevant organizations to which participants can be signposted. A researcher interviewing a participant about debt, for instance, might prepare in advance a list of debt advice charities, organizations and helplines that could provide further support and advice. It is important to remember that the role of an interviewer is as a researcher rather than as a social worker or counsellor because researchers may not have relevant and requisite training in these other domains.

Box 1 Mapping potential forms of harm

Social: researchers should avoid causing any relational detriment to anyone in the course of interviews, for example, by sharing information with other participants or causing interview participants to be shunned or mistreated by their community as a result of participating.

Economic: researchers should avoid causing financial detriment to anyone, for example, by expecting them to pay for transport to be interviewed or to potentially lose their job as a result of participating.

Physical: researchers should minimize the risk of anyone being exposed to violence as a result of the research both from other individuals or from authorities, including police.

Psychological: researchers should minimize the risk of causing anyone trauma (or re-traumatization) or psychological anguish as a result of the research; this includes not only the participant but importantly the researcher themselves and anyone that might read or analyse the transcripts, should they contain triggering information.

Political: researchers should minimize the risk of anyone being exposed to political detriment as a result of the research, such as retribution.

Professional/reputational: researchers should minimize the potential for reputational damage to anyone connected to the research (this includes ensuring good research practices so that any researchers involved are not harmed reputationally by being involved with the research project).

The task here is not to map exhaustively the potential forms of harm that might pertain to a particular research project (that is the researcher’s job and they should have the expertise most suited to mapping such potential harms relative to the specific project) but to demonstrate the breadth of potential forms of harm.

Ethical considerations post-interview

Researchers should consider how interview data are stored, analysed and disseminated. If participants have been offered anonymity and confidentiality, data should be stored in a way that does not compromise this. For example, researchers should consider removing names and any other unnecessary personal details from interview transcripts, password-protecting and encrypting files and using pseudonyms to label and store all interview data. It is also important to address where interview data are taken (for example, across borders in particular where interview data might be of interest to local authorities) and how this might affect the storage of interview data.

Examining how the researcher will represent participants is a paramount ethical consideration both in the planning stages of the interview study and after it has been conducted. Dissemination strategies also need to consider questions of anonymity and representation. In small communities, even if participants are given pseudonyms, it might be obvious who is being described. Anonymizing not only the names of those participating but also the research context is therefore a standard practice 19 . With particularly sensitive data or insights about the participant, it is worth considering describing participants in a more abstract way rather than as specific individuals. These practices are important both for protecting participants’ anonymity but can also affect the ability of the researcher and others to return ethically to the research context and similar contexts 20 .

Reflexivity and positionality

Reflexivity and positionality mean considering the researcher’s role and assumptions in knowledge production 13 . A key part of reflexivity is considering the power relations between the researcher and participant within the interview setting, as well as how researchers might be perceived by participants. Further, researchers need to consider how their own identities shape the kind of knowledge and assumptions they bring to the interview, including how they approach and ask questions and their analysis of interviews (Box  2 ). Reflexivity is a necessary part of developing ethical sensibility as a researcher by adapting and reflecting on how one engages with participants. Participants should not feel judged, for example, when they share information that researchers might disagree with or find objectionable. How researchers deal with uncomfortable moments or information shared by participants is at their discretion, but they should consider how they will react both ahead of time and in the moment.

Researchers can develop their reflexivity by considering how they themselves would feel being asked these interview questions or represented in this way, and then adapting their practice accordingly. There might be situations where these questions are not appropriate in that they unduly centre the researchers’ experiences and worldview. Nevertheless, these prompts can provide a useful starting point for those beginning their reflexive journey and developing an ethical sensibility.

Reflexivity and ethical sensitivities require active reflection throughout the research process. For example, researchers should take care in interview memos and their notes to consider their assumptions, potential preconceptions, worldviews and own identities prior to and after interviews (Box  2 ). Checking in with assumptions can be a way of making sure that researchers are paying close attention to their own theoretical and analytical biases and revising them in accordance with what they learn through the interviews. Researchers should return to these notes (especially when analysing interview material), to try to unpack their own effects on the research process as well as how participants positioned and engaged with them.

Box 2 Aspects to reflect on reflexively

For reflexive engagement, and understanding the power relations being co-constructed and (re)produced in interviews, it is necessary to reflect, at a minimum, on the following.

Ethnicity, race and nationality, such as how does privilege stemming from race or nationality operate between the researcher, the participant and research context (for example, a researcher from a majority community may be interviewing a member of a minority community)

Gender and sexuality, see above on ethnicity, race and nationality

Social class, and in particular the issue of middle-class bias among researchers when formulating research and interview questions

Economic security/precarity, see above on social class and thinking about the researcher’s relative privilege and the source of biases that stem from this

Educational experiences and privileges, see above

Disciplinary biases, such as how the researcher’s discipline/subfield usually approaches these questions, possibly normalizing certain assumptions that might be contested by participants and in the research context

Political and social values

Lived experiences and other dimensions of ourselves that affect and construct our identity as researchers

In this section, we discuss the next stage of an interview study, namely, analysing the interview data. Data analysis may begin while more data are being collected. Doing so allows early findings to inform the focus of further data collection, as part of an iterative process across the research project. Here, the researcher is ultimately working towards achieving coherence between the data collected and the findings produced to answer successfully the research question(s) they have set.

The two most common methods used to analyse interview material across the social sciences are thematic analysis 21 and discourse analysis 22 . Thematic analysis is a particularly useful and accessible method for those starting out in analysis of qualitative data and interview material as a method of coding data to develop and interpret themes in the data 21 . Discourse analysis is more specialized and focuses on the role of discourse in society by paying close attention to the explicit, implicit and taken-for-granted dimensions of language and power 22 , 23 . Although thematic and discourse analysis are often discussed as separate techniques, in practice researchers might flexibly combine these approaches depending on the object of analysis. For example, those intending to use discourse analysis might first conduct thematic analysis as a way to organize and systematize the data. The object and intention of analysis might differ (for example, developing themes or interrogating language), but the questions facing the researcher (such as whether to take an inductive or deductive approach to analysis) are similar.

Preparing data

Data preparation is an important step in the data analysis process. The researcher should first determine what comprises the corpus of material and in what form it will it be analysed. The former refers to whether, for example, alongside the interviews themselves, analytic memos or observational notes that may have been taken during data collection will also be directly analysed. The latter refers to decisions about how the verbal/audio interview data will be transformed into a written form, making it suitable for processes of data analysis. Typically, interview audio recordings are transcribed to produce a written transcript. It is important to note that the process of transcription is one of transformation. The verbal interview data are transformed into a written transcript through a series of decisions that the researcher must make. The researcher should consider the effect of mishearing what has been said or how choosing to punctuate a sentence in a particular way will affect the final analysis.

Box  3 shows an example transcript excerpt from an interview with a teacher conducted by Teeger as part of her study of history education in post-apartheid South Africa 24 (Box  3 ). Seeing both the questions and the responses means that the reader can contextualize what the participant (Ms Mokoena) has said. Throughout the transcript the researcher has used square brackets, for example to indicate a pause in speech, when Ms Mokoena says “it’s [pause] it’s a difficult topic”. The transcription choice made here means that we see that Ms Mokoena has taken time to pause, perhaps to search for the right words, or perhaps because she has a slight apprehension. Square brackets are also included as an overt act of communication to the reader. When Ms Mokoena says “ja”, the English translation (“yes”) of the word in Afrikaans is placed in square brackets to ensure that the reader can follow the meaning of the speech.

