part of a research report includes the transcriptions based on interviews

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 data 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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Article Contents

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

Month:	Total Views:
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January 2017	546
February 2017	3,448
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June 2017	357
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August 2017	359
September 2017	566
October 2017	725
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February 2018	4,323
March 2018	5,999
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June 2018	4,049
July 2018	3,921
August 2018	4,915
September 2018	4,080
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December 2018	3,997
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April 2019	6,753
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March 2020	2,853
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August 2020	1,957
September 2020	1,940
October 2020	2,479
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December 2020	2,069
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April 2021	3,294
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July 2021	2,605
August 2021	2,527
September 2021	2,709
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November 2021	3,475
December 2021	2,703
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February 2024	3,814
March 2024	5,149
April 2024	5,687
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June 2024	2,482
July 2024	2,100
August 2024	1,131

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Qualitative Data Analysis: Transcription

• Atlas.ti web
• R for text analysis
• Microsoft Excel & spreadsheets
• Other options
• Planning Qual Data Analysis
• Free Tools for QDA
• QDA with NVivo
• QDA with Atlas.ti
• QDA with MAXQDA
• PKM for QDA
• QDA with Quirkos
• Working Collaboratively
• Qualitative Methods Texts
• Transcription
• Data organization
• Example Publications

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 43 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 58+ languages including English, Spanish, Arabic, Mandarin, Japanese, Korean, and Hindi.
• Trint Trint is a paid transcription and analysis tool, with transcription available for 40+ 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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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.

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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

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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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DOI : https://doi.org/10.1007/s11266-021-00400-3

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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.

Aspect	Qualitative Interview	Quantitative Interview
Purpose	To understand people's experiences, ideas, and feelings on a deeper level.	To quantify information and generalise findings to a wider audience.
Nature of data collection	Textual, descriptive	Numerical, statistics
Data collection method	Open-ended questions and unstructured interviews	Close-ended questions and structured interviews
Sample size	Smaller since more focus is on depth of information	Larger since more focus is on statistics and generalisation
Approach	Exploratory	Conclusive and factual
Outcome	Detailed understanding of a particular problem or area of interest	Quantifiable insights
Flexibility	Increased flexibility as questions are adapted according to the situation	Decreased flexibility as questions are predetermined and close-ended

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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How to Analyze Interview Transcripts in Qualitative Research

How do you analyze qualitative interviews?

Inductive methods of analyzing interview transcripts, deductive approach to qualitative analysis, how do you transcribe a qualitative interview, collecting feedback for qualitative research, organize your research recordings, transcribe all the interviews and focus group recordings.

The 6 Main Steps to Qualitative Analysis of Interviews

1. read the transcripts, 2. annotate the transcripts, 3. conceptualize the data.

• Create a spreadsheet to easily compile your data.
• Then, use the columns to structure important variables of your data analysis using codes as tools for reference.
• Create a separate tab for the front of the document that contains a coding table. This glossary contains important codes used in the segmentation process. This will help you and others quickly identify what the codes are referring to.

5. Analyze the segments

6. write the results, transcription is essential to qualitative research analysis.

5 Benefits of Using Transcription in Research

• Consent preferences

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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Transcription in qualitative research: a comprehensive guide for ux researchers, theertha raj.

August 15, 2024

As a UX researcher, you're likely familiar with the importance of gathering qualitative data through interviews, focus groups, and observational studies. But what happens after you've collected all that valuable audio or video content? That's where transcription for qualitative research comes into play. 

In this article, we'll dive deep into the world of transcription, exploring its importance, types, and best practices for UX researchers.

What is transcription in qualitative research?

Transcription in qualitative research is the process of converting spoken words or recorded audio into written text. 

This crucial step allows researchers to analyze, code, and interpret the data collected during interviews, focus groups, or other qualitative research methods. 

This written format makes it easier to review, share, and analyze the data, ultimately leading to more informed design choices and improved user experiences.

What are the 4 types of transcription?

When it comes to transcription qualitative research, there are four main types that researchers should be aware of:

• Verbatim transcription: This type captures every utterance, including filler words, false starts, and non-verbal sounds. It's the most detailed form of transcription and is often used when analyzing speech patterns or conducting linguistic studies.
• Intelligent verbatim transcription: Also known as "clean verbatim," this type removes filler words and false starts while maintaining the essence of the conversation. It's more readable than strict verbatim transcription and is commonly used in qualitative research.
• This type focuses on capturing the main ideas and content of the conversation while cleaning up grammar and removing unnecessary repetitions. It's useful when the primary goal is to understand the content rather than analyze speech patterns.
• This specialized type uses phonetic symbols to represent the sounds of speech. It's primarily used in linguistic research and is less common in UX research contexts.

