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Introduction, what are the aims of the research project, what level of detail is required, who should do the transcribing, what contextual detail is necessary to interpret data, how should data be represented, what equipment is needed, declaration.

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First steps in qualitative data analysis: transcribing

Bailey J. First steps in qualitative data analysis: transcribing. Family Practice 2008; 25: 127–131.

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Julia Bailey, First steps in qualitative data analysis: transcribing, Family Practice , Volume 25, Issue 2, April 2008, Pages 127–131, https://doi.org/10.1093/fampra/cmn003

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Qualitative research in primary care deepens understanding of phenomena such as health, illness and health care encounters. Many qualitative studies collect audio or video data (e.g. recordings of interviews, focus groups or talk in consultation), and these are usually transcribed into written form for closer study. Transcribing appears to be a straightforward technical task, but in fact involves judgements about what level of detail to choose (e.g. omitting non-verbal dimensions of interaction), data interpretation (e.g. distinguishing ‘I don't, no’ from ‘I don't know’) and data representation (e.g. representing the verbalization ‘hwarryuhh’ as ‘How are you?’).

Representation of audible and visual data into written form is an interpretive process which is therefore the first step in analysing data. Different levels of detail and different representations of data will be required for projects with differing aims and methodological approaches. This article is a guide to practical and theoretical considerations for researchers new to qualitative data analysis. Data examples are given to illustrate decisions to be made when transcribing or assigning the task to others.

Qualitative research can explore the complexity and meaning of social phenomena, 1 , 2 for example patients' experiences of illness 3 and the meanings of apparently irrational behaviour such as unsafe sex. 4 Data for qualitative study may comprise written texts (e.g. documents or field notes) and/or audible and visual data (e.g. recordings of interviews, focus groups or consultations). Recordings are transcribed into written form so that they can be studied in detail, linked with analytic notes and/or coded. 5

Word limits in medical journals mean that little detail is usually given about how transcribing is actually done. Authors' descriptions in papers convey the impression that transcribing is a straightforward technical task, summed up using terms such as ‘verbatim transcription’. 6 However, representing audible talk as written words requires reduction, interpretation and representation to make the written text readable and meaningful. 7 , 8 This article unpicks some of the theoretical and practical decisions involved in transcribing, for researchers new to qualitative data analysis.

Researchers' methodological assumptions and disciplinary backgrounds influence what are considered relevant data and how data should be analysed. To take an example, talk between hospital consultants and medical students could be studied in many different ways: the transcript of a teaching session could be analysed thematically, coding the content (topics) of talk. Analysis could also look at the way that developing an identity as a doctor involves learning to use language in particular ways, for example, using medical terminology in genres such as the ‘case history’. 9 The same data could be analysed to explore the construction of ‘truth’ in medicine: for example, a doctor saying ‘the patient's blood pressure is 120/80’ frames this statement as an objective, quantifiable, scientific truth. In contrast, formulating a patient's medical history with statements such as ‘she reports a pain in the left leg’ or ‘she denies alcohol use’ frames the patient's account as less trustworthy than the doctor's observations. 10 The aims of a project and methodological assumptions have implications for the form and content of transcripts since different features of data will be of analytic interest. 7

Making recordings involves reducing the original data, for example, selecting particular periods of time and/or particular camera angles. Selecting which data have significance reflects underlying assumptions about what count as data for a particular project, for example, whether social talk at the beginning and end of an interview is to be included or the content of a telephone call which interrupts a consultation.

Visual data

Verbal and non-verbal interaction together shape communicative meaning. 11 The aims of the project should dictate whether visual information is necessary for data interpretation, for example, room layout, body orientation, facial expression, gesture and the use of equipment in consultation. 12 However, visual data are more difficult to process since they take a huge length of time to transcribe, and there are fewer conventions for how to represent visual elements on a transcript. 5

Capturing how things are said

The meanings of utterances are profoundly shaped by the way in which something is said in addition to what is said. 13 , 14 Transcriptions need to be very detailed to capture features of talk such as emphasis, speed, tone of voice, timing and pauses but these elements can be crucial for interpreting data. 7

Dr 9: I would suggest yes paracetamol is a good symptomatic treatment, and you'll be fine Pt K: fine, okay, well, thank you very much.

Dr 9: (..) I would suggest (..) yes paracetamol or ibuprofen is a good (..) symptomatic treatment (..) um (.) (slapping hands on thighs) and you'll be fine Pt K: fine (..) okay (.) well (..) (shrugging shoulders and laughing) thank you very much

In the second representation of this interaction, both speakers pause frequently. The doctor slaps his thigh and uses the idiom ‘you'll be fine’ to wrap up his advice giving. In response, Patient K is hesitant and he uses the mitigation ‘well’, shrugs his shoulders and laughs, suggesting turbulence or difficulty in interaction. 15 Although the patient's words seem to indicate agreement, the way these words are said seem to indicate the opposite. 16

Dr 5: So let's just go back to this. So, so you've had this for a few weeks Pt F: yes

Dr 5: .hhh so let's just go back to this (.) so (..) so you've had this for a few w ee ks Pt F: yes (1.0) (left hand on throat, stroking with fingers)

Dr 5: I must ask you (.) why have you come in tod a y because it is a Saturday morning (1.0) it's for u rgent cases only that really have just st a rted Pt F: Yes because it has been troubling me since last last night (left hand still on neck)

This more detailed level of transcribing facilitates analysis of the social relationship between doctor and patient; in this example, the consequences for the doctor–patient interaction of consulting in an urgent surgery with ‘minor’ symptoms. 16

Data must inevitably be reduced in the process of transcribing, since interaction is hugely complex. Decisions therefore need to be made about which features of interaction to transcribe: the level of detail necessary depends upon the aims of a research project, and there is a balance to be struck between readability and accuracy of a transcript. 18

Transcribing is often delegated to a junior researcher or medical secretary for example, but this can be a mistake if the transcriber is inadequately trained or briefed. Transcription involves close observation of data through repeated careful listening (and/or watching), and this is an important first step in data analysis. This familiarity with data and attention to what is actually there rather than what is expected can facilitate realizations or ideas which emerge during analysis. 1 Transcribing takes a long time (at least 3 hours per hour of talk and up to 10 hours per hour with a fine level of detail including visual detail) 5 and this should be allowed for in project time plans, budgeting for researchers’ time if they will be doing the transcribing.

Recordings may be difficult to understand because of the recording quality (e.g. quiet volume, overlaps in speech, interfering noise) and differing accents or styles of speech. Utterances are interpretable through knowledge of their local context (i.e. in relation to what has gone before and what follows), 8 for example, allowing differentiation between ‘I don't, no’ and ‘I don't know'. Interaction is also understood in wider context such as understanding questions and responses to be part of an ‘interview’ or ‘consultation’ genre with particular expectations for speaker roles and the form and content of talk. 19 For example, the question ‘how are you?’ from a patient in consultation would be interpreted as a social greeting, while the same question from a doctor would be taken as an invitation to recount medical problems. 14 Contextual information about the research helps the transcriber to interpret recordings (if they are not the person who collected the data), for example, details about the project aims, the setting and participants and interview topic guides if relevant.

Dr 1: so what are your symptoms since yesterday (..) the aches Pt B: aches ere (..) in me arm (..) sneezing (..) edache Dr 1: ummm (..) okay (..) and have you tried anything for this (.) at all? Pt B: no (..) I ain't a believer of me- (.) medicine to tell you the truth

Although this attempts to represent linguistic variety, using a more literal spelling is difficult to read and runs the risk of portraying respondents as inarticulate and/or uneducated. 20 Even using standard written English, transcribed talk appears faltering and inarticulate. For example, verbal interaction includes false starts, repetitions, interruptions, overlaps, in- and out-breaths, coughs, laughs and encouraging noises (such as ‘mm’), and these features may be omitted to avoid cluttering the text. 18

If talk is mediated via an interpreter, decisions must be made about how to represent translation on a transcript, 8 for example, whether to translate ‘literally’, and then to interpret the meaning in terms of the second language and culture. For example, from French to English, ‘j'ai mal au coeur’ translates literally as ‘I have bad in the heart’, interpreted in English as ‘I feel sick’. Translation therefore adds an additional layer of interpretation to the transcribing process.

Written representations reflect researchers’ interpretations. For example, laughter could be transcribed as ‘he he he', ‘laughter (2 seconds)’, ‘nervous laughter’, ‘quiet laughter’ or ‘giggling’ and these representations convey different interpretations. The layout on paper and labelling also reflect analytic assumptions about data. 20 For example, labelling speakers as ‘patient’ and ‘doctor’ implies that their respective roles in a medical encounter are more salient than other attributes such as ‘man’, ‘mother’, ‘Spanish speaker’ or ‘advice giver’. Talk is often presented in speech turns, with a new line for the next speaker (as in the data examples given), but could also be laid out in a timeline, in columns or in stanzas like poetry, for example. 7 Transcripts are not therefore neutral records of events, but reflect researchers’ interpretations of data.

Presenting quotations in a research paper involves further steps in reduction and representation through the choice of which data to present and what to highlight. There is debate about what counts as relevant context in qualitative research. 21 , 22 For example, studies usually describe the setting in which data were collected and demographic features of respondents such as their age and gender, but relevant contextual information could also include historical, political and policy context, participants’ physical appearance, recent news events, details of previous meetings and so on. 23 Authors’ decisions on which data and what contextual information to present will lead to different framing of data.

Decisions about the level of detail needed for a project will inform whether video or audio recordings are needed. 24 Taking notes instead of making recordings is not sufficiently accurate or detailed for most qualitative projects. Digital audio and video recorders are rapidly replacing analogue equipment: digital recordings are generally better quality, but require computer software to store and process, and digital video files take up huge quantities of computer memory. It is usually necessary to playback recordings repeatedly: a foot-controlled transcription machine facilitates this for analogue audio tapes (see Fig. 1 ) and transcribing software is recommended for digital audio or video files, since this allows synchronous playback and typing (see Fig. 2 ).

Analogue audio recording equipment: dictaphone with microphone and mini-cassette tape and foot-pedal controlled transcription machine with headphones

Digital video recording equipment: video camera with firewire computer lead, mini DV cassette and Transana transcribing software

Representation of audible and visible data into written form is an interpretive process which involves making judgments and is therefore the first step in analysing data. Decisions about transcribing are guided by the methodological assumptions underpinning a particular research project, and there are therefore many different ways to transcribe the same data. Researchers need to decide which level of transcription detail is required for a particular project and how data are to be represented in written form.

Transcribing is an interpretive act rather than simply a technical procedure, and the close observation that transcribing entails can lead to noticing unanticipated phenomena. It is impossible to represent the full complexity of human interaction on a transcript and so listening to and/or watching the ‘original’ recorded data brings data alive through appreciating the way that things have been said as well as what has been said.

Funding: Primary Care Researcher Development award, Department of Health National Coordinating Centre for Research Capacity Development.

Ethical approval: East London and the City Ethical Committee.

Conflict of interest: None.

This paper derives from a PhD thesis written by Julia Bailey entitled ‘Doctor-patient consultations for upper respiratory tract infections: a discourse analysis’, which was supervised by Celia Roberts, Roger Jones and Jane Barlow. Thanks are due to doctors and patients who participated in the project, to practice staff, and to Anne Rouse for her advice on the practicalities of transcribing.

Transcription Conventions

(?) talk too obscure to transcribe.

Hhhhh audible out-breath

.hhh in-breath

[ overlapping talk begins

] overlapping talk ends

(.) silence, less than half a second

(..) silence, less than one second

(2.8) silence measured in 10 ths of a second

:::: lengthening of a sound

Becau- cut off, interruption of a sound

he says. Emphasis

= no silence at all between sounds

LOUD sounds

? rising intonation

(left hand on neck) body conduct

[notes, comments]

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Author notes

• consultation
• primary health care
• qualitative research
• interpretation of findings

Julia Bailey’s article on transcription of qualitative research data caught my attention because she gives the reader valuable advice regarding the theoretical and practical decisions involved in the process of transcription. For example, she emphasized the importance of focusing on the aim of the research project, on proper reduction of original data, on capturing the meaning of verbal and non-verbal interaction, and on the influence of the researcher’s interpretation of raw data on the outcome of the study (1).

Transcription is indeed a crucial process in any qualitative research project as it is the first step in the analysis of raw data. I agree with Bailey that investigators should be very careful with handling this process. I would like to add a few thoughts about the transcription process by reviewing some additional literature sources and by adding a few of my own experiences.

Marshall and Rossman (2) pointed out that we do not speak in paragraphs and do not give signals to researchers about punctuation during a conversation. Thus, transcribing qualitative data is challenging because the transcriber makes judgments and shapes the meaning of the written words. Sofaer (3) emphasized that the analysis and interpretation process should be deliberate and thorough in order to avoid the use of initial impressions. Bradley and Curry (4) discussed the importance of formatting and suggest the labeling of transcripts with a systematic file name and inserting line-numbering so that communication among members of the analysis team is easier, particularly when certain sections of an interview are being discussed later. They also suggest that once a transcript has been prepared, it should be read closely to gain a general understanding of the data. I found it personally quite helpful to read out loud my self-prepared transcripts for several times, which significantly improved my understanding of the qualitative data and also facilitated the subsequent development of coding categories.

Lichtman (5) discussed the issue of transcribing research data collected from a focus group interview with many people (e.g., 10 different voices). She pointed to the difficulty in transcribing those raw data because some people may speak at the same time, some may interrupt, and others may be talking so quietly that it is difficult to understand them. A solution to this problem is to listen and then extract themes rather than to attempt distinguishing one voice from another. I believe this is a good idea.

Transcribing recorded qualitative data is time-consuming and can be quite costly. Most literature sources I read indicate that self- transcribing original data has advantages over hiring a professional transcriber. However, this may not always be possible, particularly when large data sets need to be processed. Seidman (6) pointed out that an advantage of transcribing own tapes is that the investigator comes to know his/her interviews better. In case someone else is hired to transcribe the raw data, Bogdan and Biklen (7) suggest that the investigator should work closely together with the transcriber in order to make sure that the transcription is accurate. More specifically, when a professional transcriber is hired, the investigator should have prepared detailed written instructions for this person. As Seidman (6) puts it: “Writing out the instructions will improve the consistency of the process, encourage the researchers to think through all that is involved, and allow them to share their decision making with their readers at a later point.”

Another important issue relates to the length of the transcripts. Should everything be transcribed or only certain sections of it? Seidman (6) does not recommend preselecting particular parts of the tape for transcription and omitting others because this could lead to premature judgments about what is important and what is not. I have tried out both ways and came to the same conclusion.

The term “transcription” is well known in biology. In this scientific discipline, it relates to “gene-transcription,” a process that can be defined as using the DNA as a template in order to make RNA strands (the transcripts) from it (8). If the genes encoded in the DNA are not accurately transcribed, the deciphering of the transcripts will be difficult and may result in improper protein synthesis. This, in turn, can significantly impact cellular functions. I suggest that we recognize the significance of “accurate gene transcription” in biology and adopt it to the field of qualitative research. Accurate “qualitative data transcription” will allow us to obtain a readable text that has important meaning and can help us solve complex social phenomena, including those related to medicine, public health, and education.

1. Bailey J. First steps in qualitative data analysis: transcribing. Fam Pract. 2008; 25: 127-131.

2. Marshall C, Rossman GB. Designing Qualitative Research. 4th edn. Thousand Oaks, CA: Sage Publications, 2006.

3. Sofaer S. Qualitative research methods. Int J Qual Health Care. 2002; 14: 329-336.

4. Bradley EH, Curry LA. Codes to theory: a critical stage in qualitative analysis. In: Curry L, Shield R, Wetle T (eds.). Improving Aging and Public Health Research: Qualitative and Mixed Methods. Washington, DC: American Public Health Association, 2006: 91-102.

5. Lichtman M. Qualitative Research in Education: A User’s Guide. Thousand Oaks, CA: Sage Publications, 2006.

6. Seidman I. Interviewing as Qualitative Research: A Guide for Researchers in Education and the Social Sciences. 3rd edn. New York, NY: Teachers College Press, 2006.

7. Bogdan RC, Biklen SK. Qualitative Research for Education: An Introduction to Theories and Methods. 5th edn. Boston, MA: Pearson Education, 2007.

8. Starr C. Basic Concepts in Biology. 6th edn. Belmont, CA: Thomson Brook/Cole, 2006.

Conflict of Interest:

None declared

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How to Transcribe Interviews for Qualitative Research

Saving time and effort with Notta, starting from today!

Collecting numerical data is as easy as clicking copy and paste. But what about the unique feedback, comments, and descriptions of qualitative data? Not so much. Once you’ve figured out your research objectives and conducted your interviews, everything else feels intimidating. How do you transcribe entire interviews if every participant talks about something different? How do you even measure answers in natural language with unique viewpoints?

I feel you. When I managed customer support for an online company a few years ago, a lot of our valuable feedback didn’t come from scores out of 10 on product surveys, but rather from customer emails, reviews, and social media comments telling us what they loved and where we could do better. I think that’s why transcribing interviews for qualitative research like this is so important. It’s the first step in organizing all kinds of information into groups and themes you can actually use.

Keep reading because I’ll explain more about how to transcribe an interview (and why you should), plus using transcription software for qualitative research to speed things up.

What is Qualitative Research?

Qualitative research is when you ask open questions that prompt people for descriptive answers. It encourages feedback and observations that you can’t measure with numbers. If quantitative research finds out the facts from numbers , qualitative research is the reason why people make specific choices or behave a certain way .

What is an Interview?

An interview is a great qualitative feedback method whereby you ask a series of open-ended questions to an interviewee to gain answers, feedback, and opinions in their own words. You can conduct interviews in person, over the phone, or on video, solo or in groups.

How to Prepare an Interview for Qualitative Research

1. Decide Important Information for Your Interview

Start with your research objectives and create questions based on these - what do you want to learn?

How will you structure your interview? For structured interviews, prepare a list of questions to ask. For unstructured interviews, list topics you want to talk about.

Use open-ended questions to help the interviewee express their thoughts in their own words.

Ask your team to review and approve the interview questions before you begin so you can tweak them if needed.

2. What You Need from Your Research Interview Transcription

How will you extract answers and comments from your transcripts? Coding answers for specific questions and connecting themes can help categorize the data.

Will you read through the entire transcript or condense the conversation into bullet points? You can format your transcript in three ways:

Full Verbatim : The conversation in a raw, unedited state including slang and false starts

Intelligent Verbatim : A cleaned-up version of the full transcript, written in a grammatically correct way without false starts or stuttering

Detailed notes : Summarizing the conversation into scannable notes that cover the main points of conversation

3. Have Your Tools Ready

Choose a good quality microphone and noise-canceling headset that provides clear audio for easy communication.

What device will you use to conduct the interview and write the transcript? Have your PC, Mac, or tablet up to date with software installed.

Settle on the transcription software you’ll use. If you’re typing the transcript manually, have your preferred text editor installed. For automatic transcription, set up your Notta account and log in.

How to Transcribe an Interview for Qualitative Research

Manually transcribe your interview.

Listen to the recorded interview all the way through to familiarize yourself with the content of the conversation.

Open your favorite text editor such as Microsoft Word or Notepad and begin writing the speech while you listen to the recording. Don’t worry about getting it perfect the first time, just write as much as possible.

Go through the transcript again while listening to the audio, this time adding in timestamps in [HH:MM:SS] format and speaker tags every time the interviewer and interviewee speak, with a tag such as [Rachel] or [Interviewer]. If the interviewee wishes to remain anonymous, you can use a general tag such as [Interviewee].

Save your document to your device and share with your research team. Repeat these steps for every interview.

Automatically Transcribe Qualitative Interviews with Notta

Upload an existing recording to notta.

Log into Notta and go to your Dashboard .

Click ‘ Import files ’. You can drag and drop your audio or video file or paste a Google Drive, Dropbox, or YouTube link in the ‘ Import from link ’ field.

Record a Live Meeting or Live Audio with Notta

Click ‘ Transcribe live meeting ’ and paste your meeting link from Zoom, Google Meet, Microsoft Teams, or Webex, if you’re conducting your meeting virtually. This invites Notta Bot to record and transcribe your conversation.

Click ‘ Record an Audio ’ if your interview is in person. This starts recording using your microphone and transcribes in real time.

Edit, Export, and Share Your Transcript

Click on the interview transcript under ‘ Recent Recordings ’ on your Notta dashboard.

Read through the full transcript. You can adjust the blocks of text by clicking your cursor and pressing ‘ Enter ’ or ‘ Delete ’ to join or separate transcript text.

Click a speaker’s name to change it. You can type their name and then decide whether you want to adjust it for this block of text only, or for the entire transcript. Remember, if an interviewee wants to remain anonymous, you can type a generic tag like ‘Interviewee’.

Correct transcription errors by typing directly into the transcript text. Click the highlighted words or phrases in blue and the audio playback will jump to this point.

Add written notes and images to specific points in your transcript using the floating toolbar. This is helpful if you want to add observations you made during the interview.

Click the ‘ Download ’ icon at the top of your transcript page and export in a variety of formats. Notta exports in MP3, TXT, SRT, PDF, DOCX, and XLSX. You can toggle timestamps, speakers, and more.

Share the transcript with people in your team by clicking the ‘ Share ’ button, then managing group and team permissions with the drop-down menus. Create a shareable link by toggling ‘ Share ’ on and copy the link provided.

How to Analyze the Interview Qualitative Data

At first glance, you might feel daunted by the prospect of arranging your qualitative data. After all, numerical and factual data is easy to organize. But what about unique answers, observations, and feedback? Don’t worry—here are some simple steps you can follow to analyze your findings without pulling your hair out.

Organize the Information You Collected at Interview

To study your interviews effectively, use the same method of collection for every interview. This means transcribing each interview in the same way and asking the same or similar questions to each participant. This way, you can stick to the same process when analyzing what data you’ve collected.

Summarize Insights Using Your Transcript

Reading through full transcripts takes a lot of time, so you may find it easier to condense the information into a summary. Use Notta AI to create a summary in a few moments.

Click the ‘ Magic wand ’ icon in your Notta transcript and then click ‘ Generate ’. Notta AI uses machine learning to create an AI summary with three useful parts:

AI Summary: A condensed version of your full transcript, highlighting the basic points

Chapters: A list of key moments and themes during the conversation

Action items: A list of next steps to take, according to the conversation

Explore the Data

Creating a coding system helps you categorize the data and make it easier to understand. Codes can vary and come in a variety of formats but here are some examples:

Descriptive codes : Providing context for the data such as ‘interview setting in coffee shop’.

In-Vivo codes : A verbatim phrase the interviewee used to describe a product or service such as ‘I couldn’t live without it’.

Themed codes : Describing an overarching theme or pattern relating to the interview questions such as ‘accessibility issues’.

Process codes : Identifies what stage of the process the interviewee is currently at in relation to your product or service, such as ‘canceled subscription’ or ‘I’ve heard of your product recently’.

It’s vital that everyone involved in research sticks to these pre-agreed codes to organize data efficiently. It’s okay if you need to revise your methods as you go, but keep everyone informed so there’s no confusion.

Present Your Research

To make your research findings easier to interpret, you can organize them in several ways. Here are some common methods to share your data:

Spreadsheets : arrange your data into a table

Graphs : Displays themes and patterns using their codes in visual graphs

Word clouds : Using In-Vivo codes, displays the most commonly used language by participants

Are There Any Other Qualitative Research Methods?

Interviews aren’t the only way to collect useful qualitative data. If you’re pressed for time or need a deeper understanding of a culture or group, you can try other options.

Observations : Observing peoples’ behaviors in their natural setting without directly interacting with them. Take detailed field notes to describe what you can see, hear, and encounter in terms of interactions.

Focus groups : Taking a small group of people and asking questions. Their answers and interactions with each other can provide verbal and non-verbal insights.

Ethnography : Immersing yourself in a culture or group of people to understand their behaviors, rituals, and perceptions. This is similar to observations but might require deeper and longer-term fieldwork.

Narrative analysis : Studying personal stories, biographies, and autobiographical data to understand the perceptions and meanings people give to certain experiences.

Surveys : Create a series of open-ended questions in a questionnaire to distribute to people, to get unique feedback in their own words.

Secondary research : Gathering different, pre-existing sources of feedback as qualitative data. This includes emails, texts, images, videos, audio recordings, documents, policies, and diaries.

Frequently Asked Questions about Transcription in Qualitative Research

What is a good example of qualitative data.

Qualitative data is valuable when it provides insights into a person’s reasoning behind certain behaviors and lived experiences. Some examples of good qualitative data could include:

Documents like contracts, notes, and emails that contain descriptive, non-numerical data

Social media post and forum comments expressing authentic discussions and opinions

Audio or video recordings of natural conversations

The best qualitative data provides real, descriptive feedback in a person’s natural language. It should express feelings, emotions, attitudes, and perceptions.

Is Qualitative Research Subjective?

Yes, qualitative research is subjective because it’s relative to that individual’s culture, experience, and perceptions. It’s based on opinions and thoughts. Quantitative data is objective because it deals with numerical facts. Both have a place in research, but the subjective nature of qualitative research provides reasoning behind behaviors and decisions.

Why Should You Choose Qualitative Research?

There are many reasons why qualitative research is valuable:

It’s unbiased, as participants can provide thoughts and opinions in their own words.

You can uncover new theories and hypotheses that you may not have known about previously. Collecting qualitative data is often unstructured, allowing people to express new ideas and themes

Gain a deeper understanding of trends. Participants using their own language allows you to find common patterns and insights on a particular issue.

You can discuss sensitive topics. Participants can broach subjects in their own words and share as much as they feel comfortable with.

What is the Difference Between Qualitative and Quantitative Data?

Quantitative data is fixed, numerical feedback. Examples can include annual income, number of people in a household, times a person has bought a specific item, and so on. Qualitative data provides non-numerical, descriptive feedback in natural language. Examples might look like thoughts about a brand’s new color scheme or favorite part of visiting a recent conference.

How Do I Record an Interview?

Here are some basic steps you can follow to prepare for recording an interview as part of your qualitative research:

Write out a list of open-ended questions or a list of topics you want to cover.

Check that the device you’re recording on is charged.

Plug in your microphone and headphones and test them.

Conduct your interview in a quiet environment.

Use the meeting software’s built-in recorder such as Zoom, or a meeting recorder such as Notta.

When the interview starts, set your interviewee at ease by asking some icebreaker questions about their day, their plans, and themselves to get to know each other better and build rapport.

Move onto your interview questions, leaving plenty of time for the interviewee to gather their thoughts and answer in their own words.

Listen and follow up to ask for clarification if needed.

Thank them for their time and let them know what the next steps are.

Check the recorded audio or video file to ensure it came out clearly, ready for transcription.

See? Transcribing an interview for qualitative research doesn’t have to be stressful or time-consuming when you have a plan! My biggest piece of advice here is to understand the goal of the interviews you’re conducting . Interviews with vague questions and no direction in relation to your research objective aren’t likely to garner you valuable information you can actually use. Once you know what insights you’re aiming to uncover, it makes the conversation feel more productive and your research interview transcription will be far easier to use when collecting the data.

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Transcribing interviews for qualitative research

Transcribing interviews is an important step in qualitative research, as it forms the backbone of data analysis and interpretation. In other words we can say that it acts as a vital link between those unfiltered conversations and insightful data acquired from them. But why is accurate transcription so crucial in qualitative studies?

The fundamentals of qualitative research itself provide the first justification. The depth with which linguistic expressions and emotions are communicated during interviews is crucial for this kind of research. Accurate transcription ensures that these non-verbal cues are also added for more clarity.

Transcribing interviews qualitative research is essential to ensuring the correctness of findings because it enables researchers to fully capture the range of participant replies and perspectives. Moving forward in this article we have compiled a comprehensive guide to help you get a more clear perspective on how to transcribe interviews for qualitative research.

What Is qualitative research?

Qualitative research is one of the most commonly used research methods in the field of academia. Instead of concentrating just on the what, where, and when of decision-making, it explores the why and how by focusing on the human aspects of a specific issue or situation. It aims to comprehend people's experiences, actions, feelings, and the interpretations they place on objects.

Getting a much deeper insight into people's attitudes, actions, value systems, concerns, motives and goals is the main aim of qualitative research. It is employed to acquire a deeper comprehension of intricate occurrences that are challenging to put into numerical form.

The main characteristics of qualitative research are:

• Focus on context: It explores the context in which behaviours and events take place.
• Subjectivity: It recognises the subjective nature of the study and frequently captures the perspectives of the participants.
• Extensive analysis: This entails a thorough examination of a limited number of case studies or circumstances.
• Inductive approach: The inductive approach often begins with observations and builds theories from them.
• Flexibility in design: As the study goes on, the research question format may change. Here it is not necessary to follow the predetermined context.

Researchers use qualitative interview as their main method of data collection for this research since it allows them to interact with the subject first hand and focus on the non-verbal cues along with the information they are sharing.

Looking for support in transcribing your qualitative research interviews? Good Tape offers transcription services that can help you better understand your interviews. We're here to help make your transcription process more manageable and efficient. Explore how Good Tape can assist you in your research endeavors .

Qualitative vs quantitative interviews

Qualitative and quantitative interviews are different research approaches, each with a unique strategy for collecting and interpreting data. Quantitative interviews seek to measure human behaviour and experiences in a form that can be statistically examined, whereas qualitative interviews concentrate on investigating and comprehending the depth and complexity of human behaviour and experiences.

While both are extensively used in the field of research, it is important to understand where either of the two should be used. Below is a comparative table of both against which you can determine which of the two would work best in your scenario.

This table presents a clear contrast between qualitative and quantitative interviews, highlighting the differences in their technique, strategy, and study conclusions. The choice between both majorly depends on the research question at hand and the nature of the topic being studied.

How to transcribe an interview for qualitative research

For qualitative research, transcription of interviews is a painstaking procedure that needs time and close attention to detail. It requires turning spoken words from your recorded audio or video into text.

In qualitative research, this transcribing procedure is essential to data processing. Here's a step-by-step tutorial on effectively transcribing interviews, along with a few tips to make the process as easy as it can be.

Record clear audio of the interview

Select a peaceful, quiet workstation for your interviews to reduce distractions and improve focus. It is important to have a well-positioned microphone and high-quality headphones if you want to record even the minute details of speech without picking up excessive background noise.