Decisions about what to include when transcribing will be hugely important for the direction and possibilities of analysis. Researchers should decide what they want to capture in the transcript, based on their analytic focus. From a (post)positivist perspective 25 , the researcher may be interested in the manifest content of the interview (such as what is said, not how it is said). In that case, they may choose to transcribe intelligent verbatim . From a constructivist perspective 25 , researchers may choose to record more aspects of speech (including, for example, pauses, repetitions, false starts, talking over one another) so that these features can be analysed. Those working from this perspective argue that to recognize the interactional nature of the interview setting adequately and to avoid misinterpretations, features of interaction (pauses, overlaps between speakers and so on) should be preserved in transcription and therefore in the analysis 10 . Readers interested in learning more should consult Potter and Hepburn’s summary of how to present interaction through transcription of interview data 26 .

The process of analysing semi-structured interviews might be thought of as a generative rather than an extractive enterprise. Findings do not already exist within the interview data to be discovered. Rather, researchers create something new when analysing the data by applying their analytic lens or approach to the transcripts. At a high level, there are options as to what researchers might want to glean from their interview data. They might be interested in themes, whereby they identify patterns of meaning across the dataset 21 . Alternatively, they may focus on discourse(s), looking to identify how language is used to construct meanings and therefore how language reinforces or produces aspects of the social world 27 . Alternatively, they might look at the data to understand narrative or biographical elements 28 .

A further overarching decision to make is the extent to which researchers bring predetermined framings or understandings to bear on their data, or instead begin from the data themselves to generate an analysis. One way of articulating this is the extent to which researchers take a deductive approach or an inductive approach to analysis. One example of a truly inductive approach is grounded theory, whereby the aim of the analysis is to build new theory, beginning with one’s data 6 , 29 . In practice, researchers using thematic and discourse analysis often combine deductive and inductive logics and describe their process instead as iterative (referred to also as an abductive approach ) 30 , 31 . For example, researchers may decide that they will apply a given theoretical framing, or begin with an initial analytic framework, but then refine or develop these once they begin the process of analysis.

Box 3 Excerpt of interview transcript (from Teeger 24 )

Interviewer : Maybe you could just start by talking about what it’s like to teach apartheid history.

Ms Mokoena : It’s a bit challenging. You’ve got to accommodate all the kids in the class. You’ve got to be sensitive to all the racial differences. You want to emphasize the wrongs that were done in the past but you also want to, you know, not to make kids feel like it’s their fault. So you want to use the wrongs of the past to try and unite the kids …

Interviewer : So what kind of things do you do?

Ms Mokoena : Well I normally highlight the fact that people that were struggling were not just the blacks, it was all the races. And I give examples of the people … from all walks of life, all races, and highlight how they suffered as well as a result of apartheid, particularly the whites… . What I noticed, particularly my first year of teaching apartheid, I noticed that the black kids made the others feel responsible for what happened… . I had a lot of fights…. A lot of kids started hating each other because, you know, the others are white and the others were black. And they started saying, “My mother is a domestic worker because she was never allowed an opportunity to get good education.” …

Interviewer : I didn’t see any of that now when I was observing.

Ms Mokoena : … Like I was saying I think that because of the re-emphasis of the fact that, look, everybody did suffer one way or the other, they sort of got to see that it was everybody’s struggle … . They should now get to understand that that’s why we’re called a Rainbow Nation. Not everybody agreed with apartheid and not everybody suffered. Even all the blacks, not all blacks got to feel what the others felt . So ja [yes], it’s [pause] it’s a difficult topic, ja . But I think if you get the kids to understand why we’re teaching apartheid in the first place and you show the involvement of all races in all the different sides , then I think you have managed to teach it properly. So I think because of my inexperience then — that was my first year of teaching history — so I think I — maybe I over-emphasized the suffering of the blacks versus the whites [emphasis added].

Reprinted with permission from ref. 24 , Sage Publications.

From data to codes

Coding data is a key building block shared across many approaches to data analysis. Coding is a way of organizing and describing data, but is also ultimately a way of transforming data to produce analytic insights. The basic practice of coding involves highlighting a segment of text (this may be a sentence, a clause or a longer excerpt) and assigning a label to it. The aim of the label is to communicate some sort of summary of what is in the highlighted piece of text. Coding is an iterative process, whereby researchers read and reread their transcripts, applying and refining their codes, until they have a coding frame (a set of codes) that is applied coherently across the dataset and that captures and communicates the key features of what is contained in the data as it relates to the researchers’ analytic focus.

What one codes for is entirely contingent on the focus of the research project and the choices the researcher makes about the approach to analysis. At first, one might apply descriptive codes, summarizing what is contained in the interviews. It is rarely desirable to stop at this point, however, because coding is a tool to move from describing the data to interpreting the data. Suppose the researcher is pursuing some version of thematic analysis. In that case, it might be that the objects of coding are aspects of reported action, emotions, opinions, norms, relationships, routines, agreement/disagreement and change over time. A discourse analysis might instead code for different types of speech acts, tropes, linguistic or rhetorical devices. Multiple types of code might be generated within the same research project. What is important is that researchers are aware of the choices they are making in terms of what they are coding for. Moreover, through the process of refinement, the aim is to produce a set of discrete codes — in which codes are conceptually distinct, as opposed to overlapping. By using the same codes across the dataset, the researcher can capture commonalities across the interviews. This process of refinement involves relabelling codes and reorganizing how and where they are applied in the dataset.

From coding to analysis and writing

Data analysis is also an iterative process in which researchers move closer to and further away from the data. As they move away from the data, they synthesize their findings, thus honing and articulating their analytic insights. As they move closer to the data, they ground these insights in what is contained in the interviews. The link should not be broken between the data themselves and higher-order conceptual insights or claims being made. Researchers must be able to show evidence for their claims in the data. Figure  2 summarizes this iterative process and suggests the sorts of activities involved at each stage more concretely.

As well as going through steps 1 to 6 in order, the researcher will also go backwards and forwards between stages. Some stages will themselves be a forwards and backwards processing of coding and refining when working across different interview transcripts.

At the stage of synthesizing, there are some common quandaries. When dealing with a dataset consisting of multiple interviews, there will be salient and minority statements across different participants, or consensus or dissent on topics of interest to the researcher. A strength of qualitative interviews is that we can build in these nuances and variations across our data as opposed to aggregating them away. When exploring and reporting data, researchers should be asking how different findings are patterned and which interviews contain which codes, themes or tropes. Researchers should think about how these variations fit within the longer flow of individual interviews and what these variations tell them about the nature of their substantive research interests.

A further consideration is how to approach analysis within and across interview data. Researchers may look at one individual code, to examine the forms it takes across different participants and what they might be able to summarize about this code in the round. Alternatively, they might look at how a code or set of codes pattern across the account of one participant, to understand the code(s) in a more contextualized way. Further analysis might be done according to different sampling characteristics, where researchers group together interviews based on certain demographic characteristics and explore these together.

When it comes to writing up and presenting interview data, key considerations tend to rest on what is often termed transparency. When presenting the findings of an interview-based study, the reader should be able to understand and trace what the stated findings are based upon. This process typically involves describing the analytic process, how key decisions were made and presenting direct excerpts from the data. It is important to account for how the interview was set up and to consider the active part that the researcher has played in generating the data 32 . Quotes from interviews should not be thought of as merely embellishing or adding interest to a final research output. Rather, quotes serve the important function of connecting the reader directly to the underlying data. Quotes, therefore, should be chosen because they provide the reader with the most apt insight into what is being discussed. It is good practice to report not just on what participants said, but also on the questions that were asked to elicit the responses.