What type of transcription is used in qualitative research?

In qualitative research, intelligent verbatim transcription is often the preferred choice . This type of transcription strikes a balance between capturing the essence of the conversation and maintaining readability. It preserves the interviewee's words and intent while removing unnecessary filler words and false starts that can distract from the main content.

This type of transcription in qualitative research makes it easier to identify key themes, pain points, and user needs that can inform design decisions.

What type of transcription is used in thematic analysis?

Thematic analysis , a common method used in qualitative research to identify patterns and themes within data, typically relies on intelligent verbatim transcription . It provides enough detail to capture the nuances of participants' responses while maintaining readability, making it ideal for identifying recurring themes and concepts.

What is the average price for transcription services?

The cost of transcription services for qualitative research can vary widely depending on factors such as turnaround time, audio quality, and the level of detail required. On average, professional qualitative transcription services may charge anywhere from $1 to $3 per audio minute for standard turnaround times (typically 3-5 business days).

For UX researchers working on time-sensitive projects, expedited services are available but often come at a premium, with prices potentially doubling or tripling. 

It's worth noting that some of the best transcription services for qualitative research offer discounts for bulk orders or ongoing projects, which can be beneficial for researchers conducting multiple interviews or focus groups.

When considering the cost, it's important to weigh the value of professional qualitative research transcription services against the time and effort required to transcribe in-house. While DIY transcription might seem cost-effective, it can be time-consuming and may not yield the same level of accuracy as professional services.

How to write a transcript for qualitative research

Writing a transcript for qualitative research involves more than just typing out what you hear. 

Here are some key steps to ensure your transcripts are accurate, useful, and ready for analysis:

• Prepare your tools: Choose reliable transcription software for qualitative research or a word processing program. Ensure you have a good quality audio playback device and headphones for clear listening.
• Listen to the entire recording: Before you start transcribing, listen to the entire recording to familiarize yourself with the content, speakers, and any potential audio issues.
• Create a template: Set up a consistent format for your transcripts, including headers for participant information, date, time, and any other relevant details.
• Transcribe the content: Begin typing out the conversation, following the intelligent verbatim method unless your research requires a different approach. Include speaker labels to differentiate between the interviewer and participant(s).
• Add time stamps: Regularly insert timestamps throughout the transcript. This helps in referencing specific parts of the conversation later and syncing the transcript with the original audio if needed.
• Note non-verbal cues: When relevant, include descriptions of significant non-verbal communication or environmental factors in square brackets, e.g., [laughs], [long pause], [background noise].
• Review and edit: Once you've completed the initial transcription, review it while listening to the audio again. Correct any errors and ensure the transcript accurately represents the conversation.
• Format for readability: Use paragraphs to separate distinct topics or questions. While it's best to keep bullet points and lists to a minimum, you can use them sparingly to highlight key points if necessary.

How do you transcribe audio data in qualitative research?

Transcribing audio data in qualitative research is a process that requires careful consideration of several factors. 

What are the aims of the research project?

Before you begin transcribing, it's crucial to clearly understand the goals of your research project . Are you looking to gather specific user feedback on a product feature? Or are you conducting a broader study on user behavior and preferences? The aims of your project will influence the level of detail and focus required in your transcriptions.

For example, if you're researching user reactions to a new app interface, you might pay special attention to comments about the layout, navigation, and visual elements. On the other hand, if you're exploring user motivations and decision-making processes, you might focus more on capturing the reasoning and emotions behind their responses.

What level of detail is required?

The level of detail in your transcriptions should align with your research goals and analysis methods. For most UX research projects, intelligent verbatim transcription provides an ideal balance of detail and readability. However, there may be instances where more or less detail is necessary.

If you're conducting a usability test and need to capture specific user actions along with their verbal feedback, you might include more detailed notes about their interactions with the product. Conversely, if you're more interested in high-level themes and general user sentiment, a slightly less detailed transcription might suffice.

Who should do the transcribing?