If there are any unpleasant noises in your audio, services like Good Tape can be quite helpful. They are made to carefully pick up on all spoken and nonverbal cues, even in busy settings, and automatically transcribe all your work for you, so you won't miss any important information.

Work around your transcription

Precise transcription is essential for detailed analysis, accurately recording each word and nonverbal cue. This comprehensive approach allows for a deeper understanding of both the verbal as well as non-verbal cues in communication.

Similarly, intelligent verbatim concentrates on streamlining the text by eliminating unnecessary words and sounds to focus on the primary concepts, resulting in a transcript that is more focused and structured. Revised transcriptions enhance the material by improving clarity and fixing grammar, guaranteeing that the final transcript is accurate, comprehensible, and cohesive.

Audio transcription services such as Good Tape make accurate transcription easy with a shorter turnaround time.

Finalise the transcript

For easy navigation and the identification of important points or sensitive parts within the text, transcript formatting consistency is essential. Consistent formatting facilitates reading and improves the transcript's overall usefulness.

A further crucial stage is anonymisation, which anonymises any confidential or private data to comply with legal regulations. This also gives the interviewees peace of mind knowing that the information they provide will not be used illegally. To ensure that the transcript is correct, well-written, and presented professionally, one last review is necessary to spot any spelling, grammatical, or flow errors.

Some useful tips

Manual transcription can take a lot of time, therefore patience is essential. However, if you wish to have accurate transcripts in less time, using services such as Good Tape can cut down on the amount of time required.

It's also very important to make sure that your transcribed documents are safe. Maintaining regular backups is essential to avoiding data loss. Using services that automatically store and back up your transcribed audio might be a sensible choice if you find it difficult to remember to do backups, since they provide efficiency and peace of mind.

Why accurate transcription matters in qualitative research

Precise transcription is essential to qualitative research because it supports the accuracy and essence of the whole research process. It is the first stage of data analysis and has a direct impact on the findings and recommendations of the study. There are several reasons why accurate transcribing is important and advantageous.

Impact on data analysis

• Maintains originality: Preserving the original context of spoken words is ensured via precise transcription. For accurate interpretation of the data, this is essential.
• Enables comprehensive study: If the transcription has even minute error, it may prevent researchers from doing a thorough study of the interview data, including discourse, theme, and content analysis. Conversation analysis requires a lot of details which is possible through detailed notes of its accurate transcription.
• Supports accuracy: Data analysis in qualitative research is a very crucial step. More valid findings are produced when transcripts are accurate because they give researchers a solid foundation.

Impact on research outcomes

• Validity of findings: The reliability of the study findings is directly impacted by the quality of the transcribing. Inaccurate conclusions may result from word misinterpretation or omission.
• Reliability and reproducibility: A key component of scientific investigation is replication, which is made possible by accurate transcribing, which also increases the research's dependability.
• Reflects the voice of the participant: Accurate transcribing preserves the integrity of the participants' contributions by correctly capturing their voices.

Benefits of accurate transcription

• Enhances credibility: Precisely recorded information strengthens the credibility of the study among other researchers and readers
• Facilitates peer review and cooperation: Because other researchers can comprehend and analyse the data with clarity, it makes effective peer review and cooperation possible.
• Enhances engagement with data: When data is precisely translated, researchers may interact with it at a deeper level, which results in more perceptive analysis and interpretation.

Accurate transcription plays a crucial role in maintaining the validity, reliability, and integrity of the research findings. It improves the quality and depth of data analysis, guaranteeing that the conclusions are solid, reliable, and accurate representations of the experiences and viewpoints of the participants.

Discover Good Tape’s interview transcription service

We’ve understood in depth how to transcribe interviews for qualitative research, let’s go over how you can do so accurately and quickly without having to put in much effort. Good Tape has a relatively simpler user interface which you can navigate through without any manual or instructions. Here’s what you can expect when going through the process of transcribing your audios.

• Upload your file: The first step in the process is to upload the file you need to transcribe. Make sure the file is complete and has all the information you require

• Select the language: Good Tape has a number of options when it comes to choosing the language of transcription. Select the one you want, although you can also choose the “auto-detect” option for the system to automatically identify the language in the audio.

• Transcribe the text: Once the file is uploaded and the language is chosen, proceed further by clicking the “transcribe” button. Your audio transcription process starts here.

• To wait or not to wait: If you’re a casual plan user, you will have to wait for some time for your transcription to be completed due to excessive load by the users. However, if you’re a professional or a team user, you get your results ASAP! The wait time depends on the plan you’re subscribed to .

• Get notified: You will receive a notification once your transcribed document is ready. An e-mail will be sent to your inbox containing the link to access and download the document.

Looking for a good transcribing interviews qualitative research service? Try out Good Tape’s audio-to-text transcription service today and increase your work productivity. Their AI incorporated technology makes sure that every verbal and non-verbal cue is recorded, giving your qualitative data a deeper level of understanding.

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Transcribing Interviews for Qualitative Research: Best Practices

• Serra Ardem

The long hours dedicated to transcribing interviews are now in a galaxy far far away, thanks to the developments in AI and machine learning. Qualitative research highly benefits from these advancements as AI transcription technology not only saves valuable time but also increases research efficiency and accuracy.

In this blog, we emphasize the significance of transcribing interviews for qualitative research as well as the best practices in this area. We also explain why automatic transcription offers more advantages to researchers and how to choose an interview transcription software to achieve optimal results.

Let’s begin.

What is qualitative research?

Qualitative research is a systematic approach to understanding and explaining social phenomena. Focused on “How?” and “Why?” questions, it is an umbrella concept that involves different research methodologies including interviews, participant observation, focus groups and so on.

Qualitative data is based on words, behaviors and images. By analyzing these, qualitative research generates theories and hypotheses on how the social world is experienced and understood by people in everyday life. Unlike quantitative research that depends on numbers and statistics, qualitative research seeks to uncover the underlying meanings in human experiences.

Importance of Transcribing Interviews in Qualitative Research

Transcribing interviews for qualitative research offers several benefits that contribute to the overall depth and success of the research process. Here are its key advantages:

• Comprehensive analysis: Transcripts capture every word, nuance and non-verbal cue, which is a goldmine for data analysis. This allows researchers to identify themes and patterns thoroughly to draw meaningful conclusions.
• Enhanced reliability: Having the transcript for an interview will strengthen research validity by providing evidence to your argument. Plus, other researchers can review the transcription, ensuring transparency and collaboration.
• Reduced bias: Transcribing interviews will reduce bias as it minimizes the risk of misinterpreting or omitting information. Compared to note-taking, which may be influenced by the researcher’s perceptions, transcription offers a more objective representation of data.
• Increased accessibility: Via transcription , researchers can share and discuss findings with people who couldn’t participate in the interview due to language barriers. Furthermore, the practice improves accessibility for deaf and hard of hearing individuals by allowing them to engage with the findings through written text.
• Time-efficiency: No more jumping back and forth in audio files! When you transcribe the interview, you can quickly search for and navigate to specific parts, saving time during the analysis phase.

4 Types of Transcription

Transcription can be grouped into four categories: verbatim, intelligent verbatim, edited and phonetic. Let’s take a look at each one’s pros and cons, and highlight the best choice for transcribing interviews for qualitative research.

Verbatim Transcription

Verbatim transcription includes every sound in the audio recording such as coughs, doorbells and hesitations (er, mm, etc.) between sentences.

Pros: Provides the most complete and accurate record of the interview, which is essential for capturing the full context and subtle nuances.

Cons: May include unnecessary details. Can be time consuming and expensive to produce in case of manual transcription.

Primarily used in: legal proceedings, sociolinguistic research studies

Intelligent Verbatim Transcription

An intelligent verbatim transcript removes filler words and repetitions but retains key content and non-verbal cues. Its purpose is to provide a more on-point transcript.

Pros: Offers a balance between readability and details. 

Cons: May sacrifice some context and require careful quality control to guarantee accuracy.

Primarily used in: qualitative research, especially in interviews and focus groups

Edited Transcription

Clarity is the main focus of an edited transcript. It corrects grammatical errors and eliminates filler words, repetitions and extraneous sounds.

Pros: More readable and concise, therefore suitable for general understanding and thematic analysis.

Cons: Risks losing some nuances and the authenticity of participants’ expressions.

Primarily used in: journalism and media contexts

Phonetic Transcription

Phonetic transcription is unorthodox as it uses symbols from the International Phonetic Association to represent sounds exactly as they are spoken. This includes accents, dialects and non-standard pronunciations.

Pros: Analyzing variations in pronunciation.

Cons: More complex and expensive than other types of transcription.

Primarily used in: linguistic studies

What is the best type for interview transcripts in qualitative research? As we’ve said above, intelligent verbatim transcription is often the best choice: It is readable and manageable for analysis, yet it also provides a detailed record of the conversation.

Still, always consider your research goals, questions, data and budget when transcribing interviews. An edited transcript might be sufficient if you want to focus on broader themes. Meanwhile, verbatim transcription can be pretty useful if details matter to you a lot.

Methods of Transcribing Interviews

There are two main methods when it comes to transcribing interviews: manual and automatic. While manual transcription involves a human transcriber typing out the spoken words in the interview, automatic transcription utilizes speech recognition technology to convert audio to text.

As in types of transcription, these two methods have their unique advantages and disadvantages. Human transcribers can better understand nuances and context. However, this method can also be pretty time consuming and it may be expensive to hire a professional transcriber.

On the other hand, automatic transcription is much faster and cost-effective. This is an important advantage in the realm of qualitative research where large amounts of interview data need to be processed and analyzed. You can definitely save time and resources by using software when transcribing interviews for research.

Moreover, automatic transcription services are getting more accurate day by day thanks to the developments in AI, machine learning and voice recognition. Current systems can handle diverse accents, linguistic variations and even contextual nuances very well. This significantly increases the reliability of the interview transcript and research results.

How to Choose an Interview Transcription Software

Decided to use an interview transcription software for research but confused on how to choose one? Look for these qualities when making your decision:

Accuracy is crucial when transcribing interviews as it directly influences the reliability of your data. Prioritize an AI-powered tool with a high accuracy rate to remain true to your original interview. We recommend you test the AI transcription software beforehand with a small sample of your interview.

Quick turnaround time is essential for researchers who work with large sums of interview data and tight deadlines. The right software must transcribe audio to text rapidly without compromising accuracy and meet the demands of an intense qualitative research process.

It is your responsibility to comply with ethical standards and protect your participants’ sensitive information. You must choose a tool that has end-to-end encryption and clear privacy policies.

Flexibility

Does the transcription software allow you to upload audio and video files in different formats? Is it easy to edit the transcript and add notes? This flexibility will help you refine interviews seamlessly, enhancing the quality of your data.

Customization

Speaker identification, timestamps and punctuation are indispensable when transcribing interviews for qualitative research. Select a software that allows you to tailor these elements to your needs.

Language Support

Make sure that the tool supports the languages spoken in your interviews. Break down the language barrier by choosing a software that transcribes multiple languages and enrich your research with global perspectives.

Transcribing Interviews with Maestra Step-by-Step

If you’re looking for a tool with all these features, then Maestra’s AI-powered interview transcription software is the right choice for you. You can get your transcript instantly by following a few simple steps.

• Upload your audio or video file. Maestra supports 125+ languages .
• Select audio language and receive the transcript in seconds. 

Custom dictionary is especially beneficial when transcribing interviews for research as the audio content is more likely to include technical terminology. With this feature, you can add specific terms to your custom dictionary, assign importance values and Maestra will transcribe them as specified, ensuring accuracy.

You can also select the number of speakers during the upload phase and assign names to each speaker, making it easier to navigate the transcript.

• Click “Submit” and witness AI transcription work its magic. You will instantly receive your interview transcript with timestamps and speaker tags.
• Ta-da! You can now proofread and edit your transcript, take notes and add comments with Maestra’s built-in text editor .

Maestra has a very high accuracy rate but you can always polish your document for maximum clarity and comprehensibility.

After transcribing interviews, you can safely reach and organize them via MaestraCloud . You can also store your interview recordings here as the cloud allows you to keep audio and video files of any size without time limitations.

Collaborating with fellow researchers? Maestra Teams is ready to help you. You can create team-based channels with different permission levels and edit the document with other researchers in real-time.

Tips for Transcribing Interviews for Qualitative Research

No matter your experience in qualitative research or the software you use, there are certain practices to adopt when transcribing interviews.

Use a High-Quality Recording Device

Utilizing a high-quality recording device lays a solid foundation for interview transcription. Invest in a reliable recorder with good microphone sensitivity and audio quality to capture every part of the conversation. Don’t forget to test your equipment beforehand to avoid potential technical issues during the interview.

Respect Confidentiality

Upholding confidentiality is paramount when transcribing interviews for qualitative research. Always obtain informed consent from participants for recording and transcription, and store your files securely. Avoid sharing any personally identifiable information to safeguard participant privacy and maintain the integrity of your research.

Include Speaker Identification and Time Stamps

This practice enhances the overall usability of an interview transcript by enabling easy reference to specific points. Make sure you clearly identify each speaker on the document either by name, role or pseudonym. You can use different fonts or colors to visually distinguish between speakers.

Follow the Specific Style Consistently

Choose a transcription style guide (verbatim, intelligent verbatim, etc.) and follow it consistently throughout the project. Define rules for punctuation, contractions and interruptions. This will guarantee uniformity and enhance the reliability of your findings.

Add Non-Verbal Cues and Annotations

This one is not mandatory but can provide valuable context. You can document non-verbal expressions, pauses or changes in tone to add depth to qualitative data analysis. Meanwhile, bracketed annotations can help you highlight important moments. Just remember that adding too much detail can be distracting, so only include relevant information.

Edit and Proofread the Transcript

Proofread and edit your document once transcribing an interview: correct any errors, format inconsistencies and review for readability. Double check speaker identification and timestamps for accuracy. These practices will ensure a smooth transition from transcription to analysis and publication.

Frequently Asked Questions

Is transcription necessary for qualitative research.

The necessity of transcription depends on the nature and goals of the qualitative research you conduct. For example, it is crucial for in-depth and focus group interviews but not essential for participant observation where researchers can rely on field notes.

How do you transcribe an anonymous interview?

When transcribing interviews with anonymous participants, remove any information that can directly or indirectly identify the participant such as name, nickname, location, job title and affiliations. Create neutral pseudonyms (Participant 1, Interviewee A, etc.) for the participant and use them consistently throughout the interview transcript.

How do you analyze interview transcripts in qualitative research?

First, familiarize yourself with the data through readings when analyzing an interview transcript for research . Then, assign codes to relevant segments and organize similar codes into broader recurring themes. Finally, present your findings via a structured narrative. Always maintain transparency during the process.

How do you transcribe an interview in APA format?

Transcripts of interviews are usually added to the appendix in APA format . You should use a specific header with interview details, double line spacing and speaker identifiers in the transcript.

How do you summarize an interview transcript?

Carefully read the content and identify key themes when summarizing the transcript of an interview . Organize the information logically, provide brief contextual details when necessary and use quotes to add impact. Capture the essence of the interview by keeping the summary short and sweet.

Interview transcription is particularly valuable in qualitative research, which delves deep into human experiences and perceptions. Transforming spoken words into text enables researchers to derive meaningful insights from the rich tapestry of qualitative data. It also increases the accessibility of the research, empowering scholars to collaborate with colleagues across disciplines and borders. 

The advent of AI technology revolutionized the process of transcribing interviews and will continue to do so in the future. Its benefits range from increased accuracy to cost-effectiveness, providing a much refined experience for researchers. By choosing the right software and adopting the best practices for transcribing interviews, researchers can unleash the full potential of their endeavors.

About Serra Ardem

Serra Ardem is a freelance writer and editor based in Istanbul. For the last 8 years, she has been collaborating with brands and businesses to tell their unique story and develop their verbal identity.

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Transcribing interviews for research

Transcription involves making a written record of speech. This can be done during an actual interview or carried out afterwards using an audio or video file.

Why transcribe interviews during research?

Transcribing interviews that are carried out for research is good practice. Interviews that are transcribed verbatim in qualitative research allow analysis of the collected responses in greater detail at a later date. It also leaves less room for bias caused by the researcher’s personal interpretation.

Researchers can read and re-read interview transcriptions, identify themes and patterns, and extract key quotes or phrases. This process helps to identify significant points, which can then help to draw meaningful conclusions.

Transcribing interviews for research is also useful because it helps researchers check that their interpretation of the conversation is accurate, so reduces errors or misinterpretations. The transcription provides a permanent record of the interview, which can be referred to by other people in the future to confirm any findings.

Interview transcription can also help to ensure the anonymity of the participants. By removing identifying information from the transcriptions, researchers can protect the privacy of the participants. This often makes people more willing to take part in research interviews.

Another benefit is that interview transcription can be easily shared among researchers. This facilitates analysis and collaboration, which further contributes to the reliability and validity of the research.

Full audio and video transcription solutions

How to transcribe manually.

Transcribing a recorded interview manually is time-consuming and it’s vital to make sure it’s done correctly. Here are some steps to follow if you’re considering transcribing interviews for research:

• Before starting the transcription process, listen to the recording to familiarise yourself with the voices, accents and any background noises that might affect the transcription process.
• Create a transcription template that includes the speakers’ names or identifiers, timestamps (if required) and space for the transcribed audio text.
• Start transcribing the interview by listening to a few seconds of the recording, pausing and then typing out what was said. It’s important to be accurate, and to include all the spoken words. Also be sure to include filler words, such as ‘um’ and ‘ah’ and any non-spoken communication, such as laughter or pauses if needed. Find out more about different types of transcription .
• Once the transcription is complete, spend time editing to ensure accuracy and readability. Make sure you have correctly identified the speakers and that any timestamps are accurate.

Download our free transcription template

Get started with transcription. Here you will find templates for both detailed transcription and standard transcription . You can use the formats and examples in your own working document.

Tools for creating automatic transcriptions

Manual interview transcription is incredibly time-consuming. However, there are several software apps available that offer free interview subscription or paid-for services. Here are some of the most popular:

• Otter.ai: Otter.ai is a popular transcription app that uses AI to transcribe interviews in real-time. It offers a free plan that allows users to transcribe up to 600 minutes of audio per month. There are paid-for plans available for higher use.
• Temi : Temi is an automated transcription service that offers both free and paid-for plans. The free plan allows users to transcribe up to 45 minutes of audio per month.
• Trint : Trint uses AI to transcribe audio and video files. It offers a free trial and paid-for plans.
• Happy Scribe : Happy Scribe is another transcription software tool that uses AI to transcribe audio and video files. It has some pretty impressive user reviews and offers a free trial and paid-for plans for higher usage.
• Speechmatics : Speechmatics is speech recognition software that offers an automatic transcription service and can also be integrated with other systems. It offers a free trial and subscription plans.

Automatic transcription software is undoubtedly convenient and time-saving. However, it might not always be accurate. At the very least, all automatic interview transcriptions should be manually checked.

Here’s what an interview transcription for research might look like

Interviewer: Can you tell me about your experience using language learning products?

Participant: OK. [inhales deeply] So, I’ve tried more than a few different products, mostly apps on my phone and tablet, and I’ve found that they can be helpful, but also frustrating at times. I also tend to lose interest in them after a while.

Interviewer: Can you tell me about a time when you found a language learning product helpful?

Participant: Yeah, so I was using this one app to learn Spanish, and it had a feature where you could record yourself speaking and then listen back to it. I found that really helpful because it helped me hear my mistakes and work on my pronunciation. It was awful listening to myself speak though. [laughs]

Interviewer: [laughs] That’s interesting. Can you tell me about a time when you found a language learning product more frustrating?

Participant: Yeah, so I was using another app to learn French, and it was just really baffling and such a waste of time. The lessons were all over the place, and the exercises didn’t seem to match up with what I was learning. It was really frustrating and demotivating. I ended up giving up after a couple of weeks and it really put me off to be honest. [sighs]

Interviewer: I see. Can you tell me about any features of the language learning apps you’ve tried that you particularly like or dislike?

Participant : I really like it when the app has a feature where you can practise speaking with a native speaker. It’s really helpful to get feedback and practise in a more natural way. On the other hand, I don't like it when the app is too repetitive or doesn’t have different exercises and that. I end up just chucking it. [intonation rises]

Interviewer: Thank you for sharing your thoughts and experiences. [laughter]

Participant: No problem, happy to help.

What if I need an APA interview transcript?

Transcribing an interview can be done manually. At the moment, it’s not possible for AI transcribers to do this. You should format your transcription to the style required by your organisation. Include the transcript as an appendix or as a reference in the body of your text. Appropriate referencing systems include APA, Harvard, MLA etc. Learn more about academic referencing styles.

Final thoughts

Transcribing interviews for research is one of the best ways of creating a permanent record of the data that’s gathered. It can then be shared with colleagues and analysed in the future. However, it’s a labour-intensive task, and sometimes it’s important to capture every word spoken, every inflection and even communication that comes from unspoken cues, such as laughter or vocal intonation.

Semantix’s multilingual transcribers can transcribe your interviews quickly and accurately, and they work in more than 170 languages. If you’d like to talk to us about our confidential transcription services , fill in the form and we’ll be in touch.

Would you like to order a transcription?

Download templates for both detailed transcription and standard transcription. You can use the formats and examples in your own working document.

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Why You Should Transcribe Interviews For a Better Qualitative Research

How to transcribe an interview in 5 simple steps (2024)

What type of content do you primarily create?

So you’ve finished an interview recording . 

Whether you’re using that conversation in an article or a research project or editing it into a video or podcast, it’s time to transcribe it. 

Where do you start? You start by typing “how to transcribe an interview” into Google, but you probably already did, and that’s what brought you here. Hi!

Anyway, you’re in the right place, because Descript will automatically transcribe your interview (and let you edit the audio or video right from the transcript). But if you want to learn more about different transcription options, here’s what you need to know.

What is an interview transcript?

An interview transcript is an audio track converted into text so people can read it. Before you start transcribing, it’s worth considering the transcript’s ultimate purpose.

• Are you using the interview as a research source? If you just want to drain your subject’s brain with interview questions , and their exact quotes don’t matter, you don’t have to waste your time with an intelligent verbatim transcription. A quick and dirty job will do.
• Will you be quoting your subject for publication? People don’t typically like being misquoted. Yet, it rarely reads well when people are quoted exactly as they speak in real life. You’ll likely want to split the difference: Go through that audio with a fine-tooth comb, get it down as faithfully as you can, and eliminate all the “ums,” “ahs,” and unnecessary “I means.” No one will fault you for making them sound more articulate.
• Is every single word, pause, and false start important? You’ll have to do a full verbatim transcript. When details count, they really count, so having every detail from your interview will be helpful for your project.

How to transcribe an interview, the easy way

1. get transcription software.

Praise the robots, for they allow us to save time. 

Not only is a lot of transcription software free (or free to a point), but it also takes the drudgery out of the transcription process. Some errors and typos are inevitable, so you’ll want to edit the text afterward, even if you use a high-quality transcriber like Descript.

Descript automatically transcribes any file you import; just drag your audio into the app, wait a few seconds, and you’ll have a complete transcript. 

That, in our very biased opinion, makes Descript the best transcription service, hands down. But here are some other transcription tools:

• Otter. Otter is a workplace tool to transcribe meetings and follow up with any actionable items easily. But you could also just as easily use it to transcribe your interviews in real time. Otter uses AI to automatically separate speakers and transcribe as your interview happens, and the basic features are free. You'll have to upgrade to get more advanced functions, like importing files for transcription. Plans start at $8.33/month.
• Amberscript: Amberscript, another AI-based transcription software, also offers translation and human editing services. The downside is that you only get 10 minutes of automatic transcription free. Beyond that, you’ll need to pay $10 per hour of audio.
• Scribie: For automatic transcription, Scribie claims to reach over 80% accuracy and only costs 10 cents per minute. That’s definitely good for the price point, though for the interviewer on the go, it’s worth noting that the service doesn’t have a mobile app. 

2. Upload your audio or video file

Open your Descript app, then click New project in the upper right corner.

On the following screen, name your project and click Choose a file to transcribe.

Choose your file.

After selecting open, Descript will automatically transcribe your audio or video file. 

3. Add speakers

Next, Descript will ask you to identify the speakers in your file. If it’s just you and another interviewee, select two from the dropdown menu.

Descript will then ask you to identify each speaker. We'll play you a short clip from your file, and you'll type in the speaker's name. 

Then click Add “ Name ” as speaker. Easy peasy!

4. Clarify the transcript as needed

Now you’ve got a transcript ready for editing. 

You probably see some mistakes already. With Descript, you can edit your audio by editing the text in the transcript. 

If you don’t like a word or sentence in your transcript, highlight it and press delete on your keyboard. You can also correct words and punctuation quickly with a few handy keyboard shortcuts . 

You can also choose to automatically correct any mistakes the AI made. Access them in the upper right hand corner of your transcript.

You’ll find shortcuts like:

• Shorten word gaps. Remove long periods of silence from your recording automatically. Set how many seconds (or more) of gaps to fill, then reduce them to whatever you want. 
• Remove filler words. Descript will automatically remove filler words like "you know," "well," or “um”. It can also remove stutters and repetitions. 
• Detect transcription errors. Clean up your transcript quickly with this tool. Click this button and Descript will highlight probable recording errors for your review.

5. Export the transcript and proofread

At this point, you’ve got a clean transcript ready to go. The last step here is to export the transcript and give it a good proofread. 

First, click the Publish button in the top right corner of your screen. Click the Export tab and then the Transcript button . 

Before exporting the file, you can choose to include:

• Composition name
• Ignored text
• Speaker labels

You can also choose how often you want the timestamps to appear in your transcript file, and the format of the transcript file. Once you hit Export, you can find the file on your computer. 

Here’s an example of what we just made.

💡PRO TIP: If you’re publishing the transcript as-is, hire an editor or use Grammarly to tighten it up. There will likely be grammar errors you missed during editing. It’s normal. Edit the transcript before pushing it live to truly make it shine out in the wild. 

Other ways to transcribe an interview

The above process for transcribing interviews is super easy, but if you’re up for a bit more of a challenge, or have more budget, there are two other types of transcription methods. 

Transcribe manually

Transcribing interviews manually is the “roll up your sleeves and type like the wind” approach. There aren’t many fancy tools here, just you, your audio recording, some headphones, and a word processor. (There are tools that will let you slow down and go back in a recording without leaving the keyboard, though — our managing editor has trusted the manual transcription tool from Wreally for years.)

The upsides are that it’s free (assuming you have the basic interview recording equipment ), and you’ll get very familiar with the material. The downsides are that it is time-consuming, and if you don’t have fast typing speed, it could feel like a Sisyphean task.

Here’s a rundown of how to get started.

• Gather your materials. You’ll want a pair of headphones, a decent word processor, and a keyboard.
• Listen to the entire interview first. If possible, you should listen to the interview soon after you do it so it’s fresh in your mind. If you or your subject dropped a word or mumbled, you might remember what was said within a day or two. But if it’s been a week or more since the interview, you probably won’t.
• Do a rough pass. Perfectionists might struggle with this step, but listen to the audio track and do your best to type everything out without stopping. This will give you an editing basis. It will probably look like a drunk person’s manifesto when you’re done, but that’s fine.
• Edit your rough pass. Before you listen through again, fix up your messy transcript the best you can. Separate the speakers from one another and correct any words or phrases you can.
• Listen again and adjust your transcript. Now that you have a serviceable document, follow along with your transcript as you listen again, tweaking words or phrases as necessary. If you’re lucky and your subject spoke slowly, this may be the only extra step you need.
• Listen through and adjust again as needed. As mentioned earlier, if you only need the transcript for the information you got from the subject, you likely don’t need to worry about getting everything exactly right. If you do need precise quotes, listen to your recording and edit your transcript until the two match up.

Hire a transcription service 

Nothing beats that human touch — for now at least. (The robots will replace all of us in the end.) Sending your audio files to a service that will farm it out to an actual person for transcription has obvious benefits. 

Namely, people understand the nuances of human speech better than a computer will, and if you hire a quality service, you won’t have to worry too much about editing what they send back. 

On the other hand, a transcript made by a human will cost you more than one made by software, and the level of detail means it’ll also take a lot of time.

Here are a handful of respected agencies to try:

• Descript White Glove : You had to know this one would be in here. It’s true: Descript allows you the option of ordering a human transcript right from the app with its White Glove service. It only extends to English for now and there’s a cap of two hours per transcription, but provided the audio quality is decent, Descript will return a transcript that’s up to 99% accurate.
• Vanan Services: Vanan provides transcription services in a variety of languages and also offers a free trial of up to five minutes from a given file. If it matters to you (and it should, because it’s cool), even NASA has used their transcription services. Their exact rate depends on the audio, but they do provide quotes in advance.
• GoTranscript: For a basic file with one or two speakers, GoTranscript’s rates start at 90 cents a minute, and they deliver reliable results. Everything beyond that (full verbatim transcription, timecoding, etc.) will cost you extra per minute.

Example of interview transcript 

Here’s an example of an edited transcription with timestamp headers: 

Interviewer (Content Marketer, Susan): Good afternoon, Mike. Thank you for taking the time to be with us today.

Mike (Founder, MarketingTool.io): Hi Susan, thank you for having me.

Susan: Mike, can you briefly tell us what inspired you to create MarketingTool.io?

Mike: Absolutely. I saw a gap in the market for user-friendly and cost-effective marketing tools. I wanted to create something that every business could use.

Susan: Interesting. What's the core functionality that sets MarketingTool.io apart from other tools?

Mike: Our AI-driven analytics. It not only provides accurate data but also insightful suggestions on how to improve your marketing strategies.

Susan: That sounds like a game-changer. Can you tell us about a significant challenge you faced while developing this tool?

Mike: Building the AI was the toughest part. It had to be smart yet straightforward. The balance was challenging but we managed it.

Susan: I can imagine that was a significant task. Moving forward, what new features can your users expect from MarketingTool.io?

Mike: We're planning to integrate more social media platforms and add real-time tracking capabilities.

Final thoughts

The nice (and maddening) thing about transcribing is that you know you can always get a free transcription by just doing it yourself. 

But if you have the option of hiring a transcriptionist to do it, it’s definitely something to consider. Transcribing an interview podcast is fun once or twice, but if you have a whole bunch of long, say, qualitative research interviews to get into text, you’ll be kicking yourself and/or going insane before long.

Interview transcription FAQ

What is the easiest way to transcribe an interview.