Researchers have increasingly used specialist qualitative data analysis software to organize and analyse their interview data, such as NVivo or ATLAS.ti. It is important to remember that such software is a tool for, rather than an approach or technique of, analysis. That said, software also creates a wide range of possibilities in terms of what can be done with the data. As researchers, we should reflect on how the range of possibilities of a given software package might be shaping our analytical choices and whether these are choices that we do indeed want to make.

Applications

This section reviews how and why in-depth interviews have been used by researchers studying gender, education and inequality, nationalism and ethnicity and the welfare state. Although interviews can be employed as a method of data collection in just about any social science topic, the applications below speak directly to the authors’ expertise and cutting-edge areas of research.

When it comes to the broad study of gender, in-depth interviews have been invaluable in shaping our understanding of how gender functions in everyday life. In a study of the US hedge fund industry (an industry dominated by white men), Tobias Neely was interested in understanding the factors that enable white men to prosper in the industry 33 . The study comprised interviews with 45 hedge fund workers and oversampled women of all races and men of colour to capture a range of experiences and beliefs. Tobias Neely found that practices of hiring, grooming and seeding are key to maintaining white men’s dominance in the industry. In terms of hiring, the interviews clarified that white men in charge typically preferred to hire people like themselves, usually from their extended networks. When women were hired, they were usually hired to less lucrative positions. In terms of grooming, Tobias Neely identifies how older and more senior men in the industry who have power and status will select one or several younger men as their protégés, to include in their own elite networks. Finally, in terms of her concept of seeding, Tobias Neely describes how older men who are hedge fund managers provide the seed money (often in the hundreds of millions of dollars) for a hedge fund to men, often their own sons (but not their daughters). These interviews provided an in-depth look into gendered and racialized mechanisms that allow white men to flourish in this industry.

Research by Rao draws on dozens of interviews with men and women who had lost their jobs, some of the participants’ spouses and follow-up interviews with about half the sample approximately 6 months after the initial interview 34 . Rao used interviews to understand the gendered experience and understanding of unemployment. Through these interviews, she found that the very process of losing their jobs meant different things for men and women. Women often saw job loss as being a personal indictment of their professional capabilities. The women interviewed often referenced how years of devaluation in the workplace coloured their interpretation of their job loss. Men, by contrast, were also saddened by their job loss, but they saw it as part and parcel of a weak economy rather than a personal failing. How these varied interpretations occurred was tied to men’s and women’s very different experiences in the workplace. Further, through her analysis of these interviews, Rao also showed how these gendered interpretations had implications for the kinds of jobs men and women sought to pursue after job loss. Whereas men remained tied to participating in full-time paid work, job loss appeared to be a catalyst pushing some of the women to re-evaluate their ties to the labour force.

In a study of workers in the tech industry, Hart used interviews to explain how individuals respond to unwanted and ambiguously sexual interactions 35 . Here, the researcher used interviews to allow participants to describe how these interactions made them feel and act and the logics of how they interpreted, classified and made sense of them 35 . Through her analysis of these interviews, Hart showed that participants engaged in a process she termed “trajectory guarding”, whereby they sought to monitor unwanted and ambiguously sexual interactions to avoid them from escalating. Yet, as Hart’s analysis proficiently demonstrates, these very strategies — which protect these workers sexually — also undermined their workplace advancement.

Drawing on interviews, these studies have helped us to understand better how gendered mechanisms, gendered interpretations and gendered interactions foster gender inequality when it comes to paid work. Methodologically, these studies illuminate the power of interviews to reveal important aspects of social life.

Nationalism and ethnicity

Traditionally, nationalism has been studied from a top-down perspective, through the lens of the state or using historical methods; in other words, in-depth interviews have not been a common way of collecting data to study nationalism. The methodological turn towards everyday nationalism has encouraged more scholars to go to the field and use interviews (and ethnography) to understand nationalism from the bottom up: how people talk about, give meaning, understand, navigate and contest their relation to nation, national identification and nationalism 36 , 37 , 38 , 39 . This turn has also addressed the gap left by those studying national and ethnic identification via quantitative methods, such as surveys.

Surveys can enumerate how individuals ascribe to categorical forms of identification 40 . However, interviews can question the usefulness of such categories and ask whether these categories are reflected, or resisted, by participants in terms of the meanings they give to identification 41 , 42 . Categories often pitch identification as a mutually exclusive choice; but identification might be more complex than such categories allow. For example, some might hybridize these categories or see themselves as moving between and across categories 43 . Hearing how people talk about themselves and their relation to nations, states and ethnicities, therefore, contributes substantially to the study of nationalism and national and ethnic forms of identification.

One particular approach to studying these topics, whether via everyday nationalism or alternatives, is that of using interviews to capture both articulations and narratives of identification, relations to nationalism and the boundaries people construct. For example, interviews can be used to gather self–other narratives by studying how individuals construct I–we–them boundaries 44 , including how participants talk about themselves, who participants include in their various ‘we’ groupings and which and how participants create ‘them’ groupings of others, inserting boundaries between ‘I/we’ and ‘them’. Overall, interviews hold great potential for listening to participants and understanding the nuances of identification and the construction of boundaries from their point of view.

Education and inequality

Scholars of social stratification have long noted that the school system often reproduces existing social inequalities. Carter explains that all schools have both material and sociocultural resources 45 . When children from different backgrounds attend schools with different material resources, their educational and occupational outcomes are likely to vary. Such material resources are relatively easy to measure. They are operationalized as teacher-to-student ratios, access to computers and textbooks and the physical infrastructure of classrooms and playgrounds.

Drawing on Bourdieusian theory 46 , Carter conceptualizes the sociocultural context as the norms, values and dispositions privileged within a social space 45 . Scholars have drawn on interviews with students and teachers (as well as ethnographic observations) to show how schools confer advantages on students from middle-class families, for example, by rewarding their help-seeking behaviours 47 . Focusing on race, researchers have revealed how schools can remain socioculturally white even as they enrol a racially diverse student population. In such contexts, for example, teachers often misrecognize the aesthetic choices made by students of colour, wrongly inferring that these students’ tastes in clothing and music reflect negative orientations to schooling 48 , 49 , 50 . These assessments can result in disparate forms of discipline and may ultimately shape educators’ assessments of students’ academic potential 51 .

Further, teachers and administrators tend to view the appropriate relationship between home and school in ways that resonate with white middle-class parents 52 . These parents are then able to advocate effectively for their children in ways that non-white parents are not 53 . In-depth interviews are particularly good at tapping into these understandings, revealing the mechanisms that confer privilege on certain groups of students and thereby reproduce inequality.

In addition, interviews can shed light on the unequal experiences that young people have within educational institutions, as the views of dominant groups are affirmed while those from disadvantaged backgrounds are delegitimized. For example, Teeger’s interviews with South African high schoolers showed how — because racially charged incidents are often framed as jokes in the broader school culture — Black students often feel compelled to ignore and keep silent about the racism they experience 54 . Interviews revealed that Black students who objected to these supposed jokes were coded by other students as serious or angry. In trying to avoid such labels, these students found themselves unable to challenge the racism they experienced. Interviews give us insight into these dynamics and help us see how young people understand and interpret the messages transmitted in schools — including those that speak to issues of inequality in their local school contexts as well as in society more broadly 24 , 55 .