Deciding who should handle the transcription is an important consideration. You have several options:

• DIY transcription: As the researcher, you might choose to transcribe the audio yourself. This can be time-consuming but allows you to immerse yourself in the data and potentially identify themes early on.
• Team member transcription: Assigning transcription tasks to other team members can distribute the workload and provide multiple perspectives on the data.
• Professional transcription services: Opting for qualitative research transcription services can save time and ensure high-quality, accurate transcripts. Many services specialize in research transcription and understand the specific needs of qualitative researchers.
• Transcription software: Using qualitative research transcription software can speed up the process, especially for clear audio recordings. However, it's important to review and edit machine-generated transcripts for accuracy.

What contextual detail is necessary to interpret data?

Context is crucial in qualitative research, and your transcripts should include relevant contextual details that aid in interpreting the data. This might include:

• Background information about the participant (e.g., age range, profession, relevant experience)
• The setting of the interview or focus group
• Any visual aids or prototypes used during the session
• Significant non-verbal cues or reactions

How should data be represented?

When it comes to representing data in your transcripts, clarity and consistency are key. Use a clear, consistent format for speaker labels (e.g., "Interviewer:", "Participant 1:"). Include time stamps at regular intervals or at the beginning of new topics. Consider using bold or italics to highlight key quotes or themes, but use this sparingly to maintain readability.

What is an example of a transcription? 

Here’s an example of what an AI-generated transcript looks like, with time-stamps and Speaker labels. The transcript also features text highlighted in green for positive responses, and blue for questions.

Best Automated Transcription Services for Qualitative Research

When conducting qualitative research, choosing the right transcription service is crucial for efficient data analysis. Here's a comparison of some popular transcription tools used in qualitative research:

NVivo is primarily a qualitative data analysis tool that offers transcription services as part of its feature set.

Price: NVivo's pricing is on the higher end, with users required to purchase blocks of transcription time (e.g., €80 for 10 hours).

• NVivo allows uploading of various audio and video file formats to its online platform.
• Allows you to edit and make changes, tag speakers, and ensure proper formatting 
• Offers encrypted and secure storage, adhering to HIPAA standards and GDPR compliance

Languages Supported: NVivo supports transcription in 42 languages.

Time taken: Specific time is not mentioned, but it's noted to be generally slower compared to other tools.

Accuracy: NVivo's transcription accuracy is lower compared to competitors, especially in noisy environments and with accents.

How much does NVivo transcription cost?

NVivo transcription costs around €80 for 10 hours of transcription time. The pricing structure is based on purchasing blocks of time rather than a subscription model.

Is NVivo transcription free?

No, NVivo transcription is not free. It requires purchasing transcription time.

Is NVivo good at transcription?

NVivo's transcription capabilities are considered less accurate compared to some competitors, especially in challenging audio conditions or with accented speech.

Is NVivo used for qualitative research?

Yes, NVivo is widely used for qualitative research, primarily as a data analysis tool. Its transcription feature is an additional service within this broader qualitative research platform.

Dovetail is a comprehensive research repository that includes transcription, coding, and data analysis features.

Price: Dovetail offers a free plan with 1 project per month, while paid plans start at $29 per user per month.

• Does automated transcription of video and audio recordings
• Does sentiment analysis of transcripts with highlights for positive and negative responses
• Also offers built-in analysis tools for coding and tagging of transcripts

Languages Supported: Supports 41 languages, including Japanese, Finnish, Hindi, and Malay.

‍ Time taken: Transcription is completed within minutes.

Accuracy: While generally considered accurate, specific accuracy metrics are not provided.

Looppanel is an AI-powered research repository tool that can do extremely accurate interview transcription for UX research, among other cool features. It’s special compared to other transcription services on this list, as it also lets you record your calls directly, and receive high-quality transcripts within mere minutes. 

Price: Paid plans start at $30 per month, with a 15-day free trial available.

‍ Features:

• Collaboratively take notes with colleagues during your user interviews 
• Save key time-stamps of important quotes as they come up
• Sentiment analysis of transcripts with highlights for questions, positive and negative responses
• Generate AI-powered notes from your transcript for you, with a dedicated analysis space where you can see all your data by question or tags
• Do Google-like search within your research repository to find any quote or data point you need, in minutes.

Languages Supported: Supports 8 languages, including English, Spanish, French, German, Italian, Portuguese, Dutch, and Hindi.

Time taken: Provides near-instant transcripts for recorded calls.

Accuracy: Looppanel boasts over 90% accuracy in transcription.

MAXQDA is a research analysis tool that’s more complex than the others, but is ideal for academics and scientists who need deep, detailed analysis. MAXQDA offers transcription services alongside qualitative analysis tools.