The easiest way to transcribe an interview is by using an automated transcription service like Descript. These apps use AI to convert spoken words into text, reducing the manual effort and time required to transcribe an interview.

How would you transcribe an interview?

To transcribe an interview, you would simply upload the audio or video file to a software like Descript. It’ll generate a written transcript in minutes and you can edit for accuracy. 

How long does it take to transcribe a 60 minute interview?

If you’re using an audio-to-text app, the turnaround time to transcribe a 60 minute interview is minutes. You’ll need to spend some time reviewing and correcting errors. If you’re transcribing the interview manually, it could take you up to six hours considering you have to pause, rewind, and review way more often.

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

• Handling qualitative data
• Introduction

Introduction to transcripts in qualitative research

Understanding the transcription process, practical insights: transcription in action, using transcription services, challenges in transcription.

• Field notes
• Survey data and responses
• Visual and audio data
• Data organization
• Data coding
• Coding frame
• Auto and smart coding
• Organizing codes
• Qualitative data analysis
• Content analysis
• Thematic analysis
• Thematic analysis vs. content analysis
• Narrative research
• Phenomenological research
• Discourse analysis
• Grounded theory
• Deductive reasoning
• Inductive reasoning
• Inductive vs. deductive reasoning
• Qualitative data interpretation
• Qualitative analysis software

Research transcripts

Conducting qualitative interviews or focus groups is only the first part of data collection in a qualitative research project. For most qualitative data analysis , you need to turn those audio or video files into written transcripts. While this may seem self-evident to many researchers, much discussion has taken place about transcripts, best research practices for generating them, the debate between transcription services and human transcription, and so much more.

Qualitative data transcription holds a key role in research , acting as the building blocks from which findings are derived and conclusions are drawn. They are the textual representation of verbal data gathered through interviews , focus groups , and observational studies . Given their significance, it's essential to grasp why they are fundamental to qualitative research.

What is the importance of transcripts in research?

The importance of transcripts in research lies in their ability to convert spoken language into written form, making data analysis significantly more manageable. Transcripts act as the raw material for your analysis , creating a tangible record of the conversations and discussions that form the basis of your research. They provide a precise, detailed account of the verbal data collected, enabling researchers to review the information repeatedly and uncover layers of meaning that might be overlooked when listening to the recording .

Transcripts help researchers systematically organize and manage the data, especially when dealing with large volumes of information. They make it easier to search for specific themes, patterns, or keywords, thereby speeding up the data analysis process. Furthermore, transcripts facilitate the sharing of data among researchers, allowing for collaborative analysis and review. They also ensure the transparency of your research by providing a permanent record that can be scrutinized by other researchers, reviewers, or auditors.

How is transcribing used in qualitative research?

A transcript is used as a way to record and represent the rich, detailed, and complex data collected during qualitative studies such as interviews, focus groups, or observations. Without transcriptions, it would be challenging for researchers to dissect, understand, and interpret the in-depth experiences, perceptions, and opinions shared by the participants. Most research involving audio recordings of interviews requires recordings to undergo the transcription process in order for qualitative data analysis to proceed.

Transcribing, in qualitative research, doesn't merely involve verbatim transcription (the word-for-word rendering of verbal data into text). It can also encompass the translation of non-verbal cues such as laughter, pauses, or emotional expressions that can provide valuable context and insights into the participants' experiences and perspectives. By capturing these details, transcripts can help portray a fuller, more authentic picture of the data, enabling a more comprehensive and nuanced analysis.

In qualitative research, transcriptions are also used for data coding , a process where researchers label or categorize parts of the data based on their content, themes, or patterns. This step is critical for identifying trends and making sense of the data, and having a written transcript makes the coding process significantly more efficient and precise.

How are transcripts used in quantitative research?

Interview transcripts also have an important role in quantitative research , specifically in methods like content analysis and conversation analysis . Content analysis involves the systematic coding and quantifying of data within transcripts, such as the frequency of specific words or themes. This allows researchers to discern patterns and trends and gain insights into the prevalence of certain concepts or attitudes. For example, this could involve quantifying the occurrence of health-related discussions within interviews with healthcare providers.

On the other hand, conversation analysis , while often qualitative, can include quantifiable aspects. Transcripts record details of conversation structure and patterns, such as timing and sequence of speech. Quantitative measures like the count of certain conversational elements or the duration of pauses can be used to understand communication dynamics.

In essence, transcripts are not solely a tool for qualitative research methods but also provide a source of quantitative data and a foundation for quantitative analysis methods. They allow for a detailed, tangible record of spoken data, crucial for both qualitative understanding and quantitative measures, showcasing their versatility in the research field.

The transcription process is a critical stage in qualitative research . It refers to the conversion of recorded or observed speech into written text, turning the fluid and dynamic nature of spoken communication into a tangible and analyzable form . In this section, we will delve deeper into the process of transcription and how it is approached in qualitative research.

How do you create a research transcript?

Writing a research transcript starts with the raw data , usually an audio or video recording from interviews , focus groups , or observations . The first step is to carefully listen to the recording and begin writing down what is being said. This should be done with utmost accuracy, capturing not only the spoken words but also any significant pauses, laughter, or emotional expressions.

A crucial aspect of writing a transcript is deciding how detailed it should be. This varies depending on the research objectives and the nature of the data. For some research, a verbatim transcription, which includes every utterance, filler words, and non-verbal cues, is necessary. For other studies, a clean verbatim transcript, which omits irrelevant details like repeated words or stutters, is sufficient. After the initial transcription, the transcript should be reviewed and cross-checked with the recording for accuracy. During this revision process, the researcher may also add time stamps, annotations, or comments to enrich the transcript further.

Other details in transcripts

Depending on your research inquiry, you may consider more nuanced approaches to generating transcripts when you require the analysis of complex and multifaceted data. Apart from accurately rendering the spoken words into text, a qualitative research transcript can also capture the context, meaning, and nuances inherent in the spoken interaction.

This could involve noting the tone of voice, pauses, emotional expressions, body language, and interactions among participants. These non-verbal cues can provide rich insights into the participants' attitudes, emotions, and social dynamics, thus giving the researcher a deeper understanding of the data.

One unique aspect of transcribing qualitative data is the reflection and interpretative process embedded in it. Researchers often gain a deeper understanding of the data during transcription, as it forces them to engage closely with the data and notice details that might have been missed during the initial data collection.

How is data transcription done?

Data transcription can be done manually or with the assistance of transcription software. Manual transcription involves the researcher or a transcriptionist listening to the recording and typing out the conversation. This method is time-consuming but can lead to a higher level of accuracy and deeper immersion in the data.

Automated transcription software, on the other hand, uses automatic speech recognition (ASR) technology to transcribe audio recordings into text. While this method is faster and can handle large volumes of data, it may not be as accurate, especially when dealing with poor audio quality, heavy accents, or technical jargon.

Regardless of the method chosen, the transcribed data should be reviewed and edited for accuracy. This might involve repeated listening to the audio, making corrections, and refining the transcript until it accurately represents the original data.

In summary, the transcription process is a meticulous task that requires careful listening, accurate writing, and thoughtful interpretation. It is an essential step in transforming the raw data into a form suitable for in-depth analysis, thus laying the foundation for your qualitative research findings. By understanding how to write a research transcript, specifically a qualitative research transcript, and knowing how data transcription is done, you'll be well-equipped to handle this critical phase of your qualitative research process.

Types of data transcription in qualitative research

As qualitative data can be diverse and complex, it’s important to understand that not all transcripts are the same. Depending on the research objectives, data characteristics, and the resources available, researchers might opt for different types of transcriptions. Let's delve deeper into these different types and their applicability in qualitative research.

What are the different types of data transcription?

There are generally three main types of data transcription:

1. Verbatim transcription: This is the most detailed form of transcription. It involves transcribing every single word, including filler words (like "um," "uh," and "you know"), false starts, repetitions, and even non-verbal cues such as laughter, pauses, or sighs. Verbatim transcription is often used in research where the manner of speaking or the emotional context is as important as the content itself.

2. Clean verbatim transcription: This type of transcription also captures every word spoken but omits filler words, stutters, and false starts, resulting in a cleaner, more readable transcript. Clean verbatim transcription is usually preferred when the focus is on the content of the speech rather than the style or manner of speaking.

3. Intelligent transcription (or edited transcription): This form of transcription goes a step further in simplifying and clarifying the text. It not only removes filler words and repetitions but also corrects grammatical errors and may even rephrase sentences for clarity. Intelligent transcription is typically used for creating transcripts intended for publication or for audiences who are not directly involved in the research.

What are the different types of transcription in qualitative research?

In qualitative research, the type of transcription used often depends on the nature of the study and the level of detail required in the analysis.

For studies aiming to explore the content of the conversations, clean verbatim or intelligent transcriptions might be sufficient. These types provide a clear and concise account of the spoken data, allowing researchers to easily identify themes and patterns in the content.

However, for studies interested in the nuances of communication, such as sociolinguistic studies or discourse analysis, a verbatim transcription might be more appropriate. This type captures the exact words, speech patterns, and non-verbal cues, thus providing a richer and more authentic representation of the spoken interaction.

Choosing the right type of transcription for your qualitative research is crucial, as it can significantly impact the depth and quality of your data analysis . By understanding the different types of data transcription and their uses in qualitative research, you will be better positioned to make an informed decision that aligns with your research goals.

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Transcription is more than a technical process; it's a fundamental part of the journey from data collection to analysis in qualitative research . Understanding transcription in action means knowing how to do it, what to include, and how to record it for optimal use in your study.

What are examples of transcription?

Transcription can take various forms based on the nature of your research. For instance, a sociolinguistic study might require a detailed verbatim transcript, including non-verbal cues and speech anomalies.

Here's an example:

Interviewer: So, how are you feeling about the project? (in a concerned tone) Participant: Umm... Well, (laughs nervously) it's been a bit... um, overwhelming?

On the other hand, an interview transcript for a market research study might be a clean verbatim transcript, focusing on the content. Here's how it could look:

Interviewer: What do you like about our product? Participant: I really enjoy its user-friendly interface and the customer service is exceptional

How do you transcribe a research interview?

Transcribing a research interview involves several steps. First, ensure you have a good-quality audio or video recording of the interview . Listen to the recording carefully, typing out the conversation verbatim. You can also slow down the speed of the recording, and shortcut keys to rewind the recording a few seconds can be a great help. It's essential to maintain accuracy and include key details that might influence the interpretation of the data , such as significant pauses or emotional inflections.

Depending on your research aims, you may choose to transcribe in verbatim, clean verbatim, or intelligent transcription style. Once the initial transcription is complete, review and cross-check it against the recording for accuracy. Finally, anonymize the data if necessary to ensure participant confidentiality .

What should be included in an interview transcript?

An interview transcript should include everything that is said in the interview, but the level of detail can vary. Here are some elements that are typically included:

1. Identifiers: These help distinguish between different speakers. In the case of an interview, this would usually be the interviewer and the interviewee(s). 2. Verbal responses: All responses to the interview questions should be included in the transcript. 3. Non-verbal cues: Depending on the research objectives, non-verbal cues such as laughter, sighs, or pauses can provide additional context and should be included. 4. Time stamps: These help locate specific parts of the audio recording and can be very helpful during analysis. 5. Annotations: These might include comments or notes made by the transcriber about the context, the tone of voice, or background noises.

How do I record an interview transcript?

Recording an interview transcript starts with creating an audio or video recording of the interview. After the interview, use either manual transcription or automatic transcription software to convert the audio into written text. Make sure to include identifiers for each speaker, their verbal responses, and any relevant non-verbal cues. Review and revise the transcript for accuracy, adding time stamps or annotations as needed.

In summary, transcribing interviews is a meticulous task that requires careful attention to detail and accuracy. By understanding what to include in a transcript and how to record it, you'll be well-equipped to capture the richness and depth of your interview data, laying the groundwork for a robust analysis.

Benefits of transcription in qualitative research

In qualitative research , transcription represents more than a technical or administrative task. It’s the transformative process that turns spoken communication into a tangible, accessible text form that can be critically examined, dissected, and evaluated. This process forms the underpinning of the entire data analysis journey, creating the foundation upon which interpretations are built and conclusions are drawn.

Looking deeper into the benefits of transcription in qualitative research

Unearthing the multiple layers of transcription’s benefits in qualitative research reveals how it contributes to the efficacy and integrity of a study.

1. Facilitating data accessibility: One of the fundamental benefits of transcription is that it brings to life the spoken word, facilitating accessibility. It translates data into a format that is readable, searchable, and conducive to rigorous analysis. Transcripts can be reviewed multiple times, allowing researchers to revisit the data continually. They can be easily shared among team members or other researchers, enhancing the communicability of the study. Transcription also bridges barriers for those who are hearing-impaired or for whom the original language of the conversation might be a hurdle.

2. Enabling comprehensive analysis: Transcripts are the bedrock upon which qualitative analysis is built. They provide the raw material for various methods of qualitative data examination, whether it's the deep dive of a thematic analysis , the linguistic focus of discourse analysis , or the systematic categorization of content analysis . These written records allow researchers to delve into the data, identify recurring patterns, extract significant themes, and uncover insights that might be less discernible or entirely lost in the original audio or video format.

3. Promoting reflection and interpretation: Transcription is far from being a mechanical, dispassionate process. It necessitates active and continual engagement with the data , leading to a process of reflection and interpretation that forms the basis of qualitative analysis. During the act of transcribing, researchers can glean new insights, recognize overlooked details, and begin to make initial interpretations. It's often during this process that the data begin to speak, allowing researchers to discern their meaning and value.

4. Providing evidence and establishing an audit trail: Transcripts constitute a concrete, verifiable record of the data collected, the words expressed by the participants, their sentiments, and their experiences. This record acts as a form of evidence to substantiate the research findings, ensuring their credibility. Furthermore, they provide an audit trail, contributing to the transparency, accountability, and, thus, the overall trustworthiness of the study.

Justifying the use of transcription for qualitative data

The crucial role of transcription in qualitative research is underscored by its ability to capture the richness and multifaceted nature of spoken data and convert it into a format ripe for in-depth analysis. It provides a lens through which subtle nuances of communication - the ebb and flow of conversation, shifts in tone, or emotional expressions - can be understood. This is invaluable in qualitative research, where the aim is to capture and understand the depth and complexity of human experiences.

Transcripts also serve as a durable, enduring record of the data, preserving the words and voices of the participants. They ensure that the insights, stories, and experiences shared by participants are not transient but can be revisited, reviewed, and reinterpreted in future research.

Transcription services have revolutionized the way researchers process their data, offering a range of possibilities from manual transcription to advanced AI-driven software. These services often come with their own benefits and drawbacks, and understanding these is key to making an informed decision for your qualitative research project. This section will delve into the world of transcription services, helping you to explore your options and make the best choice for your research needs.

Types of transcription services

Broadly, transcription services fall into two main categories: human services and automated services.

Human transcription services employ professional transcribers to convert your audio or video files into text. These services often offer high-quality, accurate transcripts, as they benefit from the nuanced understanding and context interpretation abilities of a human transcriber.

Automated transcription services, on the other hand, use speech recognition software to transcribe audio or video files. They are typically faster and less expensive than human transcription services, but their accuracy can vary depending on the quality of the audio and the complexity of the language used.

Advantages and disadvantages of outside services

Choosing between human and automated transcription services often depends on your project's specific needs. Let's delve into some advantages and disadvantages of each.

Advantages of human services

1. Accuracy: Human transcribers can understand context, decipher accents, and make out words in poor-quality audio better than any software, ensuring high-quality transcripts.

2. Personalized service: They offer personalized service with attention to detail, including specific formatting requests or specialized transcription styles.

Disadvantages of human services

1. Time-consuming: Human transcription is slower than automated transcription, which can be an issue for projects with tight timelines.

2. Cost: Human transcription services can be expensive, especially for large volumes of data. Advantages of automated services

1. Speed: Automated services can transcribe audio or video files much faster than human transcribers.

2. Cost: They are usually more affordable than human transcription services, making them a good option for budget-conscious projects.

Disadvantages of automated services

1. Accuracy: While speech recognition technology has improved significantly, it still struggles with accents, poor audio quality, and complex terminology, which may lead to less accurate transcripts.

2. Lack of context: Automated services may not capture nuances in language or understand context the way a human transcriber can.

Tips for choosing the right service

Selecting the right transcription service should be based on the specific needs and constraints of your project. Here are a few tips to guide your choice:

1. Assess your needs: Consider the complexity of your data, the quality of your recordings, your budget, and your timeline.

2. Test the service: If possible, use a short sample of your data to test the service. This can give you a sense of the quality of the transcription and whether it meets your needs.

3. Read reviews: Check out reviews and ratings from other users to gauge the reliability and performance of the service.

These outside services can be a valuable resource in qualitative research, saving you time and effort. By understanding the benefits and drawbacks of human and automated services and evaluating your specific research needs, you can make an informed choice that best supports your research goals.

The transcription process, while invaluable to qualitative research , does not come without its fair share of challenges. The transformation of oral data into written format can be a complicated endeavor, particularly in cases where the audio quality is poor, speakers have heavy accents, or the conversation is filled with technical or specific jargon. Despite these hurdles, there are various strategies that can help you navigate these issues and ensure high-quality, accurate transcripts.

Audio quality

One of the most common challenges in transcription is dealing with poor audio quality. Background noise, low speaking volumes, or unclear pronunciations can make it difficult to distinguish what is being said. It's a good idea to invest in high-quality recording equipment and choose a quiet, controlled environment for your interviews or focus groups. Ensure that all participants speak clearly and loudly enough to be heard. If your data is already collected and the audio quality is poor, consider using noise-canceling software or hiring a professional transcription service that specializes in handling poor-quality audio.

Accents and dialects

Dealing with heavy accents or unfamiliar dialects can be challenging, particularly for automated transcription services that may not be programmed to handle a wide range of accents or dialects. Human transcribers can spend time familiarizing themselves with the accent or dialect to aid their comprehension. In some cases, it may be beneficial to engage a local transcriber who is familiar with the accent or dialect. For automated services, choosing a service that offers multilingual support or can handle a variety of accents can improve the accuracy of your transcripts.

Technical jargon and specific language

Transcribing conversations that include technical jargon, specific terminology, or industry-specific language can be a challenge, especially if the transcriber is not familiar with the terminology. If you are outsourcing your transcription to a human service, providing a glossary of terms to your transcriber can be very helpful. This can include definitions of technical terms, acronyms, or any specific language used in your study. If using an automated service, choose one that has capabilities to learn and adapt to specific terminology.

Time and resources

Transcription can be a time-consuming and resource-intensive process, especially for large volumes of data. Consider using transcription software or outsourcing to a transcription service to save time. If you’re transcribing manually, developing a systematic approach can increase efficiency. This can include using transcription software to speed up or slow down the audio, utilizing keyboard shortcuts, or creating a consistent formatting system.

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How to Transcribe an Interview: A Comprehensive Guide

Learn expert tips for accurate interview transcription. Master efficient techniques, tools, and best practices in this comprehensive guide.

An interview transcript is a written record of a spoken conversation, capturing every spoken word from an audio or video recording. It's an essential tool for converting spoken content into text, enabling easier analysis, sharing, and reference.

Verbatim transcripts capture every detail, including false starts, filler words, and pauses, maintaining the authenticity of the conversation.

Definition of Interview Transcript

Transcribing an interview offers numerous benefits. It helps enhance accessibility by providing a text version of audio content, making it useful for those with hearing impairments.

Additionally, it allows for thorough analysis and research, as you can easily search for specific information within the text. Transcripts also facilitate content repurposing, as you can transform spoken words into written articles, blog posts, or social media content.

​​Photo by Scott Graham on Unsplash

Benefits of Writing a Transcript

Before diving into the transcription process, proper preparation is key. Ensure that your audio recording is of high quality, as clear audio greatly simplifies the transcription task. Using a reliable audio recording device or software helps maintain optimal audio quality.

You can also use transcription software like Express Scribe to play and control the audio recording while typing out the transcript. For video files, extract the audio to transcribe the conversation accurately.

Consider using speaker labels and time stamps to distinguish different speakers and pinpoint specific moments in the conversation. By properly preparing for the interview and the subsequent transcription process, you'll pave the way for creating accurate and valuable interview transcripts.

Prepare Questions

Creating effective interview questions is the foundation of a successful transcription process. Craft questions that encourage detailed and informative responses, ensuring the conversation covers all relevant topics.

Clarity in your questions reduces the chances of misunderstanding during the interview, leading to accurate transcription later. Be mindful of speech patterns and potential hesitations to anticipate transcription challenges and improve the quality of the transcript.

Choose an Appropriate Location and Audio Equipment

Selecting the right environment for the interview is pivotal for accurate transcription. Opt for a quiet and controlled space to minimize background noise. Wearing noise-canceling headphones or a good pair of headphones can help the interviewer and interviewee hear each other clearly, facilitating accurate recording. A clear and accurate audio record is a foundation for creating precise and reliable transcripts.

Train Others on How to Take Notes and Record the Interview

If you're not the one conducting the interview, training others on effective note-taking and audio recording is crucial. Ensure they understand the importance of capturing both verbal and non-verbal cues.

For instance, noting John Smith speaking might help distinguish speakers in a multi-participant conversation. Teach them to recognize speech patterns, like throat clearing or pauses, to ensure these nuances are accurately transcribed.

Introducing your team to the proper tools, including transcription templates, noise-canceling headphones, or even professional transcription services, can contribute to producing accurate and polished transcripts.

Conducting the Interview

Conducting an interview skillfully is crucial for creating an accurate transcript. Ensure you introduce yourself, explain the purpose of the interview, and obtain consent for recording. Establish a comfortable atmosphere for the interviewee to open up. Pay attention to both verbal and non-verbal cues, as capturing these nuances enriches the transcript.

Introduce Yourself and Explain What You Are Doing

Begin by introducing yourself and clarifying the purpose of the interview. This not only creates a rapport but also ensures the interviewee is aware of the recording process. Let them know that their insights are valued and that their words will be transcribed for later reference. This level of transparency sets the tone for a successful and collaborative interview.

Ask Open-Ended Questions

Crafting open-ended questions promotes in-depth responses, providing valuable content for your transcript. Instead of asking yes or no questions, inquire about experiences, opinions, or explanations. Open-ended questions encourage the interviewee to express themselves freely, facilitating a comprehensive and rich transcript.

Give Time for Answers and Respond Appropriately

During the interview, allow ample time for the interviewee to respond. Avoid interrupting or finishing their sentences, as this can disrupt the natural flow of conversation. A transcript with complete answers captures the essence of the interviewee's thoughts accurately. Responding appropriately to their answers conveys your interest and encourages them to elaborate, further enhancing the transcript's depth.

Ask Follow-Up Questions

Follow-up questions help clarify responses, delve deeper into topics, and extract additional information. When an interviewee provides an intriguing insight, posing follow-up questions uncovers more details.

This enriches the content of your transcript and contributes to a more comprehensive understanding of the interviewee's perspective. Skilled transcribers can capture these nuances effectively, ensuring your transcript retains its richness and accuracy.

Once you have conducted a well-structured interview, the next step is to ensure your transcript captures every detail accurately. This might require manual effort if you're not utilizing automatic transcription tools.

Skilled transcribers, well-versed in handling various accents, non-verbal cues, and even correcting grammar errors, can ensure your transcript reflects the actual person's words with a high level of accuracy. Their expertise and attention to detail eliminate the need for extensive post-interview editing, saving you time and ensuring a polished final transcript.

End the Interview Gracefully

Concluding the interview on a positive note is essential for maintaining rapport and professionalism. Express gratitude for the interviewee's time and insights. Ask if they have any additional points to share.

Clarify any unclear responses and ensure they have a chance to ask questions. This thoughtful closure leaves a lasting impression and encourages future engagement.

Transcribing the Interview

Transcribing an interview is a meticulous process that demands attention to detail. Listening to the recording multiple times is the first step. This familiarizes you with the content and helps capture nuances. If you're aiming for accurate interview transcription, utilizing professional transcription services or tools is recommended.

Dive.io, an AI-powered transcription tool, stands out in this regard. With its accurate speaker identification and time codes, Dive.io ensures a seamless accuracy level that's crucial for maintaining the essence of the conversation.

Listen to the Recording Multiple Times

After concluding the interview, carefully transcribing the conversation is pivotal for preserving its essence. Listen to the recording multiple times, allowing you to familiarize yourself with the interview's flow, speakers' voices, and any subtleties. This attentive approach significantly enhances the accuracy of the transcription.

Use Transcription Software or Services if Necessary

For interviews demanding a high degree of accuracy, transcription software or professional services are invaluable. Dive.io, an AI -powered transcription tool, offers a solution tailored for interview transcription.

Its ability to accurately identify speakers and provide time codes ensures your transcript mirrors the original conversation's nuances. This is particularly important for qualitative research interviews and legal documents. Dive.io's effectiveness is rooted in its AI technology, which delivers seamless accuracy and saves valuable time, allowing you to focus on extracting meaningful insights .

Whether you're conducting interviews for market research in the healthcare industry, legal documents, or business decisions, accurate interview transcription is vital. Utilizing sophisticated transcription solutions like Dive.io streamlines the process, ensuring effective communication and accurate documentation of crucial conversations.

This is particularly relevant in industries like healthcare, where quality care, precise communication, and legal compliance are paramount. Dive.io emerges as a dependable tool, bridging language barriers and ensuring your transcripts reflect the interview's substance with a high degree of accuracy.

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Qualitative Data Analysis: Transcription

• Atlas.ti web
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• Planning Qual Data Analysis
• Free Tools for QDA
• QDA with NVivo
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• QDA with MAXQDA
• PKM for QDA
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• Working Collaboratively
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• Transcription
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Transcription as an Act of Analysis

While transcription is often treated as part of the data collection process, it is also an act of analysis (Woods, 2020). When you manually transcribe an interview, for example, you make choices about how to turn the recording of the interview into text, and these decisions shape the analysis you conduct. 

For example, if you host a focus group, a transcription that just includes the words spoken by the participants loses data about the interaction between them. You may decide to ensure that your transcription includes details on interactions (which would take more time or resources) or decide that interaction information is not relevant to your analysis. This decision is influenced by your methodology and research goals, and should be recognized as a part of your analysis process. 

Planning and communicating the transcription process is further complicated when the researcher works in a research team, asks participants to discuss sensitive topics, occurs in a cross-cultural environment, or when the transcript must be translated into another language (Clark et al, 2017). Published research reports rarely include significant detail about the transcription process, but if you find yourself in one of these situations, it may be worth seeking works in your discipline that address best practices for transcription, data management, participant relationships, and translation, such as Clark et al's (2017) work on developing a transcription and translation protocol for sensitive and cross-cultural team research. 

Transcription Tools

• Atlas.ti (Mac)
• Atlas.ti (Windows)
• NVivo (Windows)
• NVivo (Mac)
• Kaltura/Mediaspace
• Free transcription tools
• Paid transcription services
• Importing automatic transcripts into Atlas.ti (Mac) You can import transcripts and media files from Zoom, Teams, and other video meeting platforms. Atlas.ti links the video and automatic transcript, which allows you to watch the video and edit the transcript right in Atlas.
• Creating transcripts in Atlas.ti You can import media files to Atlas.ti and then create your own transcript within the program. This process will create a transcript that is synced with the media file.
• Link a transcript to media in Atlas.ti You can import existing media and transcripts to Atlas.ti in order to link them together and enable synchronous viewing of the media with links to the transcript.
• Importing automatic transcripts into Atlas.ti (Windows) You can import transcripts and media files from Zoom, Teams, and other video meeting platforms. Atlas.ti links the video and automatic transcript, which allows you to watch the video and edit the transcript right in Atlas.
• Create a transcript in Atlas.ti You can import media files to Atlas.ti and create your own transcript within the program. The transcript will be linked to the media for synchronous scrolling.
• Link a transcript to a media file If you transcript text already, you can upload a media file to Atlas.ti and link the text. This will allow you to use synchronized scrolling, which shows you the video and transcript at the same time.
• Create transcripts in NVivo You can create new transcriptions of media in your NVivo project using the edit mode.
• Import and link transcripts in NVivo Existing transcripts can be imported to NVivo and link the transcript with a media file.

The MAXQDA is the same across Mac and Windows devices. 

• Manual Transcription You can upload media files to MAXQDA and then create new transcripts using the Multimedia Browser.
• Link transcripts to a media file by creating timestamps If you already have transcript text, you can use the edit mode in MAXQDA to create timestamps and sync the transcript to the media file.
• Automatic transcription New to MAXQDA 24, you can now automatically transcribe your media.
• Downloading captions from Kaltura Video files you upload to Kaltura (including recorded Zoom meetings) are automatically captioned, though you'll need to edit the captions and publish them before they appear on your video. Once the file is created, you can download it from Kaltura to upload to other programs. See this page on captions in Kaltura for more information.
• Find and replace text in Word When you download captions from Zoom or Kaltura, it will come with timestamps. You can use the find and replace feature in Word to clear the timestamps for easier editing.
• OTranscribe OTranscribe is a free, open-source and web browser based tool for transcribing audio and video. You can upload media and use the tool to create citations. See the help pages for information.
• Google Docs Voice Typing You can use the voice typing feature to create rough transcriptions of audio as you collect data or by re-playing a recording into the microphone.
• Microsoft Word Dictate Typing Web and desktop versions of Microsoft Word include a dictation tool that will create a rough transcription while you collect data or when you play a recording near your device's microphone.

There are companies that will create transcripts from media files on your behalf, usually for a by-minute fee. 

If you decide to use one of these options, you should ensure that the security of data shared with these services is in compliance with your IRB protocol and consent obtained from any participants. 

Do you have experience with any paid transcription services that you think would be worth adding to this list? Please share your experience with me . 

• NVivo Transcription NVivo offers a paid transcription service, which can be purchased as a paid subscription or a pay-as-you-go service. Transcription is available in 42 languages including English, Spanish, Japanese, Hindi, Arabic and Korean.
• Rev Ref offers both automatic, rough transcription as well as more accurate transcription conducted by workers. Rev supports 36 languages including English, Spanish, Arabic, Mandarin, Japanese, Korean, and Hindi.
• Trint Trint is a paid transcription and analysis tool, with transcription available for 30+ languages , including English, Spanish, Chinese Mandarin, Korean, Hindi, and Korean. Trint also offers translation of text.