The welfare state

In-depth interviews have also proved to be an important method for studying various aspects of the welfare state. By welfare state, we mean the social institutions relating to the economic and social wellbeing of a state’s citizens. Notably, using interviews has been useful to look at how policy design features are experienced and play out on the ground. Interviews have often been paired with large-scale surveys to produce mixed-methods study designs, therefore achieving both breadth and depth of insights.

In-depth interviews provide the opportunity to look behind policy assumptions or how policies are designed from the top down, to examine how these play out in the lives of those affected by the policies and whose experiences might otherwise be obscured or ignored. For example, the Welfare Conditionality project used interviews to critique the assumptions that conditionality (such as, the withdrawal of social security benefits if recipients did not perform or meet certain criteria) improved employment outcomes and instead showed that conditionality was harmful to mental health, living standards and had many other negative consequences 56 . Meanwhile, combining datasets from two small-scale interview studies with recipients allowed Summers and Young to critique assumptions around the simplicity that underpinned the design of Universal Credit in 2020, for example, showing that the apparently simple monthly payment design instead burdened recipients with additional money management decisions and responsibilities 57 .

Similarly, the Welfare at a (Social) Distance project used a mixed-methods approach in a large-scale study that combined national surveys with case studies and in-depth interviews to investigate the experience of claiming social security benefits during the COVID-19 pandemic. The interviews allowed researchers to understand in detail any issues experienced by recipients of benefits, such as delays in the process of claiming, managing on a very tight budget and navigating stigma and claiming 58 .

These applications demonstrate the multi-faceted topics and questions for which interviews can be a relevant method for data collection. These applications highlight not only the relevance of interviews, but also emphasize the key added value of interviews, which might be missed by other methods (surveys, in particular). Interviews can expose and question what is taken for granted and directly engage with communities and participants that might otherwise be ignored, obscured or marginalized.

Reproducibility and data deposition

There is a robust, ongoing debate about reproducibility in qualitative research, including interview studies. In some research paradigms, reproducibility can be a way of interrogating the rigour and robustness of research claims, by seeing whether these hold up when the research process is repeated. Some scholars have suggested that although reproducibility may be challenging, researchers can facilitate it by naming the place where the research was conducted, naming participants, sharing interview and fieldwork transcripts (anonymized and de-identified in cases where researchers are not naming people or places) and employing fact-checkers for accuracy 11 , 59 , 60 .

In addition to the ethical concerns of whether de-anonymization is ever feasible or desirable, it is also important to address whether the replicability of interview studies is meaningful. For example, the flexibility of interviews allows for the unexpected and the unforeseen to be incorporated into the scope of the research 61 . However, this flexibility means that we cannot expect reproducibility in the conventional sense, given that different researchers will elicit different types of data from participants. Sharing interview transcripts with other researchers, for instance, downplays the contextual nature of an interview.

Drawing on Bauer and Gaskell, we propose several measures to enhance rigour in qualitative research: transparency, grounding interpretations and aiming for theoretical transferability and significance 62 .

Researchers should be transparent when describing their methodological choices. Transparency means documenting who was interviewed, where and when (without requiring de-anonymization, for example, by documenting their characteristics), as well as the questions they were asked. It means carefully considering who was left out of the interviews and what that could mean for the researcher’s findings. It also means carefully considering who the researcher is and how their identity shaped the research process (integrating and articulating reflexivity into whatever is written up).

Second, researchers should ground their interpretations in the data. Grounding means presenting the evidence upon which the interpretation relies. Quotes and extracts should be extensive enough to allow the reader to evaluate whether the researcher’s interpretations are grounded in the data. At each step, researchers should carefully compare their own explanations and interpretations with alternative explanations. Doing so systematically and frequently allows researchers to become more confident in their claims. Here, researchers should justify the link between data and analysis by using quotes to justify and demonstrate the analytical point, while making sure the analytical point offers an interpretation of quotes (Box  4 ).

An important step in considering alternative explanations is to seek out disconfirming evidence 4 , 63 . This involves looking for instances where participants deviate from what the majority are saying and thus bring into question the theory (or explanation) that the researcher is developing. Careful analysis of such examples can often demonstrate the salience and meaning of what appears to be the norm (see Table  2 for examples) 54 . Considering alternative explanations and paying attention to disconfirming evidence allows the researcher to refine their own theories in respect of the data.

Finally, researchers should aim for theoretical transferability and significance in their discussions of findings. One way to think about this is to imagine someone who is not interested in the empirical study. Articulating theoretical transferability and significance usually takes the form of broadening out from the specific findings to consider explicitly how the research has refined or altered prior theoretical approaches. This process also means considering under what other conditions, aside from those of the study, the researcher thinks their theoretical revision would be supported by and why. Importantly, it also includes thinking about the limitations of one’s own approach and where the theoretical implications of the study might not hold.

Box 4 An example of grounding interpretations in data (from Rao 34 )

In an article explaining how unemployed men frame their job loss as a pervasive experience, Rao writes the following: “Unemployed men in this study understood unemployment to be an expected aspect of paid work in the contemporary United States. Robert, a white unemployed communications professional, compared the economic landscape after the Great Recession with the tragic events of September 11, 2001:

Part of your post-9/11 world was knowing people that died as a result of terrorism. The same thing is true with the [Great] Recession, right? … After the Recession you know somebody who was unemployed … People that really should be working.

The pervasiveness of unemployment rendered it normal, as Robert indicates.”

Here, the link between the quote presented and the analytical point Rao is making is clear: the analytical point is grounded in a quote and an interpretation of the quote is offered 34 .

Limitations and optimizations

When deciding which research method to use, the key question is whether the method provides a good fit for the research questions posed. In other words, researchers should consider whether interviews will allow them to successfully access the social phenomena necessary to answer their question(s) and whether the interviews will do so more effectively than other methods. Table  3 summarizes the major strengths and limitations of interviews. However, the accompanying text below is organized around some key issues, where relative strengths and weaknesses are presented alongside each other, the aim being that readers should think about how these can be balanced and optimized in relation to their own research.

Breadth versus depth of insight

Achieving an overall breadth of insight, in a statistically representative sense, is not something that is possible or indeed desirable when conducting in-depth interviews. Instead, the strength of conducting interviews lies in their ability to generate various sorts of depth of insight. The experiences or views of participants that can be accessed by conducting interviews help us to understand participants’ subjective realities. The challenge, therefore, is for researchers to be clear about why depth of insight is the focus and what we should aim to glean from these types of insight.

Naturalistic or artificial interviews

Interviews make use of a form of interaction with which people are familiar 64 . By replicating a naturalistic form of interaction as a tool to gather social science data, researchers can capitalize on people’s familiarity and expectations of what happens in a conversation. This familiarity can also be a challenge, as people come to the interview with preconceived ideas about what this conversation might be for or about. People may draw on experiences of other similar conversations when taking part in a research interview (for example, job interviews, therapy sessions, confessional conversations, chats with friends). Researchers should be aware of such potential overlaps and think through their implications both in how the aims and purposes of the research interview are communicated to participants and in how interview data are interpreted.

Further, some argue that a limitation of interviews is that they are an artificial form of data collection. By taking people out of their daily lives and asking them to stand back and pass comment, we are creating a distance that makes it difficult to use such data to say something meaningful about people’s actions, experiences and views. Other approaches, such as ethnography, might be more suitable for tapping into what people actually do, as opposed to what they say they do 65 .