Price: Approximately $20 for 2 hours of transcription, with varying plans based on industry and use case.

• MAXQDA offers a customizable dictionary for accuracy
• Does automatic speaker detection, and timestamps. 
• It's GDPR-compliant 
• Doesn't require a subscription or MAXQDA license for transcription services.

Languages Supported: Supports over 48 languages.

Time taken: Transcription is completed within minutes.

‍ Accuracy: Claims over 90% accuracy

Otter.ai is a transcription tool that doesn’t offer any in-app analysis features, unlike the other tools on this list.

Price: Offers a free tier with 300 minutes of transcription; paid plans start at $8.33 per month.

‍ Features: 

• Otter.ai can record and transcribe meetings on various platforms in real-time, capture slides, and generate summaries
• It also allows for YouTube video transcription
• Exports to various file formats.

Languages Supported: Only English

Time taken: Within minutes

Accuracy: Generally high accuracy, especially in structured meeting environments.

Challenges in transcription

Transcription in qualitative research can face several challenges:

• Audio quality issues
• Multiple speakers or overlapping speech
• Accents or dialects
• Background noise
• Jargon or specialized terminology
• Time-consuming nature of manual transcription
• Maintaining consistency across multiple transcripts

Best Practices for Transcription in Action

How to do it:

• Use high-quality recording equipment
• Conduct interviews in quiet environments when possible
• Consider using a foot pedal for manual transcription to improve efficiency
• Use transcription software or services for larger projects

What to include:

Speaker identification, time stamps, non-verbal cues (laughter, pauses, sighs), contextual information and consistent formatting

How to record for optimal use in your study:

• Test your recording equipment before the interview
• Use external microphones for better audio quality
• Inform participants about the recording
• Take brief notes during the interview to supplement the recording
• Back up your recordings immediately after the interview

‍ What is the difference between transcription and translation in qualitative research?

Transcription involves converting spoken language into written text in the same language, while translation involves converting text from one language to another. In qualitative research, transcription is typically done first, followed by translation if the research is conducted in a language different from the one used for analysis.

What are the different types of transcription process?

The main types of transcription processes are:

1. Verbatim transcription (including all utterances and sounds) 2. Intelligent verbatim (removing fillers and false starts) 3. Edited transcription (cleaning up grammar and removing repetitions) 4. Phonetic transcription (using phonetic symbols to represent sounds)

What transcription services for qualitative data?

Transcription services for qualitative data include automated tools like NVivo, Dovetail, Looppanel, MAXQDA, and Otter.ai, as well as human transcription services. The choice depends on factors such as budget, accuracy requirements, and the complexity of the audio data.

Can I use NVivo for transcription?

Yes, you can use NVivo for transcription. However, it's important to note that while NVivo offers transcription services, it may not be as accurate or cost-effective as some alternatives, especially for large-scale projects or challenging audio conditions.

What is the alternative to NVivo transcription?

Alternatives to NVivo transcription include Dovetail, Looppanel, MAXQDA, and Otter.ai, each offering different features and pricing structures.

Is NVivo transcription worth it?

The value of NVivo transcription depends on your specific needs. While it integrates well with NVivo's analysis tools, its lower accuracy and higher price point may make it less appealing for some researchers.

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How to Transcribe Interviews for UX Research

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How to Analyze Interview Transcripts in Qualitative Research

Rev › Blog › Transcription Blog › How to Analyze Interview Transcripts in Qualitative Research

Studies take time, accuracy, and a drive to provide excellent information, and qualitative research is a critical part of any successful study. You may be wondering how qualitative data adds to a paper or report, given that it’s not the hard “science” we often see highlighted the most often.

How Do You Analyze Qualitative Interviews?

There are two main approaches to qualitative analysis: inductive and deductive . What’s more, there are two types of inductive qualitative analysis to choose from. These are called thematic content analysis and narrative analysis, both of which call for an unstructured approach to research.

Inductive Methods of Analyzing Interview Transcripts

A thematic content analysis begins with weeding out biases and establishing your overarching impressions of the data. Rather than approaching your data with a predetermined framework, identify common themes as you search the materials organically. Your goal is to find common patterns across the data set.

A narrative analysis involves making sense of your interview respondents’ individual stories. Use this type of qualitative data analysis to highlight important aspects of their stories that will best resonate with your readers. And, highlight critical points you have found in other areas of your research.

Deductive Approach to Qualitative Analysis

Deductive analysis , on the other hand, requires