Cited on this page

Clark, L., Birkhead, A. S., Fernandez, C., & Egger, M. J. (2017). A transcription and translation protocol for sensitive cross-cultural team research . Qualitative Health Research , 27 (12), 1751–1764. https://doi.org/10.1177/1049732317726761

Woods, D. Presentation in: Christina Silver, Phd. (2020, December 4).  CAQDAS webinar 005 Transcription as an analytic act.  [Video].  https://www.youtube.com/watch?v=7X-s1r4l0QQ.  

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• URL: https://guides.library.illinois.edu/qualitative

Transcription and Qualitative Methods: Implications for Third Sector Research

• Research Papers
• Published: 10 September 2021
• Volume 34 , pages 140–153, ( 2023 )

Cite this article

• Caitlin McMullin   ORCID: orcid.org/0000-0002-7029-9998 1  

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While there is a vast literature that considers the collection and analysis of qualitative data, there has been limited attention to audio transcription as part of this process. In this paper, I address this gap by discussing the main considerations, challenges and implications of audio transcription for qualitative research on the third sector. I present a framework for conducting audio transcription for researchers and transcribers, as well as recommendations for writing up transcription in qualitative research articles.

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Introduction

The field of third sector studies is inherently interdisciplinary, with studies from political science, management, sociology and social work, among others. Within the field of research, a large percentage (between 40–80%) of studies employ qualitative methods such as interviews, focus groups and ethnographic observations (von Schnurbein et al., 2018 ). In order to ensure rigor, qualitative researchers devote considerable time to developing interview guides, consent forms and coding frameworks. While there is a vast literature that considers the collection and the analysis of qualitative data, there has been comparatively limited attention paid to audio transcription, which is the conversion of recorded audio material into a written form that can be analyzed. Despite advances made in qualitative methodologies and increasing attention to positionality, subjectivity and reliability in qualitative data analysis, the transcription of interviews and focus groups is often presented uncritically as a direct conversion of recorded audio to text. As technology to facilitate transcription improves, many researchers have shifted to using voice-to-text software and companies that employ AI rather than human transcription. These technological advances in transcription, along with shifts in the way that research is undertaken (for example, increasingly via video conferencing as a result of the COVID-19 pandemic), mean that the need to critically reflect upon the place of transcription in third sector research is more urgent.

In this article, I explore the place of transcription in qualitative research, with a focus on the importance of this process for third sector researchers. The article is structured as follows. First, I review the qualitative methods literature on audio transcription and the key themes that arise. Next, I report on a review undertaken of recent qualitative research articles in Voluntas and the way that authors discuss transcription in these articles. Finally, I propose a framework for qualitative third sector researchers to include transcription as part of their research design and elements to consider in including descriptions of the transcription process in writing up qualitative research.

Audio Transcription: What We Know

At a basic level, transcription refers to the transformation of recorded audio (usually spoken word) into a written form that can be used to analyze a particular phenomenon or event (Duranti, 2006 ). For many qualitative researchers, transcription has become a fairly taken-for-granted aspect of the research process. In this section, I review the methods literature on the process of audio (and video) transcription as part of qualitative research on the third sector, focusing on three key areas—how transcription is undertaken, epistemological and ethical considerations, and the role of technology.

Qualitative research and transcription

While quantitative research seeks to explain, generalize and predict patterns through the analysis of variables, qualitative research questions are more interested in understanding and interpreting the socially constructed world around us (Bryman, 2016 ). This means that data are collected through documents, observation and interviews, and the latter are often recorded in order to analyze these as documents. For third sector research, recordings are most commonly made of interviews and focus groups, but may also be of meetings, events and other activities to ensure that researchers do not have to rely on their power of recall or scribbled notes.

Transcription is a notoriously time-consuming and often tedious task which can take between three hours and over eight hours to transcribe one hour of audio, depending on typing speed. Transcription is not, however, a mechanical process where the written document becomes an objective record of the event—indeed, written text varies from the spoken word in terms of syntax, word choice and accepted grammar (Davidson, 2009 ). The transcriber therefore has to make subjective decisions throughout about what to include (or not), whether to correct mistakes and edit grammar and repetitions. This has been described as a spectrum between “naturalized” transcription (or “intelligent verbatim”) which adapts the oral to written norms, and “denaturalized” transcription (“full verbatim”), where everything is left in, including utterances, mistakes, repetitions and all grammatical errors (Bucholtz, 2000 ).

While some contend that denaturalized transcription is more ‘accurate’, the same can equally be argued for naturalized, as it allows the transcriber to omit occasions when, for instance, an individual mis-speaks and corrects themselves, thereby allowing the transcriber to record closer to what was intended and how the interviewee might have portrayed themselves in a written form. As Lapadat ( 2000 , p. 206) explains, “Spoken language is structured and accomplished differently than written text, so when talk is re-presented as written text, it is not surprising that readers draw on their knowledge of written language to evaluate it.” Other nonverbal cues, such as laughter, tone of voice (e.g. sarcasm, frustration, emphasis) and the use or omission of punctuation, can also drastically alter the meaning or intention of what an individual says. In addition, the transcriber must make decisions about how much contextual information to include, such as interruptions, crosstalk and inaudible segments (Lapadat, 2000 ). Because of the range of types of research that employ qualitative methods, there is no single set of rules for transcription but rather these decisions must be based on the research questions and approach.

Epistemological and Ethical Considerations

Because the researcher (or external transcriber) must make these decisions as they translate audio into written text, transcription is an inherently interpretative and political act, influenced by the transcriber’s own assumptions and biases (Jaffe, 2007 ). Every choice that the transcriber makes therefore shapes how the research participant is portrayed and determines what knowledge or information is relevant and valuable and what is not. Indeed, two transcribers may hear differently and select relevant spoken material differently (Stelma & Cameron, 2007 ). As Davidson ( 2009 ) notes (and as I explore in further detail in the next section), despite being a highly interpretive process, transcription is frequently depicted using positivist norms of knowledge creation.

Transcription also involves potential ethical considerations and dilemmas. When working with disadvantaged communities, deciding how to depict research participants in written text can highlight the challenges of ethical representation. As Kvale ( 1996 , pp. 172–3) notes, “Be mindful that the publication of incoherent and repetitive verbatim interview transcripts may involve an unethical stigmatization of specific persons or groups of people”. Oliver et al. ( 2005 ) similarly demonstrate how transcribers must make decisions about how to represent participants’ use of slang, colloquialisms and accents in ways that are accurate but also respectful of the respondent’s intended meaning. Some researchers decide to send finished transcriptions to interviewees for approval in order to honor commitments to fully informed consent, to ensure transcription accuracy or in some cases as a means to address the balance of power between the researcher and interviewee. As Mero-Jaffe ( 2011 ) describes, on the one hand, this may empower interviewees to control the way that they are portrayed in the research. On the other hand, Mero-Jaffe found that seeking transcript approval from interviewees sometimes increased their embarrassment at the way that their statements appear in text. This may be especially problematic with full verbatim transcriptions.

Technology and Transcription

As technology improves and AI becomes increasingly able to create written text from recorded audio, researchers might ask—is human transcription even necessary? New options in Computer Assisted Qualitative Data Analysis Software (CAQDAS) such as NVivo, Atlas.ti and MAXQDA give qualitative researchers the option to forgo audio-to-text transcription altogether, and instead engage in live coding of audio or video files. Using this method, researchers first watch or listen to recordings to code for nonverbal cues, followed by a stage of note taking and coding based on pre-defined themes and matching these with time codes and nonverbal cues. Finally, researchers then transcribe specific quotes of interest from the recording (Parameswaran et al., 2020 ). This process may improve immersion in the data and allow researchers to account for dynamics that are often lost in complete audio-to-text transcription, such as group interactions and nonverbal communication.

There is a considerable need to develop the evidence base on the role of AI in transcription for qualitative research, with many important publications that consider the issue (e.g. Gibbs et al., 2002 ; Markle et al., 2011 ) out-of-date given the swift rate of change in AI technologies. Over the last few years, voice and speech recognition technologies have improved dramatically and may now be able to provide researchers with “good enough” first drafts of transcripts (Bokhove & Downey, 2018 ), providing certain conditions are in place (e.g. limited number of speakers and excellent audio quality). Using these technologies can save researchers time and money. As a result of the COVID-19 pandemic, many qualitative researchers are now undertaking interviews over Zoom or other video conferencing apps, which is a trend that may continue beyond the pandemic (Dodds & Hess, 2020 ). Zoom offers AI live transcription options, which benefits from the generally clear audio quality of a video conference, compared to in-person interviews where there is a greater chance of audio interference and background noise that may be undetected in the moment.

While AI may offer a cheaper and quicker alternative to human transcription, these transcripts will need to be meticulously checked by the researcher to ensure accuracy, fill in missing details or edit for context and readability. Using cloud-based AI transcription services also raises potential ethical concerns about data protection and confidentiality (Da Silva, 2021 ). There are numerous subjective decisions made in the course of creating a transcription that AI is unable to process, such as where to include punctuation, which words to include or exclude (such as filler words, hesitations, etc.) and how to denote things such as interruptions, hesitations and nonverbal cues. Voice-to-text software is also generally less accurate in discerning multiple voices or different accents (Bokhove & Downey, 2018 ). Several studies have considered how researchers/transcribers can use voice recognition software to listen and repeat the spoken text of an interview into software as a shortcut to traditional typing transcription (Matheson, 2007 ; Tilley, 2003 ), but the above shortcomings and cautions apply.

Transcription and Third Sector Research

Transcription matters for third sector research because qualitative research methodologies make up a large percentage of studies undertaken on nonprofits—as much as 40–80% of research published in this field (Igalla et al., 2019 ; Laurett & Ferreira, 2018 ; von Schnurbein et al., 2018 ). Audio transcription is particularly important for third sector research for several reasons. In conducting qualitative research (which aims to produce rich, rigorous description) and as third sector researchers (who study organizations that seek to improve society and who may be working with traditionally disenfranchised or disadvantaged communities), we have a particular ethical obligation to ensure that our research provides an accurate depiction of our participants’ lives and the organizations with which they are involved.

However, transcription is perhaps the most underacknowledged aspect of the qualitative research process, and this is also evident in the way that transcription is discussed in research articles. In order to survey the current depiction of the transcription process in third sector research, I undertook a review of the 212 most recent papers in Voluntas that include the word ‘interview’ to explore how qualitative research articles discuss transcription as part of their methodology. Footnote 1 Of these papers, 79 were deemed not applicable (because they were quantitative research papers that mentioned interviews in another context, or used the word interview to denote the administering of a structured questionnaire, or systematic review papers reporting on other research). This left 133 articles which were analyzed to explore the extent to which transcription was described—if at all—as part of the research methodology. Footnote 2

The analysis (illustrated in Fig.  1 ) found that 41% of papers employing interviews as a research method did not mention transcription at all, while 11% mentioned transcripts but not the process of transcription. It was not clear from these whether or not interviews were recorded or if researchers relied upon written notes taken during interviews, or how information from the oral interview was converted into analyzable text. The most common discussion of transcription (19%) was a simple sentence along the lines of “interviews were recorded and transcribed”, while 26% gave some further information including who undertook the transcription (the researcher(s), a research assistant or a commercial company) or that the interviews were transcribed ‘verbatim’ (with none explaining what they mean by this term). These findings are not dissimilar to a study of qualitative research in nursing, where it was found that 66% of articles reporting solely that interviews were transcribed, and the remaining articles indicated only “full” or “verbatim” to clarify the process (Wellard & McKenna, 2001 ). I also surveyed the first authors’ departmental affiliations/field of study to gauge any differences between academic fields (Table 1 ) although there were not considerable differences.

Transcription in Voluntas qualitative articles

The fact that over half of the Voluntas articles using interviews as a research method make no mention of the transcription process is a problem for transparency in qualitative research. This tendency may be a symptom of the fact that qualitative researchers face greater challenges in academic publishing that disadvantage longer from, in-depth qualitative research to fit within prescribed word limits (Moravcsik, 2014 ). In researchers’ efforts to ensure that qualitative research meets requirements for transparency, rigor and reliability, efforts are concentrated on descriptions of case and participant selection and data analysis while transcription as the conduit between data collection and analysis remains unproblematized. This emphasis reflects the growing influence of positivist views of validity. Ignoring the subjective decisions and theoretical perspectives that determine the creation of a transcript therefore inadvertently presupposes a positivist stance on the objective nature of data which is inconsistent with qualitative methodologies.

A Framework for Undertaking and Reporting on Transcription

As shown in the previous section, there is currently widespread neglect of transcription as part of interpretive qualitative research on the third sector. In this section, I present key elements for third sector researchers to consider in regard to transcription, both to ensure rigor as part of the qualitative research process and in writing up qualitative research, drawing upon examples of good practice from previous research in Voluntas. These recommendations are based on a review of the literature as well as my personal experience as a qualitative researcher, qualitative methods teacher, and professional transcriber.

Before Transcribing: Ethics and Data Management

All decisions regarding research design, data collection and data management should be made at the beginning of a qualitative research project when applying for ethical/IRB approval from one’s university, and this includes transcription. At this stage, the researcher should confirm with their university whether they have a budget for transcription. Undertaking ethical qualitative research means ensuring standards of transparency, informed consent, confidentiality and protection of the data obtained from the research (Blaxter et al., 2001 ). Increasing concerns about data protection and legislation such as GDPR in the European Union have prompted many universities to institute strict rules about where research data can be stored. Some universities do not allow the use of certain cloud servers, such as Dropbox. These considerations should be taken into account when deciding how to undertake and record interviews (Da Silva, 2021 )—for instance, if you are recording using your mobile phone, it is important to be sure you know whether recordings automatically upload to the cloud. For this reason, it may be preferable to use a traditional digital recorder so you can manually download the files to your computer and know exactly where everything is saved.

Before Transcribing: The Interview

Before transcription can even be considered, researchers must ensure that they have a suitable audio recording, which begins with the interview itself—whenever possible, interviews should be conducted in a quiet environment without background noise or interruptions and the audio recording device should be placed close enough to the respondent to pick up their voice clearly. While recording interviews with a mobile phone has become increasingly common and easy, using a backup recording device is always a good idea to mitigate against flat batteries, full memory cards, and human error. If recording with your mobile phone, it’s also critical to remember to place it on airplane mode/‘do not disturb’ for the duration of the interview.

To Transcribe or Not to Transcribe?

While transcription from audio recordings is considered standard practice in qualitative research (Tracy, 2019 ), it is not the only way of undertaking qualitative interviews, and it is important to note that there are many reasons why it may not be desirable, appropriate or possible to record interviews at all. In relation to third sector research, this is most commonly the case in community-based research, research with political elites or research in challenging environments. One article explained that they did not record interviews because: “In sectors marked by fear, intimidation, and strong security apparatuses, recording devices would almost certainly have led to self-censorship and limited our access.” (Atia & Herrold, 2018 , p. 1046). Similarly, researchers may be unable to record in community settings because of sub-optimal recording conditions (e.g. meeting outside, noisy environments, etc.) or because using recording device makes participants uncomfortable or reinforces power relations between the researcher and participants (Quintanilha et al., 2015 ).

If researchers decide not to comprehensively transcribe recordings, or decide not to record qualitative fieldwork at all, this should be noted and explained in relation to methods. Other methods of notetaking and analysis may be more suited to certain types of ethnographic research, such as reflexive journaling (Halcomb & Davidson, 2006 ), or Systematic and Reflexive Interviewing and Reporting—a process by which a researcher and research assistant jointly interview participants and write their own reports that include observations and analyses, which are collaboratively analyzed (Loubere, 2017 ).

How to Transcribe?

Traditionally, transcribers used foot pedals to play, rewind and fast forward tape recordings while they typed. Now that audio files are digital, several free and low cost programs are available (such as Express Scribe and oTranscribe) that let transcribers set up hot keys to perform the same actions without having to navigate away from their transcript document.

The degree of detail to include in transcripts should be decided upon before interviews are transcribed. This is important because previous research has demonstrated that the format selected for transcription significantly impacts how the researcher interprets the data (Mishler, 2003 ; Packer, 2017 ). There is no one best or “most accurate” style of transcription, but rather, a researcher should consider the particular theoretical background and research questions of the study in order to determine where on the scale of full verbatim to intelligent verbatim is most appropriate for the study. Because third sector research is most commonly associated with social science and business disciplines rather than linguistics, it will rarely be necessary or appropriate to employ the conventions of conversation analysis or extreme levels of denaturalized transcription (Bucholtz, 2000 ). Indeed, it might most frequently be appropriate to employ a version of naturalized/intelligent verbatim, so that any participants’ quotes included in written works are more ‘readable’ and do not include excessive repetitions or verbal fillers such as ‘um’.

If the researcher determines that naturalized or intelligent verbatim transcription is the most appropriate for their study, several considerations should be heeded in order to ensure that meaning is not distorted or lost. First, indications of laughter, nonverbal cues (such as sighs, huffs, finger-snaps, sobbing or even blowing raspberries) should be included if these convey important meaning. Other considerations of how to transcribe may be based more on personal preference and the ability to produce a document that is easily analyzable in the researcher’s chosen medium. For instance, wide margins on one side can be useful for researchers who choose to analyze their data on paper or in Microsoft Word, while other more flowing templates will work better to import into software such as NVivo. It can also be useful to include time stamps for unclear or inaudible statements, or at regular intervals (e.g. every minute) which makes it much easier to check a transcript against the original audio.

Who Transcribes?

As discussed in the consideration of qualitative studies, the prevalence of the passive voice when reporting on transcription (i.e. “interviews were transcribed”) obscures the important distinction of who undertook the transcription. If the researcher transcribes recordings themselves, then it is generally acceptable to assume the coherence between the research approach and approach to transcription, as well as the researcher’s confidence that the written transcript is an accurate record of the event/interview that took place. If, however, the researchers choose to outsource transcription to a research assistant or commercial transcription company, then care should be taken to give detailed and thorough instructions about the elements described above. The researcher should also spot check transcripts for accuracy, fill in any missed words/inaudibles and ensure that the transcription document fulfils their expectations in regard to level of verbatim, style and formatting.

Ideally, transcribers should be hired who have specialist knowledge of the subject matter and familiarity with the accents or dialect of the speakers. They should be provided with a key information about the project, such as the research questions, important terms and acronyms. Lapadat ( 2000 ) provides several useful suggestions when hiring transcribers in order to ensure transcription quality and increase rigor. First, rather than fully outsourcing transcription, the researchers can transcribe some interviews or portions of interviews themselves in order to provide an example for transcribers and develop a transcription protocol. Another option when employing research assistants to transcribe interviews is to include them directly in the interviews (either as a co-interviewer or observer), so they have direct involvement in the research and context.

Finally, when working with external transcribers it can also be valuable to encourage transcribers to keep memos of the transcription process or contextual observations and impressions that may not come through in the written text. For instance, does the interviewee sound tired, frustrated, distracted or nervous? Does the interviewer interrupt the respondent frequently (which the transcriber may choose to edit for readability)? Or did the interview take place somewhere public, like a cafe, which may have made the respondent more guarded? Such information is often lost, particularly in projects that involve multiple research team members (for instance, a PI, multiple interviewers, research assistants and/or professional transcribers).

Writing about Transcription

Due to limited space or word limits, it is not typically possible or desirable to include all of the above details in research articles. Instead, at a minimum, researchers should include who transcribed the audio recordings as part of a commitment to ethical and transparent qualitative research. If this was done by anyone other than the researchers, authors should ideally describe the measures taken to ensure accuracy (developing a protocol for transcribers, spot checking, proofreading, sending transcripts to interviewees if appropriate) and ethical considerations (such as data protection and confidentiality).

Second, researchers should indicate the type of transcription—whether selective (pulling out relevant quotes and themes, or transcribing just the ‘gist’), intelligent verbatim/naturalized or full verbatim/denaturalized. The choice of type of transcription should align to the researcher’s epistemological position and theoretical framework.

Finally, researchers should include any other subjective decision-making that took place during the transcription process, in much the same way that researchers are encouraged to be transparent about their subjectivity and positionality in undertaking interviews and analysis of qualitative data (McCorkel & Myers, 2003 ). This may include information about selecting the level of verbatim, working with external transcribers, feedback from interviewees on transcripts or efforts to ensure accuracy of transcripts and coherence with the research approach.

The following quotes provide good examples of how to write about transcription:

The interviews, which were conducted in the native language of the interviewees by six female Hebrew-Arabic-speaking interviewers, were recorded, translated, and transcribed verbatim. […] Immediately following the interview, each interviewer transcribed and translated her interviews into Hebrew. In this manner, we sought to achieve a translation that was as close as possible to the interviewer’s insights regarding the participants, and we regarded the interviewers as active agents in the creation of knowledge. (Yanay-Ventura et al., 2020 , p. 6) Three Spanish speaking investigators transcribed all of the interviews from audio recording devices, checked each other’s transcription for accuracy, and analyzed the interviews using thematic analysis (Braun & Clarke, 2006 ). The transcribers observed the focus groups and took notes on participants’ voices and other identifying traits to help the transcription process go more smoothly. Researchers aided the transcribers in this regard by asking participants at the beginning of the focus groups to introduce themselves using a pseudonym and briefly remark upon how they preferred to spend their time. (Schwingel et al., 2017 , p. 170)

In both of these examples, the authors treat the process of transcription as part of the broader research process, rather than as an automatic conversion of audio to text. While there is limited clarification about the type of transcription (beyond ‘verbatim’), the discussion of the subjective decision-making as part of the transcription process and acknowledgment of the agency of the individuals undertaking transcription increases transparency and therefore rigor.

Conclusions

Qualitative research can help us to understand some of the important issues impacting the third sector in ways that quantitative methods fall short of explaining, such as the ways that individuals and organizations make sense of public policy and societal challenges, how and why organizations design their services and activities in particular ways, and the intricacies of the relationships between boards, executives, staff and volunteers. Qualitative methods training stresses that an interpretivist epistemological position sees knowledge as socially constructed, yet transcription has slipped through the cracks of methodological examination in the process of creating and interpreting meaning.

In this short article, I sought to draw our attention to this important stage of qualitative data collection and analysis and call on third sector researchers to critically reflect upon transcription both in conducting research and in writing about it. I have focused primarily on the transcription of interviews , rather than focus groups or other multi-person events. All of the points raised in my framework transcription apply to these methods of data collection as well; however, there are further issues that need to be taken into consideration regarding focus groups that warrant further attention, such as the issues of power and accuracy of transcription when there are multiple people speaking and interrupting one another. Researchers employing multi-person recordings should therefore devote more time and consideration to transcription. Finally, technology continues to advance in the area of voice recognition, which may save researchers considerable time and/or money in transcription; however, I implore scholars to see transcription through an interpretivist rather than positivist lens, to ensure that the production of written transcripts is not approached as the creation of objective knowledge.

While this approach may have obscured other methods that employ transcription, such as focus groups, the intention of the survey is to provide a snapshot illustration of transcription and qualitative methods rather than a systematic review.

Articles reviewed are listed in Appendix 1.

Atia, M., & Herrold, C. E. (2018). Governing through patronage: The rise of NGOs and the fall of civil society in Palestine and Morocco. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 29 (5), 1044–1054. https://doi.org/10.1007/s11266-018-9953-6

Article   Google Scholar  

Blaxter, L., Hughes, C., & Tight, M. (2001). How to research (2nd ed.). Open University Press.

Bokhove, C., & Downey, C. (2018). Automated generation of “good enough” transcripts as a first step to transcription of audio-recorded data. Methodological Innovations , 11 (2). https://doi.org/10.1177/2059799118790743 .

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology , 3 (2), 77–101. https://doi.org/10.1191/1478088706qp063oa .

Bryman, A. (2016). Social research methods (5th ed.). Oxford University Press.

Bucholtz, M. (2000). The politics of transcription. Journal of Pragmatics, 32 (10), 1439–1465. https://doi.org/10.1016/S0378-2166(99)00094-6

Da Silva, J. (2021). Producing ‘good enough’ automated transcripts securely: Extending Bokhove and Downey (2018) to address security concerns. Methodological Innovations, 14 (1), 2059799120987766. https://doi.org/10.1177/2059799120987766

Davidson, C. (2009). Transcription: Imperatives for qualitative research. International Journal of Qualitative Methods, 8 (2), 35–52.

Dodds, S., & Hess, A. C. (2020). Adapting research methodology during COVID-19: Lessons for transformative service research. Journal of Service Management, 32 (2), 203–217. https://doi.org/10.1108/JOSM-05-2020-0153

Duranti, A. (2006). Transcripts, like shadows on a wall. Mind, Culture, and Activity, 13 (4), 301–310. https://doi.org/10.1207/s15327884mca1304_3

Gibbs, G. R., Friese, S., & Mangabeira, W. C. (2002). View of the use of new technology in qualitative research. Orum Qualitative Sozialforschung/Forum: Qualitative Social Research , 3 (2). https://www.qualitative-research.net/index.php/fqs/article/view/847/1840

Halcomb, E. J., & Davidson, P. M. (2006). Is verbatim transcription of interview data always necessary? Applied Nursing Research, 19 (1), 38–42. https://doi.org/10.1016/j.apnr.2005.06.001

Igalla, M., Edelenbos, J., & van Meerkerk, I. (2019). Citizens in action, what do they accomplish? A systematic literature review of citizen initiatives, their main characteristics, outcomes, and factors. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 30 (5), 1176–1194. https://doi.org/10.1007/s11266-019-00129-0

Jaffe, A. (2007). Variability in transcription and the complexities of representation, authority and voice. Discourse Studies, 9 (6), 831–836. https://doi.org/10.1177/1461445607082584

Kvale, S. (1996). Interviews: An introduction to qualitative research interviewing . Sage.

Lapadat, J. C. (2000). Problematizing transcription: Purpose, paradigm and quality. International Journal of Social Research Methodology, 3 (3), 203–219. https://doi.org/10.1080/13645570050083698

Laurett, R., & Ferreira, J. J. (2018). Strategy in nonprofit organisations: A systematic literature review and agenda for future research. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 29 (5), 881–897. https://doi.org/10.1007/s11266-017-9933-2

Loubere, N. (2017). Questioning transcription: The case for the systematic and reflexive interviewing and reporting (SRIR) method. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research , 18 (2), Article 2. https://doi.org/10.17169/fqs-18.2.2739

Markle, D. T., West, R. E., & Rich, P. J. (2011). Beyond transcription: Technology, change, and refinement of method. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research , 12 (3), Article 3. https://doi.org/10.17169/fqs-12.3.1564

Matheson, J. L. (2007). The voice transcription technique: Use of voice recognition software to transcribe digital interview data in qualitative research. Qualitative Report, 12 (4), 547–560.

Google Scholar  

McCorkel, J. A., & Myers, K. (2003). What difference does difference make? Position and privilege in the field. Qualitative Sociology, 26 (2), 199–231. https://doi.org/10.1023/A:1022967012774

Mero-Jaffe, I. (2011). ‘Is that what I Said?’ Interview transcript approval by participants: An aspect of ethics in qualitative research. International Journal of Qualitative Methods, 10 (3), 231–247. https://doi.org/10.1177/160940691101000304

Mishler, E. (2003). Representing discourse: The rhetoric of transcription. In N. Fielding (Ed.), Interviewing . Sage.

Moravcsik, A. (2014). Transparency: The revolution in qualitative research. PS: Political Science & Politics , 47 (1), 48–53. https://doi.org/10.1017/S1049096513001789

Oliver, D. G., Serovich, J. M., & Mason, T. L. (2005). Constraints and opportunities with interview transcription: Towards reflection in qualitative research. Social Forces, 84 (2), 1273–1289. https://doi.org/10.1353/sof.2006.0023

Packer, M. J. (2017). The science of qualitative research . Cambridge University Press.