Dynamism and replicability

Interviews following a semi-structured format offer flexibility both to the researcher and the participant. As the conversation develops, the interlocutors can explore the topics raised in much more detail, if desired, or pass over ones that are not relevant. This flexibility allows for the unexpected and the unforeseen to be incorporated into the scope of the research.

However, this flexibility has a related challenge of replicability. Interviews cannot be reproduced because they are contingent upon the interaction between the researcher and the participant in that given moment of interaction. In some research paradigms, replicability can be a way of interrogating the robustness of research claims, by seeing whether they hold when they are repeated. This is not a useful framework to bring to in-depth interviews and instead quality criteria (such as transparency) tend to be employed as criteria of rigour.

Accessing the private and personal

Interviews have been recognized for their strength in accessing private, personal issues, which participants may feel more comfortable talking about in a one-to-one conversation. Furthermore, interviews are likely to take a more personable form with their extended questions and answers, perhaps making a participant feel more at ease when discussing sensitive topics in such a context. There is a similar, but separate, argument made about accessing what are sometimes referred to as vulnerable groups, who may be difficult to make contact with using other research methods.

There is an associated challenge of anonymity. There can be types of in-depth interview that make it particularly challenging to protect the identities of participants, such as interviewing within a small community, or multiple members of the same household. The challenge to ensure anonymity in such contexts is even more important and difficult when the topic of research is of a sensitive nature or participants are vulnerable.

Increasingly, researchers are collaborating in large-scale interview-based studies and integrating interviews into broader mixed-methods designs. At the same time, interviews can be seen as an old-fashioned (and perhaps outdated) mode of data collection. We review these debates and discussions and point to innovations in interview-based studies. These include the shift from face-to-face interviews to the use of online platforms, as well as integrating and adapting interviews towards more inclusive methodologies.

Collaborating and mixing

Qualitative researchers have long worked alone 66 . Increasingly, however, researchers are collaborating with others for reasons such as efficiency, institutional incentives (for example, funding for collaborative research) and a desire to pool expertise (for example, studying similar phenomena in different contexts 67 or via different methods). Collaboration can occur across disciplines and methods, cases and contexts and between industry/business, practitioners and researchers. In many settings and contexts, collaboration has become an imperative 68 .

Cheek notes how collaboration provides both advantages and disadvantages 68 . For example, collaboration can be advantageous, saving time and building on the divergent knowledge, skills and resources of different researchers. Scholars with different theoretical or case-based knowledge (or contacts) can work together to build research that is comparative and/or more than the sum of its parts. But such endeavours also carry with them practical and political challenges in terms of how resources might actually be pooled, shared or accounted for. When undertaking such projects, as Morse notes, it is worth thinking about the nature of the collaboration and being explicit about such a choice, its advantages and its disadvantages 66 .

A further tension, but also a motivation for collaboration, stems from integrating interviews as a method in a mixed-methods project, whether with other qualitative researchers (to combine with, for example, focus groups, document analysis or ethnography) or with quantitative researchers (to combine with, for example, surveys, social media analysis or big data analysis). Cheek and Morse both note the pitfalls of collaboration with quantitative researchers: that quality of research may be sacrificed, qualitative interpretations watered down or not taken seriously, or tensions experienced over the pace and different assumptions that come with different methods and approaches of research 66 , 68 .

At the same time, there can be real benefits of such mixed-methods collaboration, such as reaching different and more diverse audiences or testing assumptions and theories between research components in the same project (for example, testing insights from prior quantitative research via interviews, or vice versa), as long as the skillsets of collaborators are seen as equally beneficial to the project. Cheek provides a set of questions that, as a starting point, can be useful for guiding collaboration, whether mixed methods or otherwise. First, Cheek advises asking all collaborators about their assumptions and understandings concerning collaboration. Second, Cheek recommends discussing what each perspective highlights and focuses on (and conversely ignores or sidelines) 68 .

A different way to engage with the idea of collaboration and mixed methods research is by fostering greater collaboration between researchers in the Global South and Global North, thus reversing trends of researchers from the Global North extracting knowledge from the Global South 69 . Such forms of collaboration also align with interview innovations, discussed below, that seek to transform traditional interview approaches into more participatory and inclusive (as part of participatory methodologies).

Digital innovations and challenges

The ongoing COVID-19 pandemic has centred the question of technology within interview-based fieldwork. Although conducting synchronous oral interviews online — for example, via Zoom, Skype or other such platforms — has been a method used by a small constituency of researchers for many years, it became (and remains) a necessity for many researchers wanting to continue or start interview-based projects while COVID-19 prevents face-to-face data collection.

In the past, online interviews were often framed as an inferior form of data collection for not providing the kinds of (often necessary) insights and forms of immersion face-to-face interviews allow 70 , 71 . Online interviews do tend to be more decontextualized than interviews conducted face-to-face 72 . For example, it is harder to recognize, engage with and respond to non-verbal cues 71 . At the same time, they broaden participation to those who might not have been able to access or travel to sites where interviews would have been conducted otherwise, for example people with disabilities. Online interviews also offer more flexibility in terms of scheduling and time requirements. For example, they provide more flexibility around precarious employment or caring responsibilities without having to travel and be away from home. In addition, online interviews might also reduce discomfort between researchers and participants, compared with face-to-face interviews, enabling more discussion of sensitive material 71 . They can also provide participants with more control, enabling them to turn on and off the microphone and video as they choose, for example, to provide more time to reflect and disconnect if they so wish 72 .

That said, online interviews can also introduce new biases based on access to technology 72 . For example, in the Global South, there are often urban/rural and gender gaps between who has access to mobile phones and who does not, meaning that some population groups might be overlooked unless researchers sample mindfully 71 . There are also important ethical considerations when deciding between online and face-to-face interviews. Online interviews might seem to imply lower ethical risks than face-to-face interviews (for example, they lower the chances of identification of participants or researchers), but they also offer more barriers to building trust between researchers and participants 72 . Interacting only online with participants might not provide the information needed to assess risk, for example, participants’ access to a private space to speak 71 . Just because online interviews might be more likely to be conducted in private spaces does not mean that private spaces are safe, for example, for victims of domestic violence. Finally, online interviews prompt further questions about decolonizing research and engaging with participants if research is conducted from afar 72 , such as how to include participants meaningfully and challenge dominant assumptions while doing so remotely.

A further digital innovation, modulating how researchers conduct interviews and the kinds of data collected and analysed, stems from the use and integration of (new) technology, such as WhatsApp text or voice notes to conduct synchronous or asynchronous oral or written interviews 73 . Such methods can provide more privacy, comfort and control to participants and make recruitment easier, allowing participants to share what they want when they want to, using technology that already forms a part of their daily lives, especially for young people 74 , 75 . Such technology is also emerging in other qualitative methods, such as focus groups, with similar arguments around greater inclusivity versus traditional offline modes. Here, the digital challenge might be higher for researchers than for participants if they are less used to such technology 75 . And while there might be concerns about the richness, depth and quality of written messages as a form of interview data, Gibson reports that the reams of transcripts that resulted from a study using written messaging were dense with meaning to be analysed 75 .

Like with online and face-to-face interviews, it is important also to consider the ethical questions and challenges of using such technology, from gaining consent to ensuring participant safety and attending to their distress, without cues, like crying, that might be more obvious in a face-to-face setting 75 , 76 . Attention to the platform used for such interviews is also important and researchers should be attuned to the local and national context. For example, in China, many platforms are neither legal nor available 76 . There, more popular platforms — like WeChat — can be highly monitored by the government, posing potential risks to participants depending on the topic of the interview. Ultimately, researchers should consider trade-offs between online and offline interview modalities, being attentive to the social context and power dynamics involved.

The next 5–10 years

Continuing to integrate (ethically) this technology will be among the major persisting developments in interview-based research, whether to offer more flexibility to researchers or participants, or to diversify who can participate and on what terms.

Pushing the idea of inclusion even further is the potential for integrating interview-based studies within participatory methods, which are also innovating via integrating technology. There is no hard and fast line between researchers using in-depth interviews and participatory methods; many who employ participatory methods will use interviews at the beginning, middle or end phases of a research project to capture insights, perspectives and reflections from participants 77 , 78 . Participatory methods emphasize the need to resist existing power and knowledge structures. They broaden who has the right and ability to contribute to academic knowledge by including and incorporating participants not only as subjects of data collection, but as crucial voices in research design and data analysis 77 . Participatory methods also seek to facilitate local change and to produce research materials, whether for academic or non-academic audiences, including films and documentaries, in collaboration with participants.

In responding to the challenges of COVID-19, capturing the fraught situation wrought by the pandemic and the momentum to integrate technology, participatory researchers have sought to continue data collection from afar. For example, Marzi has adapted an existing project to co-produce participatory videos, via participants’ smartphones in Medellin, Colombia, alongside regular check-in conversations/meetings/interviews with participants 79 . Integrating participatory methods into interview studies offers a route by which researchers can respond to the challenge of diversifying knowledge, challenging assumptions and power hierarchies and creating more inclusive and collaborative partnerships between participants and researchers in the Global North and South.

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A pre-written interview outline for a semi-structured interview that provides both a topic structure and the ability to adapt flexibly to the content and context of the interview and the interaction between the interviewer and participant. Others may refer to the topic guide as an interview protocol.

Here we refer to the participants that take part in the study as the sample. Other researchers may refer to the participants as a participant group or dataset.

This involves dividing a population into smaller groups based on particular characteristics, for example, age or gender, and then sampling randomly within each group.

A sampling method where the guiding logic when deciding who to recruit is to achieve the most relevant participants for the research topic, in terms of being rich in information or insights.

Researchers ask participants to introduce the researcher to others who meet the study’s inclusion criteria.

Similar to stratified sampling, but participants are not necessarily randomly selected. Instead, the researcher determines how many people from each category of participants should be recruited. Recruitment can happen via snowball or purposive sampling.

A method for developing, analysing and interpreting patterns across data by coding in order to develop themes.

An approach that interrogates the explicit, implicit and taken-for-granted dimensions of language as well as the contexts in which it is articulated to unpack its purposes and effects.

A form of transcription that simplifies what has been said by removing certain verbal and non-verbal details that add no further meaning, such as ‘ums and ahs’ and false starts.

The analytic framework, theoretical approach and often hypotheses, are developed prior to examining the data and then applied to the dataset.

The analytic framework and theoretical approach is developed from analysing the data.

An approach that combines deductive and inductive components to work recursively by going back and forth between data and existing theoretical frameworks (also described as an iterative approach). This approach is increasingly recognized not only as a more realistic but also more desirable third alternative to the more traditional inductive versus deductive binary choice.

A theoretical apparatus that emphasizes the role of cultural processes and capital in (intergenerational) social reproduction.

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The Guide to Interview Analysis

• What is Interview Analysis?
• Advantages of Interviews in Research
• Disadvantages of Interviews in Research
• Ethical Considerations in Interviews
• Preparing a Research Interview
• Recruitment & Sampling for Research Interviews
• Interview Design
• How to Formulate Interview Questions
• Rapport in Interviews
• Social Desirability Bias
• Interviewer Effect
• Types of Research Interviews
• Face-to-Face Interviews
• Focus Group Interviews
• Email Interviews
• Telephone Interviews
• Stimulated Recall Interviews
• Interviews vs. Surveys
• Interviews vs Questionnaires
• Interviews and Interrogations
• How to Transcribe Interviews?
• Verbatim Transcription
• Clean Interview Transcriptions
• Manual Interview Transcription
• Automated Interview Transcription
• How to Annotate Research Interviews?
• Formatting and Anonymizing Interviews

Introduction

Methods for interview analysis, thematic analysis, narrative analysis, grounded theory, content analysis, discourse analysis, framework analysis, phenomenological analysis, less common methods of analyzing interview data, how can atlas.ti help with interview data analysis.

• Coding Interviews
• Reporting & Presenting Interview Findings

Analyzing interviews

Without the interview analysis process, qualitative research is impossible. Interviews are the most common data collection method that qualitative researchers use. This article focuses on different methods of analyzing qualitative data from interviews.

Analyzing interviews in qualitative research is like unravelling a tapestry of human experience—each thread representing a unique perspective, thought, or emotion. Interviews provide rich, in-depth data that offer researchers unparalleled insight into participants' lived experiences, opinions, and values. However, analyzing qualitative data effectively requires more than just listening; it demands a systematic approach to uncover the deeper meaning embedded in the words. The different methods of qualitative interview data analysis are key to transforming raw conversations into meaningful insights.

From identifying patterns through qualitative interviews to employing techniques for analyzing qualitative interview data, these methods help researchers break down complex information into manageable parts. The coding process is particularly vital, allowing researchers to tag and categorize data, transforming it into something that can be analyzed and interpreted. By coding qualitative data, researchers can identify themes and key data segments that reveal underlying trends and meanings. Additionally, the use of more modern approaches like sentiment analysis adds another layer to the interpretation, helping to gauge emotions and reactions embedded in responses.

In qualitative research, interview analysis methods are essential for uncovering the deeper meanings, patterns, and insights within participants' responses. From thematic analysis, which identifies common themes across interviews, to grounded theory, which builds new theories from the data, each method offers a unique approach. The choice of method depends on the research objectives, the nature of the data, and the type of insights the researcher aims to achieve. Interview analysis methods enable a structured yet flexible interpretation of qualitative data, providing valuable findings that contribute to the broader research landscape.

Thematic analysis is one of the most commonly used methods in qualitative research for identifying, analyzing, and reporting patterns or themes within data. It allows researchers to distill data from interview transcripts or other qualitative sources to find commonalities and trends, offering rich interpretations. Braun and Clarke (2006) described thematic analysis as a flexible method that can be applied across a variety of research paradigms and is suitable for identifying themes that span across the dataset. The method is widely used because it provides a systematic way to capture patterns in participants' perspectives.

Familiarization with the data

The researcher first becomes deeply familiar with the data by repeatedly reading through the interview transcripts, listening to recordings, and taking initial notes. This phase is crucial for immersion in the dataset, allowing the researcher to start forming initial impressions and to understand the data context.

Generating initial codes

The researcher then systematically codes the data by assigning labels or short phrases to segments of text that capture significant or recurring ideas. Each code represents an important concept or observation, and this step helps to break the data down into manageable, categorized parts.

Searching for themes

Once the data has been coded, the researcher reviews the codes to find overarching themes or patterns. This involves grouping related codes together and identifying broader topics that are emerging across the data set. Themes go beyond the codes to capture larger patterns of meaning.

Reviewing and refining themes

At this stage, themes are checked against the coded data and the entire data set to ensure they are comprehensive and accurately represent the data. The researcher refines the themes, combining or breaking them apart as needed to best capture the key insights from the data.