Book   Google Scholar  

Parameswaran, U. D., Ozawa-Kirk, J. L., & Latendresse, G. (2020). To live (code) or to not: A new method for coding in qualitative research. Qualitative Social Work, 19 (4), 630–644. https://doi.org/10.1177/1473325019840394

Quintanilha, M., Mayan, M. J., Thompson, J., & Bell, R. C. (2015). Different approaches to cross-lingual focus groups: Lessons from a cross-cultural community-based participatory research project in the ENRICH study. International Journal of Qualitative Methods, 14 (5), 1609406915621419. https://doi.org/10.1177/1609406915621419

Schwingel, A., Wiley, A., Teran-Garcia, M., McCaffrey, J., Gálvez, P., Hawn, R., Villegas, E., Coba, S., Vizcarra, M., Luty, G., Montez, R., & The Abriendo Caminos Promotora Project Group. (2017). More than help? Volunteerism in US Latino culture. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations , 28 (1), 162–183. https://doi.org/10.1007/s11266-016-9731-2

Stelma, J. H., & Cameron, L. J. (2007). Intonation units in spoken interaction: Developing transcription skills. Text & Talk, 27 (3), 361–393. https://doi.org/10.1515/TEXT.2007.015

Tilley, S. A. (2003). “Challenging” research practices: Turning a critical lens on the work of transcription. Qualitative Inquiry, 9 (5), 750–773. https://doi.org/10.1177/1077800403255296

Tracy, S. J. (2019). Qualitative research methods: Collecting evidence, crafting analysis (2nd ed.). John Wiley & Sons.

von Schnurbein, G., Perez, M., & Gehringer, T. (2018). Nonprofit comparative research: Recent agendas and future trends. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 29 (3), 437–453. https://doi.org/10.1007/s11266-017-9877-6

Wellard, S., & McKenna, L. (2001). Turning tapes into text: Issues surrounding the transcription of interviews. Contemporary Nurse, 11 (2–3), 180–186. https://doi.org/10.5172/conu.11.2-3.180

Yanay-Ventura, G., Issaq, L., & Sharabi, M. (2020). Civic service and social class: The case of young Arab women in Israel. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations . https://doi.org/10.1007/s11266-020-00210-z

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Appendix 1: Articles Reviewed

Aasland, A., Kropp, S., & Meylakhs, A. Y. (2020). Between Collaboration and Subordination: State and Non-state Actors in Russian Anti-drug Policy. Voluntas , 31 (2), 422–436. https://doi.org/10.1007/s11266-019-00158-9

Åberg, P., Einarsson, S., & Reuter, M. (2021). Think Tanks: New Organizational Actors in a Changing Swedish Civil Society. Voluntas , 32 (3), 634–648. https://doi.org/10.1007/s11266-019-00174-9

Afkhami, A., Nasr Isfahani, A., Abzari, M., & Teimouri, H. (2019). Toward a Deep Insight into Employee Participation in Employer-Supported Volunteering in Iranian Organizations: A Grounded Theory. Voluntas , 30 (5), 1036–1053. https://doi.org/10.1007/s11266-019-00141-4

Anasti, T. (2020). The Strategic Action Field of Sex Work and Sex Trafficking: A Case Study of a Contentious Field in Chicago. Voluntas , 31 (1), 169–183. https://doi.org/10.1007/s11266-019-00161-0

Appe, S. (2018). Directions in a Post-aid World? South–South Development Cooperation and CSOs in Latin America. Voluntas , 29 (2), 271–283. https://doi.org/10.1007/s11266-017-9838-0

Arhin, A. A., Kumi, E., & Adam, M.-A. S. (2018). Facing the Bullet? Non-Governmental Organisations’ (NGOs’) Responses to the Changing Aid Landscape in Ghana. Voluntas , 29 (2), 348–360. https://doi.org/10.1007/s11266-018-9966-1

Arvidson, M. (2018). Change and Tensions in Non-profit Organizations: Beyond the Isomorphism Trajectory. Voluntas , 29 (5), 898–910. https://doi.org/10.1007/s11266-018-0021-z

Atia, M., & Herrold, C. E. (2018). Governing Through Patronage: The Rise of NGOs and the Fall of Civil Society in Palestine and Morocco. Voluntas , 29 (5), 1044–1054. https://doi.org/10.1007/s11266-018-9953-6

Ávila, L., & Amorim, M. (2021). Organisational Identity of Social Enterprises: A Taxonomic Approach. Voluntas , 32 (1), 13–27. https://doi.org/10.1007/s11266-020-00264-z

Baillie Smith, M., Fadel, B., O’Loghlen, A., & Hazeldine, S. (2020). Volunteering Hierarchies in the Global South: Remuneration and Livelihoods. Voluntas . https://doi.org/10.1007/s11266-020-00254-1

Bandini, F., Gigli, S., & Mariani, L. (2021). Social Enterprises and Public Value: A Multiple-Case Study Assessment. Voluntas , 32 (1), 61–77. https://doi.org/10.1007/s11266-020-00285-8

Bano, M. (2019). Partnerships and the Good-Governance Agenda: Improving Service Delivery Through State–NGO Collaborations. Voluntas , 30 (6), 1270–1283. https://doi.org/10.1007/s11266-017-9937-y

Barinaga, E. (2020). Coopted! Mission Drift in a Social Venture Engaged in a Cross-Sectoral Partnership. Voluntas , 31 (2), 437–449. https://doi.org/10.1007/s11266-018-0019-6

Bayalieva-Jailobaeva, K. (2018). New Donor Strategies: Implications for NGOs in Post-Soviet Kyrgyzstan. Voluntas , 29 (2), 284–295. https://doi.org/10.1007/s11266-017-9878-5

Beaton, E. E. (2021). No Margin, No Mission: How Practitioners Justify Nonprofit Managerialization. Voluntas , 32 (3), 695–708. https://doi.org/10.1007/s11266-019-00189-2

Bergfeld, A., Plagmann, C., & Lutz, E. (2021). Know Your Counterparts: The Importance of Wording for Stakeholder Communication in Social Franchise Enterprises. Voluntas , 32 (1), 104–119. https://doi.org/10.1007/s11266-020-00289-4

Bidet, E., Eum, H., & Ryu, J. (2018). Diversity of Social Enterprise Models in South Korea. Voluntas , 29 (6), 1261–1273. https://doi.org/10.1007/s11266-018-9951-8

Bies, A., & Kennedy, S. (2019). The State and the State of the Art on Philanthropy in China. Voluntas , 30 (4), 619–633. https://doi.org/10.1007/s11266-019-00142-3

Bradford, A., Luke, B., & Furneaux, C. (2020). Exploring Accountability in Social Enterprise: Priorities, Practicalities, and Legitimacy. Voluntas , 31 (3), 614–626. https://doi.org/10.1007/s11266-020-00215-8

Cannon, S. M. (2020). Legitimacy as Property and Process: The Case of an Irish LGBT Organization. Voluntas , 31 (1), 39–55. https://doi.org/10.1007/s11266-019-00091-x

Carlsen, H. B., Doerr, N., & Toubøl, J. (2020). Inequality in Interaction: Equalising the Helper–Recipient Relationship in the Refugee Solidarity Movement. Voluntas . https://doi.org/10.1007/s11266-020-00268-9

Chatterjee, D., Subramanian, B., & Hota, P. K. (2020). Professionalization and Hybridization Dynamics of Social Enterprises. Voluntas , 31 (3), 457–471. https://doi.org/10.1007/s11266-020-00217-6

Chatzichristos, G., & Nagopoulos, N. (2020). Social Entrepreneurship and Institutional Sustainability: Insights from an Embedded Social Enterprise. Voluntas , 31 (3), 484–493. https://doi.org/10.1007/s11266-019-00188-3

Chaves-Avila, R., & Savall-Morera, T. (2019). The Social Economy in a Context of Austerity Policies: The Tension Between Political Discourse and Implemented Policies in Spain. Voluntas , 30 (3), 487–498. https://doi.org/10.1007/s11266-018-00075-3

Chewinski, M. (2019). Coordinating Action: NGOs and Grassroots Groups Challenging Canadian Resource Extraction Abroad. Voluntas , 30 (2), 356–368. https://doi.org/10.1007/s11266-018-0023-x

Clear, A., Paull, M., & Holloway, D. (2018). Nonprofit Advocacy Tactics: Thinking Inside The Box? Voluntas , 29 (4), 857–869. https://doi.org/10.1007/s11266-017-9907-4

Clerkin, B., & Quinn, M. (2019). Restricted Funding: Restricting Development? Voluntas , 30 (6), 1348–1364. https://doi.org/10.1007/s11266-018-00048-6

Cookingham Bailey, E. (2020). Advocacy and Service Delivery in the Voluntary Sector: Exploring the History of Voluntary Sector Activities for New Minority and Migrant Groups in East London, 1970s–1990s. Voluntas . https://doi.org/10.1007/s11266-020-00253-2

Crack, A. M. (2018). The Regulation of International NGOS: Assessing the Effectiveness of the INGO Accountability Charter. Voluntas , 29 (2), 419–429. https://doi.org/10.1007/s11266-017-9866-9

Crotty, J., & Ljubownikow, S. (2020). Creating Organisational Strength from Operationalising Restrictions: Welfare Non-profit Organisations in the Russian Federation. Voluntas , 31 (6), 1148–1158. https://doi.org/10.1007/s11266-020-00271-0

Deng, G. (2019). Trends in Overseas Philanthropy by Chinese Foundations. Voluntas , 30 (4), 678–691. https://doi.org/10.1007/s11266-017-9868-7

Dinh, K., Hong, K. T., Haire, B., & Worth, H. (2021). Historic and Contemporary Influences on HIV Advocacy in Vietnam. Voluntas , 32 (3), 610–620. https://doi.org/10.1007/s11266-020-00220-x

Dong, Q., Guo, J., & Huang, C.-C. (2019). Nonprofit Alliance in China: Effects of Alliance Process on Goal Achievement. Voluntas , 30 (2), 300–311. https://doi.org/10.1007/s11266-018-9990-1

Edenfield, A. C., & Andersson, F. O. (2018). Growing Pains: The Transformative Journey from a Nascent to a Formal Not-For-Profit Venture. Voluntas , 29 (5), 1033–1043. https://doi.org/10.1007/s11266-017-9936-z

Elsayed, Y. (2018). At the Intersection of Social Entrepreneurship and Social Movements: The Case of Egypt and the Arab Spring. Voluntas , 29 (4), 819–831. https://doi.org/10.1007/s11266-017-9943-0

Eschweiler, J., Svensson, S., Mocca, E., Cartwright, A., & Villadsen Nielsen, L. (2019). The Reciprocity Dimension of Solidarity: Insights from Three European Countries. Voluntas , 30 (3), 549–561. https://doi.org/10.1007/s11266-018-0031-x

Eynaud, P., Juan, M., & Mourey, D. (2018). Participatory Art as a Social Practice of Commoning to Reinvent the Right to the City. Voluntas , 29 (4), 621–636. https://doi.org/10.1007/s11266-018-0006-y

Falkenhain, M. (2020). Dividing Lines: Understanding the Creation and Replication of Fragmentations Among NGOs in Hybrid Regimes. Voluntas , 31 (4), 663–673. https://doi.org/10.1007/s11266-019-00185-6

Fang, Q., Fisher, K. R., & Li, B. (2020). Follower or Challenger? How Chinese Non-governmental Organizations Manage Accountability Requirements from Funders. Voluntas , 31 (4), 722–735. https://doi.org/10.1007/s11266-019-00184-7

Farid, M., & Li, H. (2021). Reciprocal Engagement and NGO Policy Influence on the Local State in China. Voluntas , 32 (3), 597–609. https://doi.org/10.1007/s11266-020-00288-5

Fehsenfeld, M., & Levinsen, K. (2019). Taking Care of the Refugees: Exploring Advocacy and Cross-sector Collaboration in Service Provision for Refugees. Voluntas , 30 (2), 422–435. https://doi.org/10.1007/s11266-019-00097-5

Ferguson, G. (2018). The Social Economy in Bolivia: Indigeneity, Solidarity, and Alternatives to Capitalism. Voluntas , 29 (6), 1233–1243. https://doi.org/10.1007/s11266-018-0013-z

Fulton, B. R., & Wood, R. L. (2018). Civil Society Organizations and the Enduring Role of Religion in Promoting Democratic Engagement. Voluntas , 29 (5), 1068–1079. https://doi.org/10.1007/s11266-018-9965-2

Gaby, S. (2020). Reconfiguring Organizational Commitments: Boundary Crossing in Civic Groups. Voluntas , 31 (6), 1121–1133. https://doi.org/10.1007/s11266-020-00272-z

Gaeta, G. L., Ghinoi, S., Silvestri, F., & Trasciani, G. (2021). Exploring Networking of Third Sector Organizations: A Case Study Based on the Quartieri Spagnoli Neighborhood in Naples (Italy). Voluntas , 32 (4), 750–766. https://doi.org/10.1007/s11266-020-00241-6

García-Orosa, B., & Pérez-Seijo, S. (2020). The Use of 360° Video by International Humanitarian Aid Organizations to Spread Social Messages and Increase Engagement. Voluntas , 31 (6), 1311–1329. https://doi.org/10.1007/s11266-020-00280-z

Grazioli, M., & Caciagli, C. (2018). Resisting to the Neoliberal Urban Fabric: Housing Rights Movements and the Re-appropriation of the ‘Right to the City’ in Rome, Italy. Voluntas , 29 (4), 697–711. https://doi.org/10.1007/s11266-018-9977-y

Grubb, A., & Henriksen, L. S. (2019). On the Changing Civic Landscape in Denmark and its Consequences for Civic Action. Voluntas , 30 (1), 62–73. https://doi.org/10.1007/s11266-018-00054-8

Guha, P. (2019). Going to Scale: A Case Study of an Indian Educational NGO. Voluntas , 30 (6), 1365–1379. https://doi.org/10.1007/s11266-019-00099-3

Guo, C., & Lai, W. (2019). Community Foundations in China: In Search of Identity? Voluntas , 30 (4), 647–663. https://doi.org/10.1007/s11266-017-9932-3

Heckert, R., Boumans, J., & Vliegenthart, R. (2020). How to Nail the Multiple Identities of an Organization? A Content Analysis of Projected Identity. Voluntas , 31 (1), 129–141. https://doi.org/10.1007/s11266-019-00157-w

Heideman, L. J. (2018). Making Civil Society Sustainable: The Legacy of USAID in Croatia. Voluntas , 29 (2), 333–347. https://doi.org/10.1007/s11266-017-9896-3

Hjort, M., & Beswick, D. (2020). Volunteering and Policy Makers: The Political Uses of the UK Conservative Party’s International Development Volunteering Projects. Voluntas . https://doi.org/10.1007/s11266-020-00222-9

Holthaus, L. (2019). Furthering Pluralism? The German Foundations in Transitional Tunisia. Voluntas , 30 (6), 1284–1296. https://doi.org/10.1007/s11266-018-00074-4

Horvath, A. (2020). The Transformative Potential of Experience: Learning, Group Dynamics, and the Development of Civic Virtue in a Mobile Soup Kitchen. Voluntas , 31 (5), 981–994. https://doi.org/10.1007/s11266-018-00062-8

Horvath, A., Brandtner, C., & Powell, W. W. (2018). Serve or Conserve: Mission, Strategy, and Multi-Level Nonprofit Change During the Great Recession. Voluntas , 29 (5), 976–993. https://doi.org/10.1007/s11266-017-9948-8

Huang, Y. (2019). At the Mercy of the State: Health Philanthropy in China. Voluntas , 30 (4), 634–646. https://doi.org/10.1007/s11266-018-9960-7

Ismail, A., & Johnson, B. (2019). Managing Organizational Paradoxes in Social Enterprises: Case Studies from the MENA Region. Voluntas , 30 (3), 516–534. https://doi.org/10.1007/s11266-018-00083-3

Jezierska, K., & Polanska, D. V. (2018). Social Movements Seen as Radical Political Actors: The Case of the Polish Tenants’ Movement. Voluntas , 29 (4), 683–696. https://doi.org/10.1007/s11266-017-9917-2

Kampen, T., Veldboer, L., & Kleinhans, R. (2019). The Obligation to Volunteer as Fair Reciprocity? Welfare Recipients’ Perceptions of Giving Back to Society. Voluntas , 30 (5), 991–1005. https://doi.org/10.1007/s11266-018-00082-4

Kang, Y. (2019). What Does China’s Twin-Pillared NGO Funding Game Entail? Growing Diversity and Increasing Isomorphism. Voluntas , 30 (3), 499–515. https://doi.org/10.1007/s11266-018-00085-1

Kappelides, P., Cuskelly, G., & Hoye, R. (2019). The Influence of Volunteer Recruitment Practices and Expectations on the Development of Volunteers’ Psychological Contracts. Voluntas , 30 (1), 259–271. https://doi.org/10.1007/s11266-018-9986-x

Kennedy, D. (2019). The Inherently Contested Nature of Nongovernmental Accountability: The Case of HAP International. Voluntas , 30 (6), 1393–1405. https://doi.org/10.1007/s11266-019-00134-3

Kewes, A., & Munsch, C. (2019). Should I Stay or Should I Go? Engaging and Disengaging Experiences in Welfare-Sector Volunteering. Voluntas , 30 (5), 1090–1103. https://doi.org/10.1007/s11266-019-00122-7

Korstenbroek, T., & Smets, P. (2019). Developing the Potential for Change: Challenging Power Through Social Entrepreneurship in the Netherlands. Voluntas , 30 (3), 475–486. https://doi.org/10.1007/s11266-019-00107-6

Koubek, M. (2020). When Did They Protest? Beyond Co-optation or Channeling: Effects of EU Funding on Czech Romani NGOs and Civil Society. Voluntas , 31 (2), 404–421. https://doi.org/10.1007/s11266-018-0008-9

Kravchenko, Z., & Moskvina, A. (2018). Entrepreneurial NPOs in Russia: Rationalizing the Mission. Voluntas , 29 (5), 962–975. https://doi.org/10.1007/s11266-018-0016-9

Kumi, E. (2019). Aid Reduction and NGDOs’ Quest for Sustainability in Ghana: Can Philanthropic Institutions Serve as Alternative Resource Mobilisation Routes? Voluntas , 30 (6), 1332–1347. https://doi.org/10.1007/s11266-017-9931-4

Lall, S. A. (2019). From Legitimacy to Learning: How Impact Measurement Perceptions and Practices Evolve in Social Enterprise–Social Finance Organization Relationships. Voluntas , 30 (3), 562–577. https://doi.org/10.1007/s11266-018-00081-5

Lang, R., & Mullins, D. (2020). Field Emergence in Civil Society: A Theoretical Framework and Its Application to Community-Led Housing Organisations in England. Voluntas , 31 (1), 184–200. https://doi.org/10.1007/s11266-019-00138-z

Laurent, A., Garaudel, P., Schmidt, G., & Eynaud, P. (2020). Civil Society Meta-organizations and Legitimating Processes: The Case of the Addiction Field in France. Voluntas , 31 (1), 19–38. https://doi.org/10.1007/s11266-019-00094-8

Lee, E. K. M., & Chandra, Y. (2020). Dynamic and Marketing Capabilities as Predictors of Social Enterprises’ Performance. Voluntas , 31 (3), 587–600. https://doi.org/10.1007/s11266-019-00155-y

Li, S. (2020). Global Civil Society Under the New INGO Regulatory Law: A Comparative Case Study on Two INGOs in China. Voluntas , 31 (4), 751–761. https://doi.org/10.1007/s11266-019-00101-y

Li, S., & Wang, X. (2020). Seeking Credibility from Uncertainty: How Formal Cooptation Institution Unleashes Outspoken NGOs. Voluntas , 31 (4), 711–721. https://doi.org/10.1007/s11266-020-00204-x

Liu, F., & Zhang, X. (2020). Who are Volunteers in Urban China? Voluntas . https://doi.org/10.1007/s11266-020-00251-4

Lund, V., & Juujärvi, S. (2018). Residents’ Agency Makes a Difference in Volunteering in an Urban Neighbourhood. Voluntas , 29 (4), 756–769. https://doi.org/10.1007/s11266-018-9955-4

Marie, B., Isabelle, M., Jordan, B.-L., Hélène, C., Sophie, É., Julie, F., Christine, M., & Andrée, S. (2020). Promising Practices of Nonprofit Organizations to Respond to the Challenges Faced in Countering the Mistreatment of Older Adults. Voluntas , 31 (6), 1359–1370. https://doi.org/10.1007/s11266-020-00252-3

Martinez, D. E., & Cooper, D. J. (2020). Seeing Through the Logical Framework. Voluntas , 31 (6), 1239–1253. https://doi.org/10.1007/s11266-020-00223-8

Mathews, M. A. (2020). The Embeddedness of Nonprofit Leadership in Civic Governance. Voluntas , 31 (1), 201–212. https://doi.org/10.1007/s11266-019-00139-y

Mati, J. M. (2020). Civil Society in ‘Politics’ and ‘Development’ in African Hybrid Regimes: The Kenyan Case. Voluntas , 31 (4), 674–687. https://doi.org/10.1007/s11266-020-00211-y

Maya-Jariego, I., Holgado-Ramos, D., González-Tinoco, E., Muñoz-Alvis, A., & Ortega, M. (2020). More Money, More Problems? Resource Dependence and Professionalization of Non-governmental Social Services Organizations in Southern Spain. Voluntas , 31 (6), 1212–1225. https://doi.org/10.1007/s11266-020-00256-z

McLennan, B. J. (2020). Conditions for Effective Coproduction in Community-Led Disaster Risk Management. Voluntas , 31 (2), 316–332. https://doi.org/10.1007/s11266-018-9957-2

McMullin, C., & Skelcher, C. (2018). The Impact of Societal-Level Institutional Logics on Hybridity: Evidence from Nonprofit Organizations in England and France. Voluntas , 29 (5), 911–924. https://link.springer.com/article/10.1007/s11266-018-9996-8

Meijs, L., Handy, F., Simons, F.-J., & Roza, L. (2020). A Social Innovation: Addressing Relative Food Insecurity and Social Exclusion. Voluntas , 31 (5), 894–906. https://doi.org/10.1007/s11266-019-00105-8

Meyer, M., & Simsa, R. (2018). Organizing the Unexpected: How Civil Society Organizations Dealt with the Refugee Crisis. Voluntas , 29 (6), 1159–1175. https://doi.org/10.1007/s11266-018-00050-y

Mikołajczak, P. (2020). Social Enterprises’ Hybridity in the Concept of Institutional Logics: Evidence from Polish NGOs. Voluntas , 31 (3), 472–483. https://doi.org/10.1007/s11266-020-00195-9

Milbourn, B., Black, M. H., & Buchanan, A. (2019). Why People Leave Community Service Organizations: A Mixed Methods Study. Voluntas , 30 (1), 272–281. https://doi.org/10.1007/s11266-018-0005-z

Molina, J. L., Valenzuela-García, H., Lubbers, M. J., Escribano, P., & Lobato, M. M. (2018). “The Cowl Does Make The Monk”: Understanding the Emergence of Social Entrepreneurship in Times of Downturn. Voluntas , 29 (4), 725–739. https://doi.org/10.1007/s11266-017-9921-6

Moraes, R. L., & Andion, C. (2018). Civil Society and Social Innovation in Public Arenas in Brazil: Trajectory and Experience of the Movement Against Electoral Corruption (MCCE). Voluntas , 29 (4), 801–818. https://doi.org/10.1007/s11266-017-9867-8

Morais-da-Silva, R. L., Segatto, A. P., & Bezerra-de-Sousa, I. G. (2020). Connecting Two Sides: A Qualitative Study on Social Innovation Ventures and Poor Communities in an Emerging Economy. Voluntas , 31 (5), 966–980. https://doi.org/10.1007/s11266-019-00156-x

Musah-Surugu, I. J., Bawole, J. N., & Ahenkan, A. (2019). The “Third Sector” and Climate Change Adaptation Governance in Sub-Saharan Africa: Experience from Ghana. Voluntas , 30 (2), 312–326. https://doi.org/10.1007/s11266-018-9962-5

Mutongwizo, T. (2018). Comparing NGO Resilience and ‘Structures of Opportunity’ in South Africa and Zimbabwe (2010–2013). Voluntas , 29 (2), 373–387. https://doi.org/10.1007/s11266-017-9881-x

Näslund, H. (2020). Collective Deliberations and Hearts on Fire: Experiential Knowledge Among Entrepreneurs and Organisations in the Mental Health Service User Movement. Voluntas . https://doi.org/10.1007/s11266-020-00233-6

Nimu, A. (2018). Surviving Mechanisms and Strategies of Gender Equality NGOs in Romania and Poland. Voluntas , 29 (2), 310–332. https://doi.org/10.1007/s11266-018-9961-6

Noh, J.-E. (2019). Human Rights-Based Child Sponsorship: A Case Study of ActionAid. Voluntas , 30 (6), 1420–1432. https://doi.org/10.1007/s11266-018-0010-2

Obaze, Y. (2020). Supply Chain Challenges and Shared Value Destruction in the Community-Based Supply Chain. Voluntas , 31 (3), 550–562. https://doi.org/10.1007/s11266-020-00202-z

Onyx, J., Darcy, S., Grabowski, S., Green, J., & Maxwell, H. (2018). Researching the Social Impact of Arts and Disability: Applying a New Empirical Tool and Method. Voluntas , 29 (3), 574–589. https://doi.org/10.1007/s11266-018-9968-z

Pape, U., Brandsen, T., Pahl, J. B., Pieliński, B., Baturina, D., Brookes, N., Chaves-Ávila, R., Kendall, J., Matančević, J., Petrella, F., Rentzsch, C., Richez-Battesti, N., Savall-Morera, T., Simsa, R., & Zimmer, A. (2020). Changing Policy Environments in Europe and the Resilience of the Third Sector. Voluntas , 31 (1), 238–249. https://doi.org/10.1007/s11266-018-00087-z

Parker, M. A., Mook, L., Kao, C.-Y., & Murdock, A. (2020). Accountability and Relationship-Definition Among Food Banks Partnerships. Voluntas , 31 (5), 923–937. https://doi.org/10.1007/s11266-019-00150-3

Peci, A., Oquendo, M. I., & Mendonça, P. (2020). Collaboration, (Dis)trust and Control in Brazilian Manufactured Public/Non-profit Partnerships. Voluntas , 31 (2), 375–389. https://doi.org/10.1007/s11266-018-0027-6

Peralta, K. J., & Vaitkus, E. (2019). Constructing Action: An Analysis of the Roles of Third Sector Actors During the Implementation of the Dominican Republic’s Regularization Plan. Voluntas , 30 (6), 1319–1331. https://doi.org/10.1007/s11266-018-0003-1

Pettigrew, S., Jongenelis, M., Jackson, B., & Newton, R. U. (2019). “Charity Begins at Home”: Informal Caring Barriers to Formal Volunteering Among Older People. Voluntas , 30 (5), 921–931. https://doi.org/10.1007/s11266-018-0017-8

Pfeilstetter, R. (2020). Doing Good and Selling Goods. Voluntas , 31 (3), 511–520. https://doi.org/10.1007/s11266-020-00194-w

Pixová, M. (2018). The Empowering Potential of Reformist Urban Activism in Czech Cities. Voluntas , 29 (4), 670–682. https://doi.org/10.1007/s11266-018-0011-1

Pluciński, P. (2018). Forces of Altermodernization: Urban Social Movements and the New Urban Question in Contemporary Poland. Voluntas , 29 (4), 653–669. https://doi.org/10.1007/s11266-018-0007-x

Popplewell, R. (2018). Civil Society, Legitimacy and Political Space: Why Some Organisations are More Vulnerable to Restrictions than Others in Violent and Divided Contexts. Voluntas , 29 (2), 388–403. https://doi.org/10.1007/s11266-018-9949-2

Puljek-Shank, R. (2018). Civic Agency in Governance: The Role of Legitimacy with Citizens vs. Donors. Voluntas , 29 (4), 870–883. https://doi.org/10.1007/s11266-018-0020-0

Reijnders, M., Schalk, J., & Steen, T. (2018). Services Wanted? Understanding the Non-take-up of Social Support at the Local Level. Voluntas , 29 (6), 1360–1374. https://doi.org/10.1007/s11266-018-00060-w

Ruiz Sportmann, A. S., & Greenspan, I. (2019). Relational Interactions Between Immigrant and Native-Born Volunteers: Trust-Building and Integration or Suspicion and Conflict? Voluntas , 30 (5), 932–946. https://doi.org/10.1007/s11266-019-00108-5

Sandberg, B., Elliott, E., & Petchel, S. (2020). Investigating the Marketization of the Nonprofit Sector: A Comparative Case Study of Two Nonprofit Organizations. Voluntas , 31 (3), 494–510. https://doi.org/10.1007/s11266-019-00159-8

Schmid, H., & Almog-Bar, M. (2020). The Critical Role of the Initial Stages of Cross-Sector Partnerships and Their Implications for Partnerships’ Outcomes. Voluntas , 31 (2), 286–300. https://doi.org/10.1007/s11266-019-00137-0

Sepulveda, L., Lyon, F., & Vickers, I. (2020). Implementing Democratic Governance and Ownership: The Interplay of Structure and Culture in Public Service Social Enterprises. Voluntas , 31 (3), 627–641. https://doi.org/10.1007/s11266-020-00201-0

Shier, M. L., & Handy, F. (2020). Leadership in Nonprofits: Social Innovations and Blurring Boundaries. Voluntas , 31 (2), 333–344. https://doi.org/10.1007/s11266-018-00078-0

Spencer, S. B., & Skalaban, I. A. (2018). Organizational Culture in Civic Associations in Russia. Voluntas , 29 (5), 1080–1097. https://doi.org/10.1007/s11266-017-9925-2

Stewart, A. J., & Twumasi, A. (2020). Minding the Gap: An Exploratory Study Applying Theory to Nonprofit Board Management of Executive Transitions. Voluntas , 31 (6), 1268–1281. https://doi.org/10.1007/s11266-020-00244-3

Stougaard, M. S. (2020). Co-producing Public Welfare Services with Vulnerable Citizens: A Case Study of a Danish-Somali Women’s Association Co-producing Crime Prevention with the Local Authorities. Voluntas . https://doi.org/10.1007/s11266-020-00235-4

Strichman, N., Marshood, F., & Eytan, D. (2018). Exploring the Adaptive Capacities of Shared Jewish–Arab Organizations in Israel. Voluntas , 29 (5), 1055–1067. https://doi.org/10.1007/s11266-017-9904-7

Suter, P., & Gmür, M. (2018). Volunteer Engagement in Housing Co-operatives: Civil Society “en miniature”. Voluntas , 29 (4), 770–789. https://doi.org/10.1007/s11266-018-9959-0

Svensson, K., & Gallo, C. (2018). The Creation of an Unsolicited Organization: Victim Support Sweden. Voluntas , 29 (5), 1008–1018. https://doi.org/10.1007/s11266-018-9978-x

Tadesse, H. A., & Steen, T. (2019). Exploring the Impact of Political Context on State–Civil Society Relations: Actors’ Strategies in a Developmental State. Voluntas , 30 (6), 1256–1269. https://doi.org/10.1007/s11266-018-00077-1

Taylor, R., Torugsa, N. (Ann), & Arundel, A. (2020). Organizational Pathways for Social Innovation and Societal Impacts in Disability Nonprofits. Voluntas , 31 (5), 995–1012. https://doi.org/10.1007/s11266-019-00113-8

Uzcanga, C., & Oiarzabal, P. J. (2019). Associations of Migrants in Spain: An Enquiry into Their Digital Inclusion in the “Network Society” in the 2010s. Voluntas , 30 (5), 947–961. https://doi.org/10.1007/s11266-019-00162-z

van der Veer, L. (2020). Treacherous Elasticity, Callous Boundaries: Aspiring Volunteer Initiatives in the Field of Refugee Support in Rotterdam. Voluntas . https://doi.org/10.1007/s11266-020-00260-3

van Wessel, M., Naz, F., & Sahoo, S. (2021). Complementarities in CSO Collaborations: How Working with Diversity Produces Advantages. Voluntas , 32 (4), 717–730. https://doi.org/10.1007/s11266-020-00227-4

Vandepitte, E., Vandermoere, F., & Hustinx, L. (2019). Civil Anarchizing for the Common Good: Culturally Patterned Politics of Legitimacy in the Climate Justice Movement. Voluntas , 30 (2), 327–341. https://doi.org/10.1007/s11266-018-00073-5

Vuković, D. (2018). The Quest for Government Accountability and Rule of Law: Conflicting Strategies of State and Civil Society in Cambodia and Serbia. Voluntas , 29 (3), 590–602. https://doi.org/10.1007/s11266-018-9984-z

Waardenburg, M. (2021). Understanding the Microfoundations of Government–Civil Society Relations. Voluntas , 32 (3), 548–560. https://doi.org/10.1007/s11266-020-00221-w

Wells, R., & Anasti, T. (2020). Hybrid Models for Social Change: Legitimacy Among Community-Based Nonprofit Organizations. Voluntas , 31 (6), 1134–1147. https://doi.org/10.1007/s11266-019-00126-3

Williamson, A. K., Luke, B., & Furneaux, C. (2021). Ties That Bind: Public Foundations in Dyadic Partnerships. Voluntas , 32 (2), 234–246. https://doi.org/10.1007/s11266-020-00269-8

Yanay-Ventura, G. (2019). “Nothing About Us Without Us” in Volunteerism Too: Volunteering Among People with Disabilities. Voluntas , 30 (1), 147–163. https://doi.org/10.1007/s11266-018-0026-7

Yanay-Ventura, G., Issaq, L., & Sharabi, M. (2020). Civic Service and Social Class: The Case of Young Arab Women in Israel. Voluntas . https://doi.org/10.1007/s11266-020-00210-z

Zhang, Y., Bradtke, M., & Halvey, M. (2020). Anxiety and Ambivalence: NGO–Activist Partnership in China’s Environmental Protests, 2007–2016. Voluntas , 31 (4), 779–792. https://doi.org/10.1007/s11266-020-00237-2

Zihnioğlu, Ö. (2019). The Prospects of Civic Alliance: New Civic Activists Acting Together with Civil Society Organizations. Voluntas , 30 (2), 289–299. https://doi.org/10.1007/s11266-018-0032-9

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McMullin, C. Transcription and Qualitative Methods: Implications for Third Sector Research. Voluntas 34 , 140–153 (2023). https://doi.org/10.1007/s11266-021-00400-3

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• Transcribing an Interview | 5 Steps & Transcription Software

Transcribing an Interview | 5 Steps & Transcription Software

Published on 6 May 2022 by Raimo Streefkerk .

Transcribing is converting speech to text word for word. Transcribing is a common practice when conducting interviews because it enables you to perform analysis.

How to transcribe an interview in five steps:

• Choose your preferred transcription method.
• Transcribe the audio (using transcription software ).
• Add speaker designation and time stamps.
• Clarify the transcript where needed.
• Proofread the transcript.

Transcription software comparison

Table of contents

Transcription methods, altering the transcript, example transcript, analysing interview transcripts, transcription software.

Before you start transcribing, you first need to determine what transcription method you want to use. The best method depends on the goal of your transcription.

Verbatim transcription

Write down every single word, including pauses, the expression of emotions such as laughter, stuttering, and hesitations such as ‘uh’.

This type of transcription is mostly used in the legal profession or in research where you’re not only interested in what is said but also how it is said.

Intelligent verbatim transcription (most common)

Write down every word, but without irrelevant fillers like ‘um’, ‘yeah’, and ‘you know’. To improve readability, you can also fix grammar mistakes, broken sentences, and long paragraphs.

This method is more readable than verbatim transcription, but some data – such as emotions, pauses and hesitation – is lost in the process.

Edited transcription

A summarised and edited version of an intelligent verbatim transcript. In addition to omitting fillers like ‘you know’, irrelevant sentences can be omitted if it doesn’t change the meaning of the story.

Prevent plagiarism, run a free check.

If the audio quality is bad or the conversation itself needs clarification, you are allowed to make changes in the transcript. For instance:

• Adding a clarifying comment: ‘I showed him that this option [raising prices] would be beneficial for profitability.’
• Marking unclear or missing audio with ellipses: ‘I showed him … would be beneficial for profitability.’
• Emphasising words: ‘ Increasing prices is needed for profitability.’

There are no rules for formatting and structuring a transcript. However, most transcripts contain the following information:

• Names of the interviewer and interviewee (can be anonymised)
• Date and time when the interview took place
• Location of the interview
• Speaker designation (who says what?)
• Line numbers and time stamps (optional)

After transcribing the interview(s) it is time to start analysing. There are several techniques for doing this – coding and categorising is one of them.

This means that you link keywords (e.g., ‘understanding customer’) to the answers you’ve received to your questions. Based on these keywords you are able to find connections between the answers of different respondents.

You can also use methods such as content analysis , thematic analysis , or discourse analysis .

If you quote from an interview in your paper, make sure you correctly cite the source. Learn how to cite an interview in MLA and APA .

Transcribing interviews takes a lot of time, but luckily transcription software is developing quickly. Using transcription software can help you speed up the process.

Most software is able to accurately convert English speech to text. However, the audio quality must be good in order for the software to work. That means a noise-free background, no over-talk, clear accents and good microphones.

If the audio quality is too poor for automatic transcription, you unfortunately have to dictate it or transcribe it manually.

We tested and reviewed the transcription software below using the audio of a YouTube video in which Bill Gates is interviewed . The audio meets all the criteria listed above.

Happy Scribe

• Speaker recognition
• Clean and intuitive editor
• Omits ‘uhs’ and stuttering
• Correct capitalisation and use of full stops
• 25% student discount
• Doesn’t insert punctuation (except for full stops)

• Good speaker recognition
• Simple but powerful interface
• Comment and highlight feature
• Ignores intro music from video
• Easy to keep track of reviewing progress
• Some missing spaces

• Solid speaker recognition
• Very good capitalisation and punctuation (including commas)
• Much cheaper than other transcription software
• Just a 1-minute trial
• Dated editor with limited functionality
• Doesn’t connect audio and transcript
• $20 (approx. £16.25) annual licence fee

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How to Conduct a Research Interview

How to conduct a research interview

Whether you're a business owner or an academic, qualitative research interviews are a great way of collecting detailed information, data, and feedback on a specific topic.

If you're a business owner, that topic might be a product you're developing or have recently launched. If you're an academic, that topic is likely to be the research question you're working on for your next paper.

In this guide you'll learn how to prepare for and structure a research interview, and how to transcribe the interview once it has ended.

Qualitative vs quantitative research

How to prepare for a research interview

How to structure your research interview, what to do after the interview, how to transcribe a qualitative research interview, what's the difference between qualitative and quantitative research.

In a nutshell, quantitative research focuses on numbers and statistics, whereas qualitative research focuses on words, meanings, and behavior.

The aim of qualitative research is to describe a topic, rather than measure it. And while quantitative research is typically carried out via surveys with close-ended questions (like yes or no questions), qualitative research usually involves interviews.

Preparation is key when it comes to ensuring you get the most possible out of your research interview. Here are five ways to prepare ahead of the interview:

1. Prepare a list of questions

Start by thinking about what you want to get from the interview, and plan your questions based on this. The beauty of qualitative interviews is that you can't predict how the respondent will answer, but have a think about the follow-up questions you could ask depending on the direction the interview takes.

The number of questions you should ask in a research interview will vary depending on the project and the type of interview you want to conduct. For an academic research interview you might have just one or two broad questions in addition to the main research question, while for business research, you might have a longer list of questions. It's a good idea to have a couple of priority questions, and then plenty of additional follow-up questions that you can ask when appropriate.

Check out our guide on how to interview someone for an article for tips on the type of questions you should ask.

2. Practice your interview techniques

Enlist a colleague or peer to help you rehearse the interview and practice your interview techniques so that you feel confident for the real thing. Focus on actively listening, and ask your peer to comment on your body language and demeanor. While running through the questions, notice if anything could be phrased in a better way, and make a note of any additional questions that crop up.

3. Choose a safe and comfortable meeting place

For face-to-face interviews, arrange a meeting place where you and the interviewee will both feel safe and comfortable. An office space feels professional, while a public place like a cafe might feel more relaxed and informal.

4. Be clear about the when, where, why and how

Make sure the person you're interviewing knows well in advance when and where the interview will take place, and how long it will last for. Research interviews commonly last between 20 minutes and half an hour, depending on the context.

Be clear about the purpose of the interview, and give your interviewee a good idea of what to expect from the session.

5. Have your recording equipment ready

Recording an interview is good practice, as it means you can focus on actively listening and asking relevant follow-up questions to ensure you get the most out of the interview - rather than taking notes. Make sure you have a laptop and a microphone at the ready, and check it's all working before you start the interview.

Once you've greeted the participant and made them feel at ease with some friendly chit chat, start by introducing yourself and explaining the purpose of the interview. If you're recording the interview, make sure you ask their permission before you hit record.

Then you can start working through your questions. How you structure the interview from this point onwards depends on exactly what you want from the interview:

You might want to work through a structured list of questions, keeping things on track throughout

You might want to work through your questions but allow for plenty of tangents to see what interesting information you can gather

Or you might want an entirely unstructured interview where you let the interviewee speak freely

However you choose to structure the interview, make sure you actively listen and allow the interviewee plenty of time to respond to each question. Don't be afraid of silence, as this can allow the respondent time to reflect, which might provide you with additional insight.

Show respect for interviewee's time by keeping to the agreed timeframe, and end by asking them if they have any questions or anything they'd like to add. Thank them for their time and explain how and when you will follow up, if necessary.

Soon after the interview is finished, it's a good idea to transcribe the recording and start your analysis while the information is fresh in your mind. Make a note of key points, quotes, findings and recommendations, and use insights from each interview you conduct to improve the efficiency and effectiveness of future interviews. You might want to adjust your questions slightly or allow a little more time for the next interview.

How does transcribing an interview help with your research?

Save yourself from having to take notes while the interview is in progress

Easily search for key sections and themes, and jump to important parts of the interview

Copy and paste verbatim quotes from your academic transcriptions directly into your reports

Refer back to your written notes as and when you need to

Transcribing an interview by hand is a long and time-consuming process, so consider saving yourself precious time by using an automated transcription tool like Transcribe.

Simply upload the audio file to the Transcribe app or online editor , and you'll get an automated transcription in a matter of minutes.

Take a look at our guide on how to transcribe an interview for extra guidance.

Written By Katie Garrett

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How to Transcribe an Interview for Qualitative Research?

Transkriptor 2024-04-23

Transcribing interviews is a fundamental aspect of qualitative research, providing a textual representation of audio or video recordings that captures the depth and nuance of participants' responses. The transcription process is not merely about converting spoken words into written text for users; it's a critical step in data analysis and interpretation that ensures the accuracy and reliability of research findings.

Navigating the transcription process involves understanding the best practices for recording interviews , mastering the art of accurate transcription, and utilizing tools like Transkriptor to streamline the process.

The 5 steps of transcribing an interview for qualitative research are listed below.

• Upload to Transkriptor or Record Directly: Choose between uploading pre-recorded files or using Transkriptor's direct recording feature. Use the Meeting Assistant such as Meetingor for multi-speaker sessions.
• Transcribe with AI Precision: Use Transkriptor’s AI-driven services for efficient and accurate transcription, supporting over 100 languages for diverse research needs.
• Review and Edit Transcripts: Systematically check the transcribed text against the audio, and refine readability while maintaining the original speech’s integrity.
• Translate (Optional): Use Transkriptor’s integrated translation services for multilingual research, ensuring accurate representation of the original text.
• Export and Analyze: Download transcripts in various formats suitable for qualitative data analysis, engage deeply with the text to identify themes and insights.

Step 1: Upload to Transkriptor or Record Directly

Users have several options for capturing and submitting their audio files to Transkriptor while transcribing an interview for qualitative research, each tailored to different research needs and contexts.

Upload Files

The Upload feature of Transkriptor allows users to submit pre-recorded audio or video files directly to the platform for transcription. This method is particularly useful for researchers who have conducted interviews outside of Transkriptor and have their recordings ready in digital format.

Transkriptor supports a wide range of file formats, including MP3, MP4, WAV, AAC, M4A , and WEBM, among others, offering flexibility in terms of the type of interview recordings users are able to transcribe.

Users should simply navigate to the Transkriptor dashboard to upload files, select the "Upload" option, and choose the files from their device.

Transkriptor's advanced transcription engine processes the audio once uploaded, converting speech into text with high accuracy.

Direct Recording

The Direct recording feature provides an integrated solution to capture audio directly within Transkriptor for users conducting live interviews or meetings. This option eliminates the need for external recording devices or software, streamlining the transcription process. Transkriptor's mobile apps and web app, providing flexibility for researchers to record using their phones or laptops.

Transcribers initiate a new interview recording session within Transkriptor to use direct recording, ensuring their microphone is configured correctly and tested for optimal audio capture.

The audio is recorded in real-time as the interview proceeds, and upon completion, the recording is immediately available for transcription within the platform.

Meeting Assistant

As a meeting assistant, Meetingtor is a digital platform designed to integrate with your calendar and manage your online meetings. It expands Transkriptor's capabilities to accommodate multi-speaker meetings and interviews, commonly used in platforms like Zoom, Microsoft Teams, and Google Meet . This tool is specifically designed to handle the dynamics of group discussions, capturing each participant's contributions clearly.

Step 2: Transcribe with AI Precision

Transcribing interviews is critical in data analysis, demanding precision and efficiency. Users should harness the power of Transkriptor's AI-driven transcription services to convert their interviews into accurately transcribed text.

This sophisticated AI technology is designed to recognize and accurately transcribe speech from audio recordings, making it an invaluable tool for researchers.

Transkriptor's AI capabilities support over 100 languages, accommodating a wide range of qualitative research projects that span diverse linguistic groups. This feature mainly benefits users conducting international research or working with multilingual participants.

The advantage of using AI for transcription lies in its ability to process large volumes of audio data quickly and with high precision. This efficiency enables researchers to move faster from data collection to analysis, accelerating the overall research timeline.

Users should ensure that their qualitative data is transcribed accurately and ready for analysis, supporting a thorough and insightful research process.

Step 3: Review and Edit Transcripts

The next critical phase in qualitative research involves reviewing and editing transcripts. Users should systematically review the transcribed text , comparing it against the original audio to verify its correctness. This process allows identifying and correcting discrepancies or errors during the AI transcription phase.

The review process also involves refining the readability of the transcripts. While AI transcription provides a high level of accuracy, it doesn’t always capture the nuances of human speech, such as pauses, intonations, and non-verbal cues, which are significant in qualitative research. Users are encouraged to add annotations or notes to the transcripts to capture these subtleties, providing context crucial for data analysis.

Editing transcripts further ensures the text is coherent and follows a logical structure. This includes removing irrelevant sections and repetitive phrases or correcting grammatical errors that do not affect the participant's speech's meaning but improve the transcript's clarity.

Users need to maintain the integrity of the original speech while making these edits, preserving the participant's voice and intent.

Step 4: Translate (Optional)

Translating transcribed interviews enables users to bridge language barriers, making the insights gathered from interviews accessible to a broader audience or research team members who do not speak the original language of the interview.

Transkriptor facilitates this step by offering integrated translation services that support multiple languages, ensuring that translations maintain the nuance and meaning of the original text.

Users must ensure their transcripts are accurately reviewed and edited for clarity and correctness to initiate translation. Transcripts should be submitted for translation through Transkriptor's platform once finalized.

The translation process leverages advanced AI algorithms designed to understand and convert text accurately between languages, preserving the essence of the participants' responses and the researchers' questions.

Step 5: Export and Analyze

The final step in the qualitative research process involves exporting and analyzing the data. Users should utilize Transkriptor's export function to download their transcripts in various formats, such as TXT, Clipboard, Word , or SRT, depending on their analysis needs.

This flexibility ensures that the data is easily imported into qualitative data analysis software (QDAS) or reviewed manually.

Exporting the transcripts marks the transition from data preparation to deep analysis. Users engage with the text to identify themes, patterns, and insights relevant to their research questions.

The analysis process involves reading through the transcripts multiple times, coding the data, and categorizing codes into themes that emerge from the data itself. This in-depth engagement with the text allows researchers to construct a nuanced understanding of the interviewees' perspectives, experiences, and the study context.

The QDAS software facilitates the organization, coding, and thematic analysis of the transcripts. These tools offer features such as text search, coding, and the ability to visualize data connections, enhancing the efficiency and depth of analysis.

The goal remains to distill meaningful insights from the interviews that contribute to the broader research objectives, whether analyzing data manually or using software.

What is Qualitative Research?

Qualitative research is a methodological approach that seeks to understand how people interpret their experiences, the meanings they attribute to those experiences, and the world in which they live. Users of qualitative research methods focus on collecting non-numerical data, typically through interviews, observations, and textual analysis, to gain insights into people's attitudes, behaviors, and interactions.

Users prioritize depth over breadth in qualitative research. They aim to gather detailed information from a smaller sample size rather than seeking to generalize findings across a larger population. This approach allows researchers to explore complex phenomena within specific contexts, offering rich, nuanced understandings that quantitative methods are able to overlook.

Transcribers engage in qualitative research when they wish to explore questions of 'how' and 'why' rather than 'how many' or 'how much.' This method is particularly suited to studies that explore new or poorly understood areas, where the goal is to generate hypotheses and theories

Why is Accurate Transcription Crucial for Qualitative Research?

Accurate transcription is fundamental to the success of qualitative research, serving as the bridge between raw data and insightful analysis. The fidelity of transcribed interviews to the original audio is paramount for users engaged in qualitative studies, as it ensures that the nuances of participants' speech are faithfully represented in text form. This accuracy is critical for several reasons.

First, accurate transcription provides a reliable foundation for analysis. Users depend on the precision of transcripts to perform detailed examinations of the data, identifying themes, patterns, and insights that emerge from participants' responses.

Second, the integrity of the research process hinges on the faithful representation of participants' words. Qualitative research often dives into sensitive topics or personal experiences. Accurate transcription respects the participants' voices, ensuring their perspectives are accurately documented and analyzed.

Moreover, accurate transcription enables users to conduct a thorough and robust analysis. It allows researchers to return to the data multiple times, uncovering deeper meanings and connections that aren’t apparent on initial examination. This iterative analysis process is crucial for comprehensively understanding the research topic.

What Challenges Do Researchers Face in Transcribing Interviews?

Researchers engaged in transcribing interviews for qualitative research encounter several challenges that impact the accuracy and efficiency of the transcription process. These challenges arise from both spoken language's nature and transcription's practical aspects.

Firstly, dealing with poor audio quality is a significant challenge. Background noise, low speaking volumes, and unclear articulation make it difficult for researchers to discern words accurately, leading to potential errors or omissions in the transcript. Users must often replay sections multiple times, which is time-consuming and frustrating.

Secondly, the presence of multiple speakers adds complexity to the transcription process. Distinguishing between speakers, especially when interrupting or speaking simultaneously, requires careful attention and slowing down the transcription process. Researchers must develop strategies to attribute speech to the correct individual accurately.

Moreover, researchers face the challenge of dialects and accents, which vary significantly among participants. Transcribing speech from speakers with unfamiliar accents or regional dialects accurately demands a high level of linguistic competence and requires additional research or consultation.

Finally, the time-consuming nature of transcription is a challenge in itself. Transcription takes several hours for just one hour of audio, placing significant demands on researchers' time and resources. This is particularly challenging for large-scale studies or when researchers work under tight deadlines.

Streamlining Qualitative Research with Transkriptor

In qualitative research, transcribing interviews is essential, offering a textual version of audio or video recordings. This detailed process, crucial for data analysis, involves converting spoken words into written text to capture the depth of participants' responses. Using advanced tools like Transkriptor can significantly ease this task.

Transkriptor stands out by offering fast, accurate transcription in over 100 languages, making it an invaluable tool for researchers dealing with diverse linguistic data. It not only generates subtitles by converting audio to text but also provides options for exporting in various formats, complete with timestamps and speaker names. This functionality is crucial for researchers aiming to overcome common transcription challenges such as poor audio quality, multiple speakers, varied dialects, and the extensive time commitment typically required. Try now!

Frequently Asked Questions

You should use a high-quality digital recorder in a quiet, controlled environment. or Transkriptor as a transcription app in a quiet, controlled environment. They ensure all participants are audible and conduct a test recording to check sound levels and clarity before starting the interview.

Users must listen to the recorded interview, typing the spoken words verbatim. They should use transcription software like Transkriptor. Also, users are able to compare the text with the audio to ensure accuracy regularly and include non-verbal cues in annotations.

Users are able to transcribe an interview for qualitative research for free by utilizing Transkriptor's free trial. This trial offers access to advanced AI transcription features, enabling accurate and efficient transcription of interviews without initial cost.

The fastest way to transcribe an interview is to use automated transcription services with AI technology by Transkriptor. Although it requires post-transcription review for accuracy, it significantly reduces the initial transcription time.

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EDITORIAL article

This article is part of the research topic.

Capturing Talk: The Institutional Practices Surrounding the Transcription of Spoken Language

Capturing Talk: The Institutional Practices Surrounding the Transcription of Spoken Language Editorial Transcripts are a ubiquitous feature of virtually all modern institutions, many of which would be unable to function without them. Nevertheless, transcription remains an under-researched subject -a situation that Capturing Talk: The Institutional Practices Surrounding the Transcription of Spoken Language seeks to remedy Provisionally Accepted

• 1 The University of Melbourne, Australia
• 2 Aston University, United Kingdom
• 3 Loughborough University, United Kingdom
• 4 Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), Netherlands

The final, formatted version of the article will be published soon.

The initial aim of this Research Topic was to expose and examine under-appreciated features of 'entextualisation' (the process of representing spoken language as written text). One of these features is the fact that a transcript can only ever be a representation of speech, not a copy -and thus can never represent speech exactly. Another feature, well articulated by Sarangi (1998), is the unequal power over the process of transcription exercised by, on the one hand, the speakers whose voices are represented, and, on the other, by those controlling the transcription process. Where Sarangi's interest was mainly in health and social services institutions, the present Topic has a leaning towards legal institutions, where, arguably, these power inequalities are even more starkly contrasted -as demonstrated by the territorydefining volume Heffer et al (2013).Four of the papers in this Topic deal with police interviews, providing insight into differing practices across jurisdictions and type of interview (e.g. whether with witnesses or suspects). Several papers examine the practice of converting an interview into a 'statement', written up by the officers who conduct the interviews. Beginning with interviews with witnesses in England and Wales (E&W), Milne et al analyse a sample of such statements against transcripts produced by the researchers from an audio recording. The omissions, additions, distortions, and other errors in the police versions give cause for deep concern.An extended study analysing the creation of records of interviews with suspects in the Netherlands is recounted by Komter, which, again, contrasts transcripts prepared by police interviewers, with the author's transcripts prepared from audio recordings. Again, many concerning limitations on the police transcripts are observed and analysed. However, while her own transcripts are far more detailed, Komter acknowledges that she too is necessarily selective in what she chooses to represent, guided by the evolving research questions she seeks to investigate.One practice Komter discusses is that of police records presenting an interview as a monologue, in the voice of the interviewee, rather than as the question-and-answer dialogue it actually was. This practice is also investigated by Eerland and van Charldorp, again focusing on the Dutch context. These authors study how readers of the statements were influenced by three different styles of reporting (monologue, dialogue and narrative), with the troubling finding that the style of reporting affected perceptions of the statements' accuracy and comprehensibility.In many jurisdictions, police interviews with suspects are routinely audio-or videorecorded. However, this does not signal the end of problems with the representation of these high-stakes interactions. The last of our interview papers is Haworth et al, which summarises the key findings to date of an ongoing study of the transcription of electronic records of interviews with suspects in E&W. It demonstrates a range of problems with official police transcripts even when these ostensibly capture the dialogue "verbatim", and proposes that consistency, accuracy, and neutrality are the foundational features that should underpin any police interview transcript.A second group of papers studies transcription in non-legal institutional settings. Holder et al delves into two very large and highly structured organisations with serious security needs: NASA and the US Military. Both make extensive use of audio and video recordings capturing employees as they work -with transcripts produced either routinely, or on demand. The authors look into the two organisations' use of these transcripts, again comparing the official transcripts with their own transcripts of selected sections, using conversation analysis (CA) conventions.Park and Hepburn also examine CA-style transcripts. Taking as an example Rachel Mitchell's interview of US Supreme Court nominee Brett Kavanaugh about his alleged historical sexual misconduct, these authors compare the information retrievable from a richly detailed Jeffersonian transcript with an orthographic transcript that 'wipes out' or 'skates over' crucial aspects of speech used by speakers and listeners in constructing the message expressed by the speech.Another institutional use of transcripts covered in Capturing Talk concerns workers on the assembly line of a small factory in Sweden. Carlsson and Harari report an observation-and-interview study of the instruction manuals created by the workers. While they find much to commend in the retention of power by the creators and users of the manuals, the authors observe room for improvement in the 'information design' of the texts, recommending that consultation of linguistics experts could offer benefits.Voutilainen showcases the high quality of transcripts produced as an official record of the complex and challenging multicultural discussions of wide-ranging topics covered by the parliament in Finland. His account demonstrates how much thought, research and work goes into managing all the factors that need to be considered to create transcripts of this standard.In a return to the legal setting, a further group of papers examines transcripts of forensic audio, i.e. recordings of speech used as evidence in criminal trials. These are often of very poor quality, meaning that the transcript is intended not as a record of what was said, but as assistance to the court in determining what was said. Internationally, it is common for such transcripts to be provided by police investigating the case. While the courts recognise that police transcripts might contain errors, they rely on judges and/or juries being able to check the transcript against the audio. This ignores well-established research findings that the very act of checking a transcript can cause the listener to hear in line with the transcript, even if it is demonstrably false. For this reason, linguists sometimes recommend that, to ensure accuracy, transcripts should be produced by independent experts in transcription.However, mere independence may not be enough, and Love and Wright point out some important caveats around this recommendation. They had eight trained transcribers produce transcripts of poor-quality forensic-like audio -finding huge divergences in the content of the transcripts (less than 3% of conversational turns were transcribed consistently by all eight participants). This demonstrates that transcribing poor-quality forensic audio needs not just expertise in linguistics, but a managed, evidence-based method.Recently, a common response to any discussion of the difficulty of transcribing poorquality audio has been: 'Why not let AI do it?'. Loakes investigates this suggestion (with her study updated here to reflect very recent developments). She found that, while modern automatic speech recognition (ASR) systems are extremely efficient at transcribing good-quality audio, their performance on poor-quality forensic-like audio is low. Even the best-performing system, Whisper, scored only around 50% accuracy, with others far lower.Harrington also observed low scores for ASR transcripts of poor-quality forensic-like audio. Bridging two of the main areas considered in this Topic, she also trialled ASR on recordings of police interviews. The resulting transcripts, though not problem-free, score far higher than those of covert recordings, with errors easier to identify. Harrington makes innovative recommendations for how ASR could be used as a 'first draft' interview transcript, to be refined via human transcribers.Two papers consider the transcription and translation of forensic audio featuring languages other than English. Gilbert and Heydon look at translated transcripts of Vietnamese recordings used as evidence in a drug-related trial. They point out significant errors in the translations, but note that, unless the defence goes to the expense of hiring their own translator/interpreter, such errors are unlikely to be detected -and suggest that audio in languages other than English is often admitted with inadequately tested translations.Lai presents results of a large national survey of the practices and concerns of translators and interpreters who undertake forensic casework across a wide range of languages. Here, too, results indicate a number of important deficiencies in current practice for translating forensic audio featuring languages other than English -and Lai makes valuable recommendations for improvement.Finally, taking an authoritative overview of the key issues relevant to this Topic, Fraser provides a systematic review of interdisciplinary research on transcripts and transcription, and sets out a series of interacting factors that are known to affect a transcript's reliability. Using examples from a range of legal and academic situations, Fraser argues that, to ensure a transcript is suitable for its intended purpose, it is essential that all the factors be appropriately managed.Taken as a whole, Capturing Talk amplifies two observations made in both Sarangi (1998) andHeffer et al (2013), which, though not the exclusive focus of any individual paper, are highlighted throughout the Topic. First, the strong role that context inevitably plays in the interpretation of a transcript implies that 'recontextualisation' (using a transcript in a context other than the one it was created in) is likely to change its interpretation. Second, even the most expert linguistic analysis of transcripts produced by others is not itself a neutral or 'objective' activity. However, this does not mean that such analysis must be 'subjective' in any limiting sense. Rather it indicates a need for transcripts to be produced and analysed by independent, context-aware experts able to devote appropriate attention to all relevant factors.Most importantly, all contributions to Capturing Talk emphasise that transcription is far from the simple transduction of 'sounds' into letters that it is often assumed to be by those who have not studied its intricacies. It is a highly complex and fascinating topic worthy of taking its place as a dedicated field of research in its own right, particularly in view of the widespread misconceptions and unhelpful language ideologies that still beset the institutional practices surrounding the transcription of spoken language.

Keywords: transcription, misconceptions about language and linguistics, language ideologies, Forensic linguistics, Forensic transcription, Police interviews and interrogations

Received: 15 Apr 2024; Accepted: 22 Apr 2024.

Copyright: © 2024 Fraser, Haworth, Deamer, Loakes, Richardson and Komter. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Helen Fraser, The University of Melbourne, Parkville, Australia

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Transcription and Qualitative Methods: Implications for Third Sector Research

Caitlin mcmullin.

Department of Politics & Society, Aalborg University, Aalborg, Denmark

While there is a vast literature that considers the collection and analysis of qualitative data, there has been limited attention to audio transcription as part of this process. In this paper, I address this gap by discussing the main considerations, challenges and implications of audio transcription for qualitative research on the third sector. I present a framework for conducting audio transcription for researchers and transcribers, as well as recommendations for writing up transcription in qualitative research articles.

Introduction

The field of third sector studies is inherently interdisciplinary, with studies from political science, management, sociology and social work, among others. Within the field of research, a large percentage (between 40–80%) of studies employ qualitative methods such as interviews, focus groups and ethnographic observations (von Schnurbein et al., 2018 ). In order to ensure rigor, qualitative researchers devote considerable time to developing interview guides, consent forms and coding frameworks. While there is a vast literature that considers the collection and the analysis of qualitative data, there has been comparatively limited attention paid to audio transcription, which is the conversion of recorded audio material into a written form that can be analyzed. Despite advances made in qualitative methodologies and increasing attention to positionality, subjectivity and reliability in qualitative data analysis, the transcription of interviews and focus groups is often presented uncritically as a direct conversion of recorded audio to text. As technology to facilitate transcription improves, many researchers have shifted to using voice-to-text software and companies that employ AI rather than human transcription. These technological advances in transcription, along with shifts in the way that research is undertaken (for example, increasingly via video conferencing as a result of the COVID-19 pandemic), mean that the need to critically reflect upon the place of transcription in third sector research is more urgent.

In this article, I explore the place of transcription in qualitative research, with a focus on the importance of this process for third sector researchers. The article is structured as follows. First, I review the qualitative methods literature on audio transcription and the key themes that arise. Next, I report on a review undertaken of recent qualitative research articles in Voluntas and the way that authors discuss transcription in these articles. Finally, I propose a framework for qualitative third sector researchers to include transcription as part of their research design and elements to consider in including descriptions of the transcription process in writing up qualitative research.