Defining and naming themes

Once the themes are reviewed, they are defined and named to reflect the essence of the data they represent. The researcher writes detailed descriptions of each theme, explaining its relevance to the research question and supporting it with data excerpts.

Writing up the report

The final phase involves writing the report, which includes a detailed narrative explaining how the themes were identified, what they mean, and how they answer the research question. The write-up integrates direct quotes from participants and a synthesis of the thematic findings.

Narrative analysis is a method that focuses on the stories participants tell, and how those stories are structured to make sense of their experiences. Riessman (2008) emphasized that narrative analysis goes beyond the content of what is told to include how it is told and the contexts that shape these stories. This method is particularly useful for exploring personal or collective stories, identity formation, and meaning-making processes.

Transcription and structuring

The researcher first transcribes the interviews, paying close attention not just to what is said, but also to how it is said. The transcription process captures the narrative flow, and the researcher begins to identify key structural elements such as the beginning, middle, and end of the story, as well as turning points or critical events.

Thematic and functional analysis

After transcribing the data, the researcher analyzes the content of the stories for recurring themes or topics that are central to the participants’ narratives. The functional aspect of narrative analysis focuses on why the story is being told in a particular way and what function the narrative serves in the context of the participant's life or identity.

Comparative analysis

When analyzing multiple narratives, researchers look for similarities and differences in how participants construct their stories. By comparing narratives, the researcher can identify common experiences or perspectives that reveal broader patterns in the data.

Interpretation and storytelling

The final step involves interpreting the narratives, weaving together insights about how participants make sense of their experiences. The researcher may highlight specific narrative strategies used by participants, such as how they justify or explain their actions, to provide deeper insights into their lived experiences.

Grounded theory, as introduced by Glaser and Strauss (1967), involves building theory from the data itself, rather than testing existing theories. Charmaz (2006) later contributed a constructivist approach to grounded theory.. Grounded theory is particularly valuable when there is no pre-existing theory to explain the phenomenon under study, or when the researcher aims to construct a new theory.

Open coding

In the open coding phase, the researcher breaks down the interview data into small, discrete parts and codes them based on the concepts or ideas present. This initial coding is highly detailed and serves to identify as many concepts as possible in the data.

Axial coding

After open coding, axial coding involves relating the codes identified during open coding to one another. This process organizes the codes into categories, identifying relationships between them to create a more structured understanding of the data. The goal is to find how the categories relate and what central themes are emerging.

Selective coding

Selective coding focuses on identifying the core category that integrates all the other categories. The researcher develops this core category into the central theme or theory of the study. Selective coding is essential for constructing a theory that explains the data comprehensively.

Theoretical sampling and saturation

As the theory emerges, the researcher collects more data to refine the theory, a process known as theoretical sampling. Data collection continues until theoretical saturation is reached, meaning no new themes or insights are emerging from the data, and the theory is fully developed.

Theory development

In the final stage, the researcher presents the theory that has been generated from the data. This theory is grounded in the data and provides an explanatory framework for the phenomenon under study, offering new insights or understanding.

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Content analysis is a systematic method used to quantify and analyze the presence of particular words, phrases, themes, or concepts within textual data. Krippendorff (1980) defined content analysis as a replicable technique for making inferences from data to its context, allowing for the systematic examination of large amounts of qualitative data. This method is especially useful when researchers need to objectively measure the frequency of certain themes or patterns in the data.

Data preparation

The researcher begins by preparing the data for analysis, typically through transcription if working with interview data. This step involves organizing and cleaning the data to ensure consistency across the text.

Developing categories and coding frame

Next, the researcher develops a coding frame, a list of predefined categories or themes based on the research questions or theoretical framework. Each category is defined, and clear criteria are established for how segments of the data will be assigned to each category.

Coding the text

The researcher systematically applies the coding frame to the data, tagging segments of text that correspond to each category. Coding can be done manually or with the help of software, depending on the size of the dataset.

Quantifying and interpreting

In quantitative content analysis, the researcher counts the frequency of each code or theme to identify patterns and trends. These frequencies are then interpreted to draw conclusions about which themes are most prominent in the data, or how different themes relate to one another.

Drawing conclusions and reporting

The final step involves interpreting the coded data and presenting the findings in a report. The researcher discusses the significance of the most frequently occurring themes, making inferences based on the data patterns and their relevance to the research questions.

Discourse analysis examines how language is used to construct social realities and relationships. Fairclough (1992) emphasized that discourse is not just a reflection of social practices but also actively shapes them. Discourse analysis explores power relations, ideologies, and social contexts through the analysis of language, offering insight into how social structures and power dynamics are produced and maintained through communication.

Transcription and contextualization

The first step in discourse analysis involves transcribing interviews or conversations, with attention to both verbal and non-verbal communication. Researchers also consider the broader social or institutional context in which the discourse occurs, as this context plays a significant role in shaping language use.

Identifying discourses

Once transcribed, the researcher identifies specific discourses or ways of talking about the subject matter that are present in the text. These discourses reflect particular worldviews, ideologies, or power relations, and the researcher examines how they influence the understanding of the topic.

Analyzing language use

The researcher focuses on the language features used in the discourse, such as word choice, metaphors, tone, and sentence structure. These features reveal how social norms, power dynamics, and ideologies are embedded in everyday language.

Examining power and ideology

Discourse analysis looks at how language reinforces or challenges power relations and social hierarchies. Researchers explore how certain discourses privilege particular viewpoints or marginalize others, shedding light on the role of language in maintaining social order.

Interpretation and implications

In the final phase, the researcher interprets the findings and discusses the implications of the discourses identified. The analysis might reveal how language contributes to social inequalities, or how it is used to assert authority or legitimize certain practices within a given context.

Framework analysis can be used when research has predefined objectives or questions. Initially developed by Ritchie and Spencer (2003), it is widely used in applied research such as policy analysis or healthcare studies. The approach is structured and transparent, making it well-suited for large datasets. Framework analysis allows for the organization of data into themes and sub-themes within a matrix, ensuring the data is rigorously examined and aligned with the research objectives. The use of matrices ensures that the data is organized both thematically and by case, providing a clear structure for comparison.

Familiarization

In the first step, the researcher becomes immersed in the data by thoroughly reading through the transcripts, field notes, or documents. This initial phase is crucial for gaining a comprehensive understanding of the data. Researchers often take notes and highlight initial ideas that appear significant.

Identifying a thematic framework

After familiarization, a thematic framework is developed. This framework emerges from the key research questions, theory, or preliminary findings during familiarization. The framework acts as a guide for analyzing the data and is made up of important themes and sub-themes.

The researcher applies the thematic framework to the data by indexing, or coding, sections of the data according to relevant themes. The indexing process links specific portions of the data with relevant themes from the framework, ensuring each section of data is categorized systematically.

In this phase, the data is charted into a matrix. The matrix organizes data according to themes (rows) and individual cases or participants (columns), creating a structured format that enables easy comparison across different cases. The charting process involves summarizing data from each case under the appropriate thematic headings.

Mapping and interpretation

The final stage involves analyzing the data within the framework, identifying patterns, relationships, and contrasts across the themes and cases. Researchers map out the key findings, drawing conclusions based on the thematic framework and using the matrix to ensure that all relevant aspects of the data are accounted for. This stage is crucial for developing insights that answer the research questions.