Audio Transcription: What We Know

At a basic level, transcription refers to the transformation of recorded audio (usually spoken word) into a written form that can be used to analyze a particular phenomenon or event (Duranti, 2006 ). For many qualitative researchers, transcription has become a fairly taken-for-granted aspect of the research process. In this section, I review the methods literature on the process of audio (and video) transcription as part of qualitative research on the third sector, focusing on three key areas—how transcription is undertaken, epistemological and ethical considerations, and the role of technology.

Qualitative research and transcription

While quantitative research seeks to explain, generalize and predict patterns through the analysis of variables, qualitative research questions are more interested in understanding and interpreting the socially constructed world around us (Bryman, 2016 ). This means that data are collected through documents, observation and interviews, and the latter are often recorded in order to analyze these as documents. For third sector research, recordings are most commonly made of interviews and focus groups, but may also be of meetings, events and other activities to ensure that researchers do not have to rely on their power of recall or scribbled notes.

Transcription is a notoriously time-consuming and often tedious task which can take between three hours and over eight hours to transcribe one hour of audio, depending on typing speed. Transcription is not, however, a mechanical process where the written document becomes an objective record of the event—indeed, written text varies from the spoken word in terms of syntax, word choice and accepted grammar (Davidson, 2009 ). The transcriber therefore has to make subjective decisions throughout about what to include (or not), whether to correct mistakes and edit grammar and repetitions. This has been described as a spectrum between “naturalized” transcription (or “intelligent verbatim”) which adapts the oral to written norms, and “denaturalized” transcription (“full verbatim”), where everything is left in, including utterances, mistakes, repetitions and all grammatical errors (Bucholtz, 2000 ).

While some contend that denaturalized transcription is more ‘accurate’, the same can equally be argued for naturalized, as it allows the transcriber to omit occasions when, for instance, an individual mis-speaks and corrects themselves, thereby allowing the transcriber to record closer to what was intended and how the interviewee might have portrayed themselves in a written form. As Lapadat ( 2000 , p. 206) explains, “Spoken language is structured and accomplished differently than written text, so when talk is re-presented as written text, it is not surprising that readers draw on their knowledge of written language to evaluate it.” Other nonverbal cues, such as laughter, tone of voice (e.g. sarcasm, frustration, emphasis) and the use or omission of punctuation, can also drastically alter the meaning or intention of what an individual says. In addition, the transcriber must make decisions about how much contextual information to include, such as interruptions, crosstalk and inaudible segments (Lapadat, 2000 ). Because of the range of types of research that employ qualitative methods, there is no single set of rules for transcription but rather these decisions must be based on the research questions and approach.

Epistemological and Ethical Considerations

Because the researcher (or external transcriber) must make these decisions as they translate audio into written text, transcription is an inherently interpretative and political act, influenced by the transcriber’s own assumptions and biases (Jaffe, 2007 ). Every choice that the transcriber makes therefore shapes how the research participant is portrayed and determines what knowledge or information is relevant and valuable and what is not. Indeed, two transcribers may hear differently and select relevant spoken material differently (Stelma & Cameron, 2007 ). As Davidson ( 2009 ) notes (and as I explore in further detail in the next section), despite being a highly interpretive process, transcription is frequently depicted using positivist norms of knowledge creation.

Transcription also involves potential ethical considerations and dilemmas. When working with disadvantaged communities, deciding how to depict research participants in written text can highlight the challenges of ethical representation. As Kvale ( 1996 , pp. 172–3) notes, “Be mindful that the publication of incoherent and repetitive verbatim interview transcripts may involve an unethical stigmatization of specific persons or groups of people”. Oliver et al. ( 2005 ) similarly demonstrate how transcribers must make decisions about how to represent participants’ use of slang, colloquialisms and accents in ways that are accurate but also respectful of the respondent’s intended meaning. Some researchers decide to send finished transcriptions to interviewees for approval in order to honor commitments to fully informed consent, to ensure transcription accuracy or in some cases as a means to address the balance of power between the researcher and interviewee. As Mero-Jaffe ( 2011 ) describes, on the one hand, this may empower interviewees to control the way that they are portrayed in the research. On the other hand, Mero-Jaffe found that seeking transcript approval from interviewees sometimes increased their embarrassment at the way that their statements appear in text. This may be especially problematic with full verbatim transcriptions.

Technology and Transcription

As technology improves and AI becomes increasingly able to create written text from recorded audio, researchers might ask—is human transcription even necessary? New options in Computer Assisted Qualitative Data Analysis Software (CAQDAS) such as NVivo, Atlas.ti and MAXQDA give qualitative researchers the option to forgo audio-to-text transcription altogether, and instead engage in live coding of audio or video files. Using this method, researchers first watch or listen to recordings to code for nonverbal cues, followed by a stage of note taking and coding based on pre-defined themes and matching these with time codes and nonverbal cues. Finally, researchers then transcribe specific quotes of interest from the recording (Parameswaran et al., 2020 ). This process may improve immersion in the data and allow researchers to account for dynamics that are often lost in complete audio-to-text transcription, such as group interactions and nonverbal communication.

There is a considerable need to develop the evidence base on the role of AI in transcription for qualitative research, with many important publications that consider the issue (e.g. Gibbs et al., 2002 ; Markle et al., 2011 ) out-of-date given the swift rate of change in AI technologies. Over the last few years, voice and speech recognition technologies have improved dramatically and may now be able to provide researchers with “good enough” first drafts of transcripts (Bokhove & Downey, 2018 ), providing certain conditions are in place (e.g. limited number of speakers and excellent audio quality). Using these technologies can save researchers time and money. As a result of the COVID-19 pandemic, many qualitative researchers are now undertaking interviews over Zoom or other video conferencing apps, which is a trend that may continue beyond the pandemic (Dodds & Hess, 2020 ). Zoom offers AI live transcription options, which benefits from the generally clear audio quality of a video conference, compared to in-person interviews where there is a greater chance of audio interference and background noise that may be undetected in the moment.

While AI may offer a cheaper and quicker alternative to human transcription, these transcripts will need to be meticulously checked by the researcher to ensure accuracy, fill in missing details or edit for context and readability. Using cloud-based AI transcription services also raises potential ethical concerns about data protection and confidentiality (Da Silva, 2021 ). There are numerous subjective decisions made in the course of creating a transcription that AI is unable to process, such as where to include punctuation, which words to include or exclude (such as filler words, hesitations, etc.) and how to denote things such as interruptions, hesitations and nonverbal cues. Voice-to-text software is also generally less accurate in discerning multiple voices or different accents (Bokhove & Downey, 2018 ). Several studies have considered how researchers/transcribers can use voice recognition software to listen and repeat the spoken text of an interview into software as a shortcut to traditional typing transcription (Matheson, 2007 ; Tilley, 2003 ), but the above shortcomings and cautions apply.

Transcription and Third Sector Research

Transcription matters for third sector research because qualitative research methodologies make up a large percentage of studies undertaken on nonprofits—as much as 40–80% of research published in this field (Igalla et al., 2019 ; Laurett & Ferreira, 2018 ; von Schnurbein et al., 2018 ). Audio transcription is particularly important for third sector research for several reasons. In conducting qualitative research (which aims to produce rich, rigorous description) and as third sector researchers (who study organizations that seek to improve society and who may be working with traditionally disenfranchised or disadvantaged communities), we have a particular ethical obligation to ensure that our research provides an accurate depiction of our participants’ lives and the organizations with which they are involved.

However, transcription is perhaps the most underacknowledged aspect of the qualitative research process, and this is also evident in the way that transcription is discussed in research articles. In order to survey the current depiction of the transcription process in third sector research, I undertook a review of the 212 most recent papers in Voluntas that include the word ‘interview’ to explore how qualitative research articles discuss transcription as part of their methodology. 1 Of these papers, 79 were deemed not applicable (because they were quantitative research papers that mentioned interviews in another context, or used the word interview to denote the administering of a structured questionnaire, or systematic review papers reporting on other research). This left 133 articles which were analyzed to explore the extent to which transcription was described—if at all—as part of the research methodology. 2

The analysis (illustrated in Fig.  1 ) found that 41% of papers employing interviews as a research method did not mention transcription at all, while 11% mentioned transcripts but not the process of transcription. It was not clear from these whether or not interviews were recorded or if researchers relied upon written notes taken during interviews, or how information from the oral interview was converted into analyzable text. The most common discussion of transcription (19%) was a simple sentence along the lines of “interviews were recorded and transcribed”, while 26% gave some further information including who undertook the transcription (the researcher(s), a research assistant or a commercial company) or that the interviews were transcribed ‘verbatim’ (with none explaining what they mean by this term). These findings are not dissimilar to a study of qualitative research in nursing, where it was found that 66% of articles reporting solely that interviews were transcribed, and the remaining articles indicated only “full” or “verbatim” to clarify the process (Wellard & McKenna, 2001 ). I also surveyed the first authors’ departmental affiliations/field of study to gauge any differences between academic fields (Table ​ (Table1) 1 ) although there were not considerable differences.

Transcription in Voluntas qualitative articles

Description of transcription and field of first authors

The fact that over half of the Voluntas articles using interviews as a research method make no mention of the transcription process is a problem for transparency in qualitative research. This tendency may be a symptom of the fact that qualitative researchers face greater challenges in academic publishing that disadvantage longer from, in-depth qualitative research to fit within prescribed word limits (Moravcsik, 2014 ). In researchers’ efforts to ensure that qualitative research meets requirements for transparency, rigor and reliability, efforts are concentrated on descriptions of case and participant selection and data analysis while transcription as the conduit between data collection and analysis remains unproblematized. This emphasis reflects the growing influence of positivist views of validity. Ignoring the subjective decisions and theoretical perspectives that determine the creation of a transcript therefore inadvertently presupposes a positivist stance on the objective nature of data which is inconsistent with qualitative methodologies.

A Framework for Undertaking and Reporting on Transcription

As shown in the previous section, there is currently widespread neglect of transcription as part of interpretive qualitative research on the third sector. In this section, I present key elements for third sector researchers to consider in regard to transcription, both to ensure rigor as part of the qualitative research process and in writing up qualitative research, drawing upon examples of good practice from previous research in Voluntas. These recommendations are based on a review of the literature as well as my personal experience as a qualitative researcher, qualitative methods teacher, and professional transcriber.

Before Transcribing: Ethics and Data Management

All decisions regarding research design, data collection and data management should be made at the beginning of a qualitative research project when applying for ethical/IRB approval from one’s university, and this includes transcription. At this stage, the researcher should confirm with their university whether they have a budget for transcription. Undertaking ethical qualitative research means ensuring standards of transparency, informed consent, confidentiality and protection of the data obtained from the research (Blaxter et al., 2001 ). Increasing concerns about data protection and legislation such as GDPR in the European Union have prompted many universities to institute strict rules about where research data can be stored. Some universities do not allow the use of certain cloud servers, such as Dropbox. These considerations should be taken into account when deciding how to undertake and record interviews (Da Silva, 2021 )—for instance, if you are recording using your mobile phone, it is important to be sure you know whether recordings automatically upload to the cloud. For this reason, it may be preferable to use a traditional digital recorder so you can manually download the files to your computer and know exactly where everything is saved.

Before Transcribing: The Interview

Before transcription can even be considered, researchers must ensure that they have a suitable audio recording, which begins with the interview itself—whenever possible, interviews should be conducted in a quiet environment without background noise or interruptions and the audio recording device should be placed close enough to the respondent to pick up their voice clearly. While recording interviews with a mobile phone has become increasingly common and easy, using a backup recording device is always a good idea to mitigate against flat batteries, full memory cards, and human error. If recording with your mobile phone, it’s also critical to remember to place it on airplane mode/‘do not disturb’ for the duration of the interview.

To Transcribe or Not to Transcribe?

While transcription from audio recordings is considered standard practice in qualitative research (Tracy, 2019 ), it is not the only way of undertaking qualitative interviews, and it is important to note that there are many reasons why it may not be desirable, appropriate or possible to record interviews at all. In relation to third sector research, this is most commonly the case in community-based research, research with political elites or research in challenging environments. One article explained that they did not record interviews because: “In sectors marked by fear, intimidation, and strong security apparatuses, recording devices would almost certainly have led to self-censorship and limited our access.” (Atia & Herrold, 2018 , p. 1046). Similarly, researchers may be unable to record in community settings because of sub-optimal recording conditions (e.g. meeting outside, noisy environments, etc.) or because using recording device makes participants uncomfortable or reinforces power relations between the researcher and participants (Quintanilha et al., 2015 ).

If researchers decide not to comprehensively transcribe recordings, or decide not to record qualitative fieldwork at all, this should be noted and explained in relation to methods. Other methods of notetaking and analysis may be more suited to certain types of ethnographic research, such as reflexive journaling (Halcomb & Davidson, 2006 ), or Systematic and Reflexive Interviewing and Reporting—a process by which a researcher and research assistant jointly interview participants and write their own reports that include observations and analyses, which are collaboratively analyzed (Loubere, 2017 ).

How to Transcribe?

Traditionally, transcribers used foot pedals to play, rewind and fast forward tape recordings while they typed. Now that audio files are digital, several free and low cost programs are available (such as Express Scribe and oTranscribe) that let transcribers set up hot keys to perform the same actions without having to navigate away from their transcript document.

The degree of detail to include in transcripts should be decided upon before interviews are transcribed. This is important because previous research has demonstrated that the format selected for transcription significantly impacts how the researcher interprets the data (Mishler, 2003 ; Packer, 2017 ). There is no one best or “most accurate” style of transcription, but rather, a researcher should consider the particular theoretical background and research questions of the study in order to determine where on the scale of full verbatim to intelligent verbatim is most appropriate for the study. Because third sector research is most commonly associated with social science and business disciplines rather than linguistics, it will rarely be necessary or appropriate to employ the conventions of conversation analysis or extreme levels of denaturalized transcription (Bucholtz, 2000 ). Indeed, it might most frequently be appropriate to employ a version of naturalized/intelligent verbatim, so that any participants’ quotes included in written works are more ‘readable’ and do not include excessive repetitions or verbal fillers such as ‘um’.

If the researcher determines that naturalized or intelligent verbatim transcription is the most appropriate for their study, several considerations should be heeded in order to ensure that meaning is not distorted or lost. First, indications of laughter, nonverbal cues (such as sighs, huffs, finger-snaps, sobbing or even blowing raspberries) should be included if these convey important meaning. Other considerations of how to transcribe may be based more on personal preference and the ability to produce a document that is easily analyzable in the researcher’s chosen medium. For instance, wide margins on one side can be useful for researchers who choose to analyze their data on paper or in Microsoft Word, while other more flowing templates will work better to import into software such as NVivo. It can also be useful to include time stamps for unclear or inaudible statements, or at regular intervals (e.g. every minute) which makes it much easier to check a transcript against the original audio.

Who Transcribes?

As discussed in the consideration of qualitative studies, the prevalence of the passive voice when reporting on transcription (i.e. “interviews were transcribed”) obscures the important distinction of who undertook the transcription. If the researcher transcribes recordings themselves, then it is generally acceptable to assume the coherence between the research approach and approach to transcription, as well as the researcher’s confidence that the written transcript is an accurate record of the event/interview that took place. If, however, the researchers choose to outsource transcription to a research assistant or commercial transcription company, then care should be taken to give detailed and thorough instructions about the elements described above. The researcher should also spot check transcripts for accuracy, fill in any missed words/inaudibles and ensure that the transcription document fulfils their expectations in regard to level of verbatim, style and formatting.

Ideally, transcribers should be hired who have specialist knowledge of the subject matter and familiarity with the accents or dialect of the speakers. They should be provided with a key information about the project, such as the research questions, important terms and acronyms. Lapadat ( 2000 ) provides several useful suggestions when hiring transcribers in order to ensure transcription quality and increase rigor. First, rather than fully outsourcing transcription, the researchers can transcribe some interviews or portions of interviews themselves in order to provide an example for transcribers and develop a transcription protocol. Another option when employing research assistants to transcribe interviews is to include them directly in the interviews (either as a co-interviewer or observer), so they have direct involvement in the research and context.

Finally, when working with external transcribers it can also be valuable to encourage transcribers to keep memos of the transcription process or contextual observations and impressions that may not come through in the written text. For instance, does the interviewee sound tired, frustrated, distracted or nervous? Does the interviewer interrupt the respondent frequently (which the transcriber may choose to edit for readability)? Or did the interview take place somewhere public, like a cafe, which may have made the respondent more guarded? Such information is often lost, particularly in projects that involve multiple research team members (for instance, a PI, multiple interviewers, research assistants and/or professional transcribers).

Writing about Transcription

Due to limited space or word limits, it is not typically possible or desirable to include all of the above details in research articles. Instead, at a minimum, researchers should include who transcribed the audio recordings as part of a commitment to ethical and transparent qualitative research. If this was done by anyone other than the researchers, authors should ideally describe the measures taken to ensure accuracy (developing a protocol for transcribers, spot checking, proofreading, sending transcripts to interviewees if appropriate) and ethical considerations (such as data protection and confidentiality).

Second, researchers should indicate the type of transcription—whether selective (pulling out relevant quotes and themes, or transcribing just the ‘gist’), intelligent verbatim/naturalized or full verbatim/denaturalized. The choice of type of transcription should align to the researcher’s epistemological position and theoretical framework.

Finally, researchers should include any other subjective decision-making that took place during the transcription process, in much the same way that researchers are encouraged to be transparent about their subjectivity and positionality in undertaking interviews and analysis of qualitative data (McCorkel & Myers, 2003 ). This may include information about selecting the level of verbatim, working with external transcribers, feedback from interviewees on transcripts or efforts to ensure accuracy of transcripts and coherence with the research approach.

The following quotes provide good examples of how to write about transcription:

The interviews, which were conducted in the native language of the interviewees by six female Hebrew-Arabic-speaking interviewers, were recorded, translated, and transcribed verbatim. […] Immediately following the interview, each interviewer transcribed and translated her interviews into Hebrew. In this manner, we sought to achieve a translation that was as close as possible to the interviewer’s insights regarding the participants, and we regarded the interviewers as active agents in the creation of knowledge. (Yanay-Ventura et al., 2020 , p. 6) Three Spanish speaking investigators transcribed all of the interviews from audio recording devices, checked each other’s transcription for accuracy, and analyzed the interviews using thematic analysis (Braun & Clarke, 2006 ). The transcribers observed the focus groups and took notes on participants’ voices and other identifying traits to help the transcription process go more smoothly. Researchers aided the transcribers in this regard by asking participants at the beginning of the focus groups to introduce themselves using a pseudonym and briefly remark upon how they preferred to spend their time. (Schwingel et al., 2017 , p. 170)

In both of these examples, the authors treat the process of transcription as part of the broader research process, rather than as an automatic conversion of audio to text. While there is limited clarification about the type of transcription (beyond ‘verbatim’), the discussion of the subjective decision-making as part of the transcription process and acknowledgment of the agency of the individuals undertaking transcription increases transparency and therefore rigor.

Conclusions

Qualitative research can help us to understand some of the important issues impacting the third sector in ways that quantitative methods fall short of explaining, such as the ways that individuals and organizations make sense of public policy and societal challenges, how and why organizations design their services and activities in particular ways, and the intricacies of the relationships between boards, executives, staff and volunteers. Qualitative methods training stresses that an interpretivist epistemological position sees knowledge as socially constructed, yet transcription has slipped through the cracks of methodological examination in the process of creating and interpreting meaning.

In this short article, I sought to draw our attention to this important stage of qualitative data collection and analysis and call on third sector researchers to critically reflect upon transcription both in conducting research and in writing about it. I have focused primarily on the transcription of interviews , rather than focus groups or other multi-person events. All of the points raised in my framework transcription apply to these methods of data collection as well; however, there are further issues that need to be taken into consideration regarding focus groups that warrant further attention, such as the issues of power and accuracy of transcription when there are multiple people speaking and interrupting one another. Researchers employing multi-person recordings should therefore devote more time and consideration to transcription. Finally, technology continues to advance in the area of voice recognition, which may save researchers considerable time and/or money in transcription; however, I implore scholars to see transcription through an interpretivist rather than positivist lens, to ensure that the production of written transcripts is not approached as the creation of objective knowledge.