Phenomenological analysis is a method used to explore and understand individuals' lived experiences. It is grounded in the philosophical tradition of phenomenology, which seeks to uncover the essence of experiences. van Manen (1990)´s approach is valuable when the research focuses on how individuals experience and perceive specific phenomena, such as illness, loss, or life transitions. The analysis aims to capture the richness of these experiences, distilling them into essential themes that describe the core essence of the phenomenon.

The researcher begins by bracketing or setting aside their preconceived notions and assumptions about the phenomenon being studied. This is essential to ensure that the analysis focuses solely on the participants' experiences without the researcher influencing the interpretation.

Immersion in the data

The researcher reads and re-reads the interview transcripts, fully absorbing the participants' descriptions of their experiences. During this phase, the researcher remains attentive to significant phrases or sentences that stand out as key to understanding the participants' experiences.

Identifying themes

Key themes are extracted from the data that reflect essential aspects of the participants' experiences. These themes often relate to emotions, perceptions, and reactions to specific situations, and they represent the commonalities across different participants' accounts.

Synthesizing themes

After identifying themes, the researcher synthesizes them into a coherent description that captures the core essence of the lived experience. This involves interpreting the meaning behind each theme and understanding how the themes interrelate.

Writing the findings

The final report provides a rich, detailed account of the phenomenon, highlighting the essential themes and using participant quotes to illustrate key points. The goal is to present the findings in a way that conveys the depth and complexity of the participants' experiences.

Conversational analysis

Conversational analysis (CA) is a method used to examine the structure and pattern of talk in social interactions. Developed by Sacks, Schegloff, and Jefferson (1974), it focuses on understanding how conversations unfold in real-time, analyzing the turn-taking mechanisms, repairs, and sequences within dialogue. CA aims to uncover the implicit rules and social norms that govern everyday conversation. This method is particularly useful for studying the micro-interactions between individuals, such as those that occur during interviews, debates, or casual conversations.

Transcription

The researcher begins by transcribing the conversation in detail, paying close attention to pauses, interruptions, overlaps, tone, and other non-verbal cues. The transcription is usually highly detailed, with symbols used to indicate timing, emphasis, and changes in speech patterns.

Turn-taking analysis

A key focus of CA is understanding how speakers manage turn-taking during conversations. The researcher examines how participants signal when they wish to speak, how they take turns, and how they yield the floor to others. This analysis reveals the social norms guiding conversational flow.

Sequence organization

Conversations often follow particular sequences (e.g., question-answer pairs, greetings, and responses). Researchers analyze how these sequences unfold and how participants maintain or disrupt the expected flow of interaction. This includes analyzing how meaning is constructed through these sequences.

Repair mechanisms

When misunderstandings or conversational breakdowns occur, participants often use repair strategies to fix the interaction. Researchers examine how participants address these issues, looking for patterns in how they clarify, repeat, or rephrase their speech.

Conclusion and social implications

Based on the analysis, the researcher draws conclusions about the social norms and rules governing conversational interactions. This analysis helps to reveal broader social structures and expectations reflected in everyday communication.

Interpretative phenomenological analysis (IPA)

Interpretative phenomenological analysis (IPA) is a qualitative research method that aims to explore how individuals make sense of their personal and social experiences. It is rooted in phenomenology and hermeneutics, focusing on understanding the meaning behind lived experiences. Smith, Flowers, and Larkin (2009) have been instrumental in developing IPA as a research method, emphasizing its use in psychology, health, and education. IPA is particularly useful for studies that explore subjective experiences, such as coping with illness or navigating personal challenges, and it allows for a deep, interpretative understanding of the data.

Reading and immersion

The researcher starts by immersing themselves in the interview transcripts, carefully reading and reflecting on the participants’ accounts. This step ensures that the researcher is fully engaged with the data before moving to analysis.

Initial coding

The researcher identifies significant elements within the data by applying codes to sections of the transcript. These codes capture the meaning of the participants' experiences in a concise form and are the foundation for later theme development.

Theme development

After coding, the researcher looks for patterns or themes across the data. Themes reflect the participants’ key concerns and meanings, and they help to explain how participants make sense of their experiences. Themes are grouped and structured to reflect their relationships.

Interpretation

The researcher moves beyond describing themes to interpret the deeper meaning behind them. This interpretative process involves understanding the participants' lived experiences within their broader social and psychological contexts.

Synthesis and reporting

The final stage involves writing a report that weaves together themes and interpretations, providing a detailed and nuanced account of how participants experience and make sense of their world. Direct quotes from participants are used to illustrate key themes.

ATLAS.ti is an advanced qualitative analysis software designed to help researchers efficiently manage and analyze interview data. Whether you're working alone or in a team, with text, audio, or video formats, ATLAS.ti provides a wide array of tools that facilitate the coding and interpretation process.

Import interview transcripts and recordings : Researchers can easily import interview data and begin identifying key themes by highlighting relevant segments of text or annotating timestamps in media files.

Write reflections and notes in memos : One of the most important tools for researchers are memos, where you can document insights, reflections, and emerging ideas throughout the analysis process, ensuring that important observations are captured.

Code your data according to your chosen methodology : Coding and analyzing data in ATLAS.ti is extremely flexible, so you can follow whichever methodology is best suited to your research. You can highlight any segment of data, write notes, and attach codes. Create your own codes, use in vivo codes, easily re-use already existing codes, or even explore AI-suggested codes.

Use AI as a virtual assistant : ATLAS.ti also includes a wide range of AI-driven tools, which can automate repetitive tasks and offer a different perspective into the data.

Intentional AI coding : Automatically generates descriptive codes by analyzing your interview transcripts. It helps identify key themes and phrases.

Conversational AI : Ask any questions about your data using natural language, and the AI-driven chatbot will provide answers based on your selected data. It will also show you the segments of data on which its answers are based, so you can easily explore your whole dataset.

Sentiment analysis : This tool analyzes the emotional tone of the text, automatically coding positive, negative, and neutral sentiments within your interview data. It helps researchers quickly assess participant attitudes and reactions.

Named entity recognition (NER) : AI identifies key entities such as names, organizations, and locations within your text, offering a faster way to code significant elements in the interviews​.

Concept detection : Automatically highlights relevant concepts or keywords, presenting a word cloud that helps in understanding the main topics and sub-themes of the interview.

Opinion mining : This feature analyzes opinions and sentiments related to identified concepts, helping to dig deeper into participant views and attitudes.

Visualize your data and analysis in networks : Use networks to brainstorm ideas, build conceptual frameworks, or simply draw out the story of your research. Any part of your project can be visualized in a network, including data quotations, code frequencies, and your notes.

Display your data to explore overarching patterns : Examine frequencies of codes across your data and dig into patterns of co-occurrences among your codes with tools that display your data in tables, graphs, and Sankey diagrams. You can explore the bigger picture of your research and develop more nuanced insights.

Interview analysis lies at the heart of qualitative research, transforming raw conversations into rich, meaningful insights that shape our understanding of human experiences. By employing structured and systematic approaches such as thematic analysis, grounded theory, or narrative analysis, researchers can uncover the underlying patterns and themes within qualitative data. These methods help analyze qualitative data and allow for deeper exploration of individual perspectives and collective experiences, contributing valuable findings to the research landscape. Tools like ATLAS.ti further enhance this process, simplifying the coding process, helping to identify themes, and providing robust frameworks for analyzing qualitative interview data. Whether the goal is to generate theory or reveal hidden sentiments through sentiment analysis, effective interview analysis is essential for translating complex human interactions into clear, actionable research outcomes.

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