Appendix 1: Articles Reviewed

• Aasland, A., Kropp, S., & Meylakhs, A. Y. (2020). Between Collaboration and Subordination: State and Non-state Actors in Russian Anti-drug Policy. Voluntas , 31 (2), 422–436. https://doi.org/10.1007/s11266-019-00158-9
• Åberg, P., Einarsson, S., & Reuter, M. (2021). Think Tanks: New Organizational Actors in a Changing Swedish Civil Society. Voluntas , 32 (3), 634–648. https://doi.org/10.1007/s11266-019-00174-9
• Afkhami, A., Nasr Isfahani, A., Abzari, M., & Teimouri, H. (2019). Toward a Deep Insight into Employee Participation in Employer-Supported Volunteering in Iranian Organizations: A Grounded Theory. Voluntas , 30 (5), 1036–1053. https://doi.org/10.1007/s11266-019-00141-4
• Anasti, T. (2020). The Strategic Action Field of Sex Work and Sex Trafficking: A Case Study of a Contentious Field in Chicago. Voluntas , 31 (1), 169–183. https://doi.org/10.1007/s11266-019-00161-0
• Appe, S. (2018). Directions in a Post-aid World? South–South Development Cooperation and CSOs in Latin America. Voluntas , 29 (2), 271–283. https://doi.org/10.1007/s11266-017-9838-0
• Arhin, A. A., Kumi, E., & Adam, M.-A. S. (2018). Facing the Bullet? Non-Governmental Organisations’ (NGOs’) Responses to the Changing Aid Landscape in Ghana. Voluntas , 29 (2), 348–360. https://doi.org/10.1007/s11266-018-9966-1
• Arvidson, M. (2018). Change and Tensions in Non-profit Organizations: Beyond the Isomorphism Trajectory. Voluntas , 29 (5), 898–910. https://doi.org/10.1007/s11266-018-0021-z
• Atia, M., & Herrold, C. E. (2018). Governing Through Patronage: The Rise of NGOs and the Fall of Civil Society in Palestine and Morocco. Voluntas , 29 (5), 1044–1054. https://doi.org/10.1007/s11266-018-9953-6
• Ávila, L., & Amorim, M. (2021). Organisational Identity of Social Enterprises: A Taxonomic Approach. Voluntas , 32 (1), 13–27. https://doi.org/10.1007/s11266-020-00264-z
• Baillie Smith, M., Fadel, B., O’Loghlen, A., & Hazeldine, S. (2020). Volunteering Hierarchies in the Global South: Remuneration and Livelihoods. Voluntas . https://doi.org/10.1007/s11266-020-00254-1
• Bandini, F., Gigli, S., & Mariani, L. (2021). Social Enterprises and Public Value: A Multiple-Case Study Assessment. Voluntas , 32 (1), 61–77. https://doi.org/10.1007/s11266-020-00285-8
• Bano, M. (2019). Partnerships and the Good-Governance Agenda: Improving Service Delivery Through State–NGO Collaborations. Voluntas , 30 (6), 1270–1283. https://doi.org/10.1007/s11266-017-9937-y
• Barinaga, E. (2020). Coopted! Mission Drift in a Social Venture Engaged in a Cross-Sectoral Partnership. Voluntas , 31 (2), 437–449. https://doi.org/10.1007/s11266-018-0019-6
• Bayalieva-Jailobaeva, K. (2018). New Donor Strategies: Implications for NGOs in Post-Soviet Kyrgyzstan. Voluntas , 29 (2), 284–295. https://doi.org/10.1007/s11266-017-9878-5
• Beaton, E. E. (2021). No Margin, No Mission: How Practitioners Justify Nonprofit Managerialization. Voluntas , 32 (3), 695–708. https://doi.org/10.1007/s11266-019-00189-2
• Bergfeld, A., Plagmann, C., & Lutz, E. (2021). Know Your Counterparts: The Importance of Wording for Stakeholder Communication in Social Franchise Enterprises. Voluntas , 32 (1), 104–119. https://doi.org/10.1007/s11266-020-00289-4
• Bidet, E., Eum, H., & Ryu, J. (2018). Diversity of Social Enterprise Models in South Korea. Voluntas , 29 (6), 1261–1273. https://doi.org/10.1007/s11266-018-9951-8
• Bies, A., & Kennedy, S. (2019). The State and the State of the Art on Philanthropy in China. Voluntas , 30 (4), 619–633. https://doi.org/10.1007/s11266-019-00142-3
• Bradford, A., Luke, B., & Furneaux, C. (2020). Exploring Accountability in Social Enterprise: Priorities, Practicalities, and Legitimacy. Voluntas , 31 (3), 614–626. https://doi.org/10.1007/s11266-020-00215-8
• Cannon, S. M. (2020). Legitimacy as Property and Process: The Case of an Irish LGBT Organization. Voluntas , 31 (1), 39–55. https://doi.org/10.1007/s11266-019-00091-x
• Carlsen, H. B., Doerr, N., & Toubøl, J. (2020). Inequality in Interaction: Equalising the Helper–Recipient Relationship in the Refugee Solidarity Movement. Voluntas . https://doi.org/10.1007/s11266-020-00268-9
• Chatterjee, D., Subramanian, B., & Hota, P. K. (2020). Professionalization and Hybridization Dynamics of Social Enterprises. Voluntas , 31 (3), 457–471. https://doi.org/10.1007/s11266-020-00217-6
• Chatzichristos, G., & Nagopoulos, N. (2020). Social Entrepreneurship and Institutional Sustainability: Insights from an Embedded Social Enterprise. Voluntas , 31 (3), 484–493. https://doi.org/10.1007/s11266-019-00188-3
• Chaves-Avila, R., & Savall-Morera, T. (2019). The Social Economy in a Context of Austerity Policies: The Tension Between Political Discourse and Implemented Policies in Spain. Voluntas , 30 (3), 487–498. https://doi.org/10.1007/s11266-018-00075-3
• Chewinski, M. (2019). Coordinating Action: NGOs and Grassroots Groups Challenging Canadian Resource Extraction Abroad. Voluntas , 30 (2), 356–368. https://doi.org/10.1007/s11266-018-0023-x
• Clear, A., Paull, M., & Holloway, D. (2018). Nonprofit Advocacy Tactics: Thinking Inside The Box? Voluntas , 29 (4), 857–869. https://doi.org/10.1007/s11266-017-9907-4
• Clerkin, B., & Quinn, M. (2019). Restricted Funding: Restricting Development? Voluntas , 30 (6), 1348–1364. https://doi.org/10.1007/s11266-018-00048-6
• Cookingham Bailey, E. (2020). Advocacy and Service Delivery in the Voluntary Sector: Exploring the History of Voluntary Sector Activities for New Minority and Migrant Groups in East London, 1970s–1990s. Voluntas . https://doi.org/10.1007/s11266-020-00253-2
• Crack, A. M. (2018). The Regulation of International NGOS: Assessing the Effectiveness of the INGO Accountability Charter. Voluntas , 29 (2), 419–429. https://doi.org/10.1007/s11266-017-9866-9
• Crotty, J., & Ljubownikow, S. (2020). Creating Organisational Strength from Operationalising Restrictions: Welfare Non-profit Organisations in the Russian Federation. Voluntas , 31 (6), 1148–1158. https://doi.org/10.1007/s11266-020-00271-0
• Deng, G. (2019). Trends in Overseas Philanthropy by Chinese Foundations. Voluntas , 30 (4), 678–691. https://doi.org/10.1007/s11266-017-9868-7
• Dinh, K., Hong, K. T., Haire, B., & Worth, H. (2021). Historic and Contemporary Influences on HIV Advocacy in Vietnam. Voluntas , 32 (3), 610–620. https://doi.org/10.1007/s11266-020-00220-x
• Dong, Q., Guo, J., & Huang, C.-C. (2019). Nonprofit Alliance in China: Effects of Alliance Process on Goal Achievement. Voluntas , 30 (2), 300–311. https://doi.org/10.1007/s11266-018-9990-1
• Edenfield, A. C., & Andersson, F. O. (2018). Growing Pains: The Transformative Journey from a Nascent to a Formal Not-For-Profit Venture. Voluntas , 29 (5), 1033–1043. https://doi.org/10.1007/s11266-017-9936-z
• Elsayed, Y. (2018). At the Intersection of Social Entrepreneurship and Social Movements: The Case of Egypt and the Arab Spring. Voluntas , 29 (4), 819–831. https://doi.org/10.1007/s11266-017-9943-0
• Eschweiler, J., Svensson, S., Mocca, E., Cartwright, A., & Villadsen Nielsen, L. (2019). The Reciprocity Dimension of Solidarity: Insights from Three European Countries. Voluntas , 30 (3), 549–561. https://doi.org/10.1007/s11266-018-0031-x
• Eynaud, P., Juan, M., & Mourey, D. (2018). Participatory Art as a Social Practice of Commoning to Reinvent the Right to the City. Voluntas , 29 (4), 621–636. https://doi.org/10.1007/s11266-018-0006-y
• Falkenhain, M. (2020). Dividing Lines: Understanding the Creation and Replication of Fragmentations Among NGOs in Hybrid Regimes. Voluntas , 31 (4), 663–673. https://doi.org/10.1007/s11266-019-00185-6
• Fang, Q., Fisher, K. R., & Li, B. (2020). Follower or Challenger? How Chinese Non-governmental Organizations Manage Accountability Requirements from Funders. Voluntas , 31 (4), 722–735. https://doi.org/10.1007/s11266-019-00184-7
• Farid, M., & Li, H. (2021). Reciprocal Engagement and NGO Policy Influence on the Local State in China. Voluntas , 32 (3), 597–609. https://doi.org/10.1007/s11266-020-00288-5
• Fehsenfeld, M., & Levinsen, K. (2019). Taking Care of the Refugees: Exploring Advocacy and Cross-sector Collaboration in Service Provision for Refugees. Voluntas , 30 (2), 422–435. https://doi.org/10.1007/s11266-019-00097-5
• Ferguson, G. (2018). The Social Economy in Bolivia: Indigeneity, Solidarity, and Alternatives to Capitalism. Voluntas , 29 (6), 1233–1243. https://doi.org/10.1007/s11266-018-0013-z
• Fulton, B. R., & Wood, R. L. (2018). Civil Society Organizations and the Enduring Role of Religion in Promoting Democratic Engagement. Voluntas , 29 (5), 1068–1079. https://doi.org/10.1007/s11266-018-9965-2
• Gaby, S. (2020). Reconfiguring Organizational Commitments: Boundary Crossing in Civic Groups. Voluntas , 31 (6), 1121–1133. https://doi.org/10.1007/s11266-020-00272-z
• Gaeta, G. L., Ghinoi, S., Silvestri, F., & Trasciani, G. (2021). Exploring Networking of Third Sector Organizations: A Case Study Based on the Quartieri Spagnoli Neighborhood in Naples (Italy). Voluntas , 32 (4), 750–766. https://doi.org/10.1007/s11266-020-00241-6
• García-Orosa, B., & Pérez-Seijo, S. (2020). The Use of 360° Video by International Humanitarian Aid Organizations to Spread Social Messages and Increase Engagement. Voluntas , 31 (6), 1311–1329. https://doi.org/10.1007/s11266-020-00280-z
• Grazioli, M., & Caciagli, C. (2018). Resisting to the Neoliberal Urban Fabric: Housing Rights Movements and the Re-appropriation of the ‘Right to the City’ in Rome, Italy. Voluntas , 29 (4), 697–711. https://doi.org/10.1007/s11266-018-9977-y
• Grubb, A., & Henriksen, L. S. (2019). On the Changing Civic Landscape in Denmark and its Consequences for Civic Action. Voluntas , 30 (1), 62–73. https://doi.org/10.1007/s11266-018-00054-8
• Guha, P. (2019). Going to Scale: A Case Study of an Indian Educational NGO. Voluntas , 30 (6), 1365–1379. https://doi.org/10.1007/s11266-019-00099-3
• Guo, C., & Lai, W. (2019). Community Foundations in China: In Search of Identity? Voluntas , 30 (4), 647–663. https://doi.org/10.1007/s11266-017-9932-3
• Heckert, R., Boumans, J., & Vliegenthart, R. (2020). How to Nail the Multiple Identities of an Organization? A Content Analysis of Projected Identity. Voluntas , 31 (1), 129–141. https://doi.org/10.1007/s11266-019-00157-w
• Heideman, L. J. (2018). Making Civil Society Sustainable: The Legacy of USAID in Croatia. Voluntas , 29 (2), 333–347. https://doi.org/10.1007/s11266-017-9896-3
• Hjort, M., & Beswick, D. (2020). Volunteering and Policy Makers: The Political Uses of the UK Conservative Party’s International Development Volunteering Projects. Voluntas . https://doi.org/10.1007/s11266-020-00222-9
• Holthaus, L. (2019). Furthering Pluralism? The German Foundations in Transitional Tunisia. Voluntas , 30 (6), 1284–1296. https://doi.org/10.1007/s11266-018-00074-4
• Horvath, A. (2020). The Transformative Potential of Experience: Learning, Group Dynamics, and the Development of Civic Virtue in a Mobile Soup Kitchen. Voluntas , 31 (5), 981–994. https://doi.org/10.1007/s11266-018-00062-8
• Horvath, A., Brandtner, C., & Powell, W. W. (2018). Serve or Conserve: Mission, Strategy, and Multi-Level Nonprofit Change During the Great Recession. Voluntas , 29 (5), 976–993. https://doi.org/10.1007/s11266-017-9948-8
• Huang, Y. (2019). At the Mercy of the State: Health Philanthropy in China. Voluntas , 30 (4), 634–646. https://doi.org/10.1007/s11266-018-9960-7
• Ismail, A., & Johnson, B. (2019). Managing Organizational Paradoxes in Social Enterprises: Case Studies from the MENA Region. Voluntas , 30 (3), 516–534. https://doi.org/10.1007/s11266-018-00083-3
• Jezierska, K., & Polanska, D. V. (2018). Social Movements Seen as Radical Political Actors: The Case of the Polish Tenants’ Movement. Voluntas , 29 (4), 683–696. https://doi.org/10.1007/s11266-017-9917-2
• Kampen, T., Veldboer, L., & Kleinhans, R. (2019). The Obligation to Volunteer as Fair Reciprocity? Welfare Recipients’ Perceptions of Giving Back to Society. Voluntas , 30 (5), 991–1005. https://doi.org/10.1007/s11266-018-00082-4
• Kang, Y. (2019). What Does China’s Twin-Pillared NGO Funding Game Entail? Growing Diversity and Increasing Isomorphism. Voluntas , 30 (3), 499–515. https://doi.org/10.1007/s11266-018-00085-1
• Kappelides, P., Cuskelly, G., & Hoye, R. (2019). The Influence of Volunteer Recruitment Practices and Expectations on the Development of Volunteers’ Psychological Contracts. Voluntas , 30 (1), 259–271. https://doi.org/10.1007/s11266-018-9986-x
• Kennedy, D. (2019). The Inherently Contested Nature of Nongovernmental Accountability: The Case of HAP International. Voluntas , 30 (6), 1393–1405. https://doi.org/10.1007/s11266-019-00134-3
• Kewes, A., & Munsch, C. (2019). Should I Stay or Should I Go? Engaging and Disengaging Experiences in Welfare-Sector Volunteering. Voluntas , 30 (5), 1090–1103. https://doi.org/10.1007/s11266-019-00122-7
• Korstenbroek, T., & Smets, P. (2019). Developing the Potential for Change: Challenging Power Through Social Entrepreneurship in the Netherlands. Voluntas , 30 (3), 475–486. https://doi.org/10.1007/s11266-019-00107-6
• Koubek, M. (2020). When Did They Protest? Beyond Co-optation or Channeling: Effects of EU Funding on Czech Romani NGOs and Civil Society. Voluntas , 31 (2), 404–421. https://doi.org/10.1007/s11266-018-0008-9
• Kravchenko, Z., & Moskvina, A. (2018). Entrepreneurial NPOs in Russia: Rationalizing the Mission. Voluntas , 29 (5), 962–975. https://doi.org/10.1007/s11266-018-0016-9
• Kumi, E. (2019). Aid Reduction and NGDOs’ Quest for Sustainability in Ghana: Can Philanthropic Institutions Serve as Alternative Resource Mobilisation Routes? Voluntas , 30 (6), 1332–1347. https://doi.org/10.1007/s11266-017-9931-4
• Lall, S. A. (2019). From Legitimacy to Learning: How Impact Measurement Perceptions and Practices Evolve in Social Enterprise–Social Finance Organization Relationships. Voluntas , 30 (3), 562–577. https://doi.org/10.1007/s11266-018-00081-5
• Lang, R., & Mullins, D. (2020). Field Emergence in Civil Society: A Theoretical Framework and Its Application to Community-Led Housing Organisations in England. Voluntas , 31 (1), 184–200. https://doi.org/10.1007/s11266-019-00138-z
• Laurent, A., Garaudel, P., Schmidt, G., & Eynaud, P. (2020). Civil Society Meta-organizations and Legitimating Processes: The Case of the Addiction Field in France. Voluntas , 31 (1), 19–38. https://doi.org/10.1007/s11266-019-00094-8
• Lee, E. K. M., & Chandra, Y. (2020). Dynamic and Marketing Capabilities as Predictors of Social Enterprises’ Performance. Voluntas , 31 (3), 587–600. https://doi.org/10.1007/s11266-019-00155-y
• Li, S. (2020). Global Civil Society Under the New INGO Regulatory Law: A Comparative Case Study on Two INGOs in China. Voluntas , 31 (4), 751–761. https://doi.org/10.1007/s11266-019-00101-y
• Li, S., & Wang, X. (2020). Seeking Credibility from Uncertainty: How Formal Cooptation Institution Unleashes Outspoken NGOs. Voluntas , 31 (4), 711–721. https://doi.org/10.1007/s11266-020-00204-x
• Liu, F., & Zhang, X. (2020). Who are Volunteers in Urban China? Voluntas . https://doi.org/10.1007/s11266-020-00251-4
• Lund, V., & Juujärvi, S. (2018). Residents’ Agency Makes a Difference in Volunteering in an Urban Neighbourhood. Voluntas , 29 (4), 756–769. https://doi.org/10.1007/s11266-018-9955-4
• Marie, B., Isabelle, M., Jordan, B.-L., Hélène, C., Sophie, É., Julie, F., Christine, M., & Andrée, S. (2020). Promising Practices of Nonprofit Organizations to Respond to the Challenges Faced in Countering the Mistreatment of Older Adults. Voluntas , 31 (6), 1359–1370. https://doi.org/10.1007/s11266-020-00252-3
• Martinez, D. E., & Cooper, D. J. (2020). Seeing Through the Logical Framework. Voluntas , 31 (6), 1239–1253. https://doi.org/10.1007/s11266-020-00223-8
• Mathews, M. A. (2020). The Embeddedness of Nonprofit Leadership in Civic Governance. Voluntas , 31 (1), 201–212. https://doi.org/10.1007/s11266-019-00139-y
• Mati, J. M. (2020). Civil Society in ‘Politics’ and ‘Development’ in African Hybrid Regimes: The Kenyan Case. Voluntas , 31 (4), 674–687. https://doi.org/10.1007/s11266-020-00211-y
• Maya-Jariego, I., Holgado-Ramos, D., González-Tinoco, E., Muñoz-Alvis, A., & Ortega, M. (2020). More Money, More Problems? Resource Dependence and Professionalization of Non-governmental Social Services Organizations in Southern Spain. Voluntas , 31 (6), 1212–1225. https://doi.org/10.1007/s11266-020-00256-z
• McLennan, B. J. (2020). Conditions for Effective Coproduction in Community-Led Disaster Risk Management. Voluntas , 31 (2), 316–332. https://doi.org/10.1007/s11266-018-9957-2
• McMullin, C., & Skelcher, C. (2018). The Impact of Societal-Level Institutional Logics on Hybridity: Evidence from Nonprofit Organizations in England and France. Voluntas , 29 (5), 911–924. https://link.springer.com/article/10.1007/s11266-018-9996-8
• Meijs, L., Handy, F., Simons, F.-J., & Roza, L. (2020). A Social Innovation: Addressing Relative Food Insecurity and Social Exclusion. Voluntas , 31 (5), 894–906. https://doi.org/10.1007/s11266-019-00105-8
• Meyer, M., & Simsa, R. (2018). Organizing the Unexpected: How Civil Society Organizations Dealt with the Refugee Crisis. Voluntas , 29 (6), 1159–1175. https://doi.org/10.1007/s11266-018-00050-y
• Mikołajczak, P. (2020). Social Enterprises’ Hybridity in the Concept of Institutional Logics: Evidence from Polish NGOs. Voluntas , 31 (3), 472–483. https://doi.org/10.1007/s11266-020-00195-9
• Milbourn, B., Black, M. H., & Buchanan, A. (2019). Why People Leave Community Service Organizations: A Mixed Methods Study. Voluntas , 30 (1), 272–281. https://doi.org/10.1007/s11266-018-0005-z
• Molina, J. L., Valenzuela-García, H., Lubbers, M. J., Escribano, P., & Lobato, M. M. (2018). “The Cowl Does Make The Monk”: Understanding the Emergence of Social Entrepreneurship in Times of Downturn. Voluntas , 29 (4), 725–739. https://doi.org/10.1007/s11266-017-9921-6
• Moraes, R. L., & Andion, C. (2018). Civil Society and Social Innovation in Public Arenas in Brazil: Trajectory and Experience of the Movement Against Electoral Corruption (MCCE). Voluntas , 29 (4), 801–818. https://doi.org/10.1007/s11266-017-9867-8
• Morais-da-Silva, R. L., Segatto, A. P., & Bezerra-de-Sousa, I. G. (2020). Connecting Two Sides: A Qualitative Study on Social Innovation Ventures and Poor Communities in an Emerging Economy. Voluntas , 31 (5), 966–980. https://doi.org/10.1007/s11266-019-00156-x
• Musah-Surugu, I. J., Bawole, J. N., & Ahenkan, A. (2019). The “Third Sector” and Climate Change Adaptation Governance in Sub-Saharan Africa: Experience from Ghana. Voluntas , 30 (2), 312–326. https://doi.org/10.1007/s11266-018-9962-5
• Mutongwizo, T. (2018). Comparing NGO Resilience and ‘Structures of Opportunity’ in South Africa and Zimbabwe (2010–2013). Voluntas , 29 (2), 373–387. https://doi.org/10.1007/s11266-017-9881-x
• Näslund, H. (2020). Collective Deliberations and Hearts on Fire: Experiential Knowledge Among Entrepreneurs and Organisations in the Mental Health Service User Movement. Voluntas . https://doi.org/10.1007/s11266-020-00233-6
• Nimu, A. (2018). Surviving Mechanisms and Strategies of Gender Equality NGOs in Romania and Poland. Voluntas , 29 (2), 310–332. https://doi.org/10.1007/s11266-018-9961-6
• Noh, J.-E. (2019). Human Rights-Based Child Sponsorship: A Case Study of ActionAid. Voluntas , 30 (6), 1420–1432. https://doi.org/10.1007/s11266-018-0010-2
• Obaze, Y. (2020). Supply Chain Challenges and Shared Value Destruction in the Community-Based Supply Chain. Voluntas , 31 (3), 550–562. https://doi.org/10.1007/s11266-020-00202-z
• Onyx, J., Darcy, S., Grabowski, S., Green, J., & Maxwell, H. (2018). Researching the Social Impact of Arts and Disability: Applying a New Empirical Tool and Method. Voluntas , 29 (3), 574–589. https://doi.org/10.1007/s11266-018-9968-z
• Pape, U., Brandsen, T., Pahl, J. B., Pieliński, B., Baturina, D., Brookes, N., Chaves-Ávila, R., Kendall, J., Matančević, J., Petrella, F., Rentzsch, C., Richez-Battesti, N., Savall-Morera, T., Simsa, R., & Zimmer, A. (2020). Changing Policy Environments in Europe and the Resilience of the Third Sector. Voluntas , 31 (1), 238–249. https://doi.org/10.1007/s11266-018-00087-z
• Parker, M. A., Mook, L., Kao, C.-Y., & Murdock, A. (2020). Accountability and Relationship-Definition Among Food Banks Partnerships. Voluntas , 31 (5), 923–937. https://doi.org/10.1007/s11266-019-00150-3
• Peci, A., Oquendo, M. I., & Mendonça, P. (2020). Collaboration, (Dis)trust and Control in Brazilian Manufactured Public/Non-profit Partnerships. Voluntas , 31 (2), 375–389. https://doi.org/10.1007/s11266-018-0027-6
• Peralta, K. J., & Vaitkus, E. (2019). Constructing Action: An Analysis of the Roles of Third Sector Actors During the Implementation of the Dominican Republic’s Regularization Plan. Voluntas , 30 (6), 1319–1331. https://doi.org/10.1007/s11266-018-0003-1
• Pettigrew, S., Jongenelis, M., Jackson, B., & Newton, R. U. (2019). “Charity Begins at Home”: Informal Caring Barriers to Formal Volunteering Among Older People. Voluntas , 30 (5), 921–931. https://doi.org/10.1007/s11266-018-0017-8
• Pfeilstetter, R. (2020). Doing Good and Selling Goods. Voluntas , 31 (3), 511–520. https://doi.org/10.1007/s11266-020-00194-w
• Pixová, M. (2018). The Empowering Potential of Reformist Urban Activism in Czech Cities. Voluntas , 29 (4), 670–682. https://doi.org/10.1007/s11266-018-0011-1
• Pluciński, P. (2018). Forces of Altermodernization: Urban Social Movements and the New Urban Question in Contemporary Poland. Voluntas , 29 (4), 653–669. https://doi.org/10.1007/s11266-018-0007-x
• Popplewell, R. (2018). Civil Society, Legitimacy and Political Space: Why Some Organisations are More Vulnerable to Restrictions than Others in Violent and Divided Contexts. Voluntas , 29 (2), 388–403. https://doi.org/10.1007/s11266-018-9949-2
• Puljek-Shank, R. (2018). Civic Agency in Governance: The Role of Legitimacy with Citizens vs. Donors. Voluntas , 29 (4), 870–883. https://doi.org/10.1007/s11266-018-0020-0
• Reijnders, M., Schalk, J., & Steen, T. (2018). Services Wanted? Understanding the Non-take-up of Social Support at the Local Level. Voluntas , 29 (6), 1360–1374. https://doi.org/10.1007/s11266-018-00060-w
• Ruiz Sportmann, A. S., & Greenspan, I. (2019). Relational Interactions Between Immigrant and Native-Born Volunteers: Trust-Building and Integration or Suspicion and Conflict? Voluntas , 30 (5), 932–946. https://doi.org/10.1007/s11266-019-00108-5
• Sandberg, B., Elliott, E., & Petchel, S. (2020). Investigating the Marketization of the Nonprofit Sector: A Comparative Case Study of Two Nonprofit Organizations. Voluntas , 31 (3), 494–510. https://doi.org/10.1007/s11266-019-00159-8
• Schmid, H., & Almog-Bar, M. (2020). The Critical Role of the Initial Stages of Cross-Sector Partnerships and Their Implications for Partnerships’ Outcomes. Voluntas , 31 (2), 286–300. https://doi.org/10.1007/s11266-019-00137-0
• Sepulveda, L., Lyon, F., & Vickers, I. (2020). Implementing Democratic Governance and Ownership: The Interplay of Structure and Culture in Public Service Social Enterprises. Voluntas , 31 (3), 627–641. https://doi.org/10.1007/s11266-020-00201-0
• Shier, M. L., & Handy, F. (2020). Leadership in Nonprofits: Social Innovations and Blurring Boundaries. Voluntas , 31 (2), 333–344. https://doi.org/10.1007/s11266-018-00078-0
• Spencer, S. B., & Skalaban, I. A. (2018). Organizational Culture in Civic Associations in Russia. Voluntas , 29 (5), 1080–1097. https://doi.org/10.1007/s11266-017-9925-2
• Stewart, A. J., & Twumasi, A. (2020). Minding the Gap: An Exploratory Study Applying Theory to Nonprofit Board Management of Executive Transitions. Voluntas , 31 (6), 1268–1281. https://doi.org/10.1007/s11266-020-00244-3
• Stougaard, M. S. (2020). Co-producing Public Welfare Services with Vulnerable Citizens: A Case Study of a Danish-Somali Women’s Association Co-producing Crime Prevention with the Local Authorities. Voluntas . https://doi.org/10.1007/s11266-020-00235-4
• Strichman, N., Marshood, F., & Eytan, D. (2018). Exploring the Adaptive Capacities of Shared Jewish–Arab Organizations in Israel. Voluntas , 29 (5), 1055–1067. https://doi.org/10.1007/s11266-017-9904-7
• Suter, P., & Gmür, M. (2018). Volunteer Engagement in Housing Co-operatives: Civil Society “en miniature”. Voluntas , 29 (4), 770–789. https://doi.org/10.1007/s11266-018-9959-0
• Svensson, K., & Gallo, C. (2018). The Creation of an Unsolicited Organization: Victim Support Sweden. Voluntas , 29 (5), 1008–1018. https://doi.org/10.1007/s11266-018-9978-x
• Tadesse, H. A., & Steen, T. (2019). Exploring the Impact of Political Context on State–Civil Society Relations: Actors’ Strategies in a Developmental State. Voluntas , 30 (6), 1256–1269. https://doi.org/10.1007/s11266-018-00077-1
• Taylor, R., Torugsa, N. (Ann), & Arundel, A. (2020). Organizational Pathways for Social Innovation and Societal Impacts in Disability Nonprofits. Voluntas , 31 (5), 995–1012. https://doi.org/10.1007/s11266-019-00113-8
• Uzcanga, C., & Oiarzabal, P. J. (2019). Associations of Migrants in Spain: An Enquiry into Their Digital Inclusion in the “Network Society” in the 2010s. Voluntas , 30 (5), 947–961. https://doi.org/10.1007/s11266-019-00162-z
• van der Veer, L. (2020). Treacherous Elasticity, Callous Boundaries: Aspiring Volunteer Initiatives in the Field of Refugee Support in Rotterdam. Voluntas . https://doi.org/10.1007/s11266-020-00260-3
• van Wessel, M., Naz, F., & Sahoo, S. (2021). Complementarities in CSO Collaborations: How Working with Diversity Produces Advantages. Voluntas , 32 (4), 717–730. https://doi.org/10.1007/s11266-020-00227-4
• Vandepitte, E., Vandermoere, F., & Hustinx, L. (2019). Civil Anarchizing for the Common Good: Culturally Patterned Politics of Legitimacy in the Climate Justice Movement. Voluntas , 30 (2), 327–341. https://doi.org/10.1007/s11266-018-00073-5
• Vuković, D. (2018). The Quest for Government Accountability and Rule of Law: Conflicting Strategies of State and Civil Society in Cambodia and Serbia. Voluntas , 29 (3), 590–602. https://doi.org/10.1007/s11266-018-9984-z
• Waardenburg, M. (2021). Understanding the Microfoundations of Government–Civil Society Relations. Voluntas , 32 (3), 548–560. https://doi.org/10.1007/s11266-020-00221-w
• Wells, R., & Anasti, T. (2020). Hybrid Models for Social Change: Legitimacy Among Community-Based Nonprofit Organizations. Voluntas , 31 (6), 1134–1147. https://doi.org/10.1007/s11266-019-00126-3
• Williamson, A. K., Luke, B., & Furneaux, C. (2021). Ties That Bind: Public Foundations in Dyadic Partnerships. Voluntas , 32 (2), 234–246. https://doi.org/10.1007/s11266-020-00269-8
• Yanay-Ventura, G. (2019). “Nothing About Us Without Us” in Volunteerism Too: Volunteering Among People with Disabilities. Voluntas , 30 (1), 147–163. https://doi.org/10.1007/s11266-018-0026-7
• Yanay-Ventura, G., Issaq, L., & Sharabi, M. (2020). Civic Service and Social Class: The Case of Young Arab Women in Israel. Voluntas . https://doi.org/10.1007/s11266-020-00210-z
• Zhang, Y., Bradtke, M., & Halvey, M. (2020). Anxiety and Ambivalence: NGO–Activist Partnership in China’s Environmental Protests, 2007–2016. Voluntas , 31 (4), 779–792. https://doi.org/10.1007/s11266-020-00237-2
• Zihnioğlu, Ö. (2019). The Prospects of Civic Alliance: New Civic Activists Acting Together with Civil Society Organizations. Voluntas , 30 (2), 289–299. https://doi.org/10.1007/s11266-018-0032-9

No funding was received to assist with the preparation of this manuscript.

Declarations

The author declares that they have no conflict of interest.

1 While this approach may have obscured other methods that employ transcription, such as focus groups, the intention of the survey is to provide a snapshot illustration of transcription and qualitative methods rather than a systematic review.

2 Articles reviewed are listed in Appendix 1.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• Atia M, Herrold CE. Governing through patronage: The rise of NGOs and the fall of civil society in Palestine and Morocco. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations. 2018; 29 (5):1044–1054. doi: 10.1007/s11266-018-9953-6. [ CrossRef ] [ Google Scholar ]
• Blaxter, L., Hughes, C., & Tight, M. (2001). How to research (2nd ed.). Open University Press.
• Bokhove C, Downey C. Automated generation of “good enough” transcripts as a first step to transcription of audio-recorded data. Methodological Innovations. 2018; 11 (2):2059799118790743. doi: 10.1177/2059799118790743. [ CrossRef ] [ Google Scholar ]
• Braun V, Clarke V. Using thematic analysis in psychology. Qualitative Research in Psychology. 2006; 3 (2):77–101. doi: 10.1191/1478088706qp063oa. [ CrossRef ] [ Google Scholar ]
• Bryman, A. (2016). Social research methods (5th ed.). Oxford University Press.
• Bucholtz M. The politics of transcription. Journal of Pragmatics. 2000; 32 (10):1439–1465. doi: 10.1016/S0378-2166(99)00094-6. [ CrossRef ] [ Google Scholar ]
• Da Silva J. Producing ‘good enough’ automated transcripts securely: Extending Bokhove and Downey (2018) to address security concerns. Methodological Innovations. 2021; 14 (1):2059799120987766. doi: 10.1177/2059799120987766. [ CrossRef ] [ Google Scholar ]
• Davidson C. Transcription: Imperatives for qualitative research. International Journal of Qualitative Methods. 2009; 8 (2):35–52. doi: 10.1177/160940690900800206. [ CrossRef ] [ Google Scholar ]
• Dodds S, Hess AC. Adapting research methodology during COVID-19: Lessons for transformative service research. Journal of Service Management. 2020; 32 (2):203–217. doi: 10.1108/JOSM-05-2020-0153. [ CrossRef ] [ Google Scholar ]
• Duranti A. Transcripts, like shadows on a wall. Mind, Culture, and Activity. 2006; 13 (4):301–310. doi: 10.1207/s15327884mca1304_3. [ CrossRef ] [ Google Scholar ]
• Gibbs, G. R., Friese, S., & Mangabeira, W. C. (2002). View of the use of new technology in qualitative research. Orum Qualitative Sozialforschung/Forum: Qualitative Social Research , 3 (2). https://www.qualitative-research.net/index.php/fqs/article/view/847/1840
• Halcomb EJ, Davidson PM. Is verbatim transcription of interview data always necessary? Applied Nursing Research. 2006; 19 (1):38–42. doi: 10.1016/j.apnr.2005.06.001. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Igalla M, Edelenbos J, van Meerkerk I. Citizens in action, what do they accomplish? A systematic literature review of citizen initiatives, their main characteristics, outcomes, and factors. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations. 2019; 30 (5):1176–1194. doi: 10.1007/s11266-019-00129-0. [ CrossRef ] [ Google Scholar ]
• Jaffe A. Variability in transcription and the complexities of representation, authority and voice. Discourse Studies. 2007; 9 (6):831–836. doi: 10.1177/1461445607082584. [ CrossRef ] [ Google Scholar ]
• Kvale, S. (1996). Interviews: An introduction to qualitative research interviewing . Sage.
• Lapadat JC. Problematizing transcription: Purpose, paradigm and quality. International Journal of Social Research Methodology. 2000; 3 (3):203–219. doi: 10.1080/13645570050083698. [ CrossRef ] [ Google Scholar ]
• Laurett R, Ferreira JJ. Strategy in nonprofit organisations: A systematic literature review and agenda for future research. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations. 2018; 29 (5):881–897. doi: 10.1007/s11266-017-9933-2. [ CrossRef ] [ Google Scholar ]
• Loubere, N. (2017). Questioning transcription: The case for the systematic and reflexive interviewing and reporting (SRIR) method. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research , 18 (2), Article 2. 10.17169/fqs-18.2.2739
• Markle, D. T., West, R. E., & Rich, P. J. (2011). Beyond transcription: Technology, change, and refinement of method. Forum Qualitative Sozialforschung/Forum: Qualitative Social Research , 12 (3), Article 3. 10.17169/fqs-12.3.1564
• Matheson JL. The voice transcription technique: Use of voice recognition software to transcribe digital interview data in qualitative research. Qualitative Report. 2007; 12 (4):547–560. [ Google Scholar ]
• McCorkel JA, Myers K. What difference does difference make? Position and privilege in the field. Qualitative Sociology. 2003; 26 (2):199–231. doi: 10.1023/A:1022967012774. [ CrossRef ] [ Google Scholar ]
• Mero-Jaffe I. ‘Is that what I Said?’ Interview transcript approval by participants: An aspect of ethics in qualitative research. International Journal of Qualitative Methods. 2011; 10 (3):231–247. doi: 10.1177/160940691101000304. [ CrossRef ] [ Google Scholar ]
• Mishler, E. (2003). Representing discourse: The rhetoric of transcription. In N. Fielding (Ed.), Interviewing . Sage.
• Moravcsik, A. (2014). Transparency: The revolution in qualitative research. PS: Political Science & Politics , 47 (1), 48–53. 10.1017/S1049096513001789
• Oliver DG, Serovich JM, Mason TL. Constraints and opportunities with interview transcription: Towards reflection in qualitative research. Social Forces. 2005; 84 (2):1273–1289. doi: 10.1353/sof.2006.0023. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Packer MJ. The science of qualitative research. Cambridge University Press; 2017. [ Google Scholar ]
• Parameswaran UD, Ozawa-Kirk JL, Latendresse G. To live (code) or to not: A new method for coding in qualitative research. Qualitative Social Work. 2020; 19 (4):630–644. doi: 10.1177/1473325019840394. [ CrossRef ] [ Google Scholar ]
• Quintanilha M, Mayan MJ, Thompson J, Bell RC. Different approaches to cross-lingual focus groups: Lessons from a cross-cultural community-based participatory research project in the ENRICH study. International Journal of Qualitative Methods. 2015; 14 (5):1609406915621419. doi: 10.1177/1609406915621419. [ CrossRef ] [ Google Scholar ]
• Schwingel, A., Wiley, A., Teran-Garcia, M., McCaffrey, J., Gálvez, P., Hawn, R., Villegas, E., Coba, S., Vizcarra, M., Luty, G., Montez, R., & The Abriendo Caminos Promotora Project Group. (2017). More than help? Volunteerism in US Latino culture. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations , 28 (1), 162–183. 10.1007/s11266-016-9731-2
• Stelma JH, Cameron LJ. Intonation units in spoken interaction: Developing transcription skills. Text & Talk. 2007; 27 (3):361–393. doi: 10.1515/TEXT.2007.015. [ CrossRef ] [ Google Scholar ]
• Tilley SA. “Challenging” research practices: Turning a critical lens on the work of transcription. Qualitative Inquiry. 2003; 9 (5):750–773. doi: 10.1177/1077800403255296. [ CrossRef ] [ Google Scholar ]
• Tracy, S. J. (2019). Qualitative research methods: Collecting evidence, crafting analysis (2nd ed.). John Wiley & Sons.
• von Schnurbein G, Perez M, Gehringer T. Nonprofit comparative research: Recent agendas and future trends. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations. 2018; 29 (3):437–453. doi: 10.1007/s11266-017-9877-6. [ CrossRef ] [ Google Scholar ]
• Wellard S, McKenna L. Turning tapes into text: Issues surrounding the transcription of interviews. Contemporary Nurse. 2001; 11 (2–3):180–186. doi: 10.5172/conu.11.2-3.180. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yanay-Ventura, G., Issaq, L., & Sharabi, M. (2020). Civic service and social class: The case of young Arab women in Israel. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations . 10.1007/s11266-020-00210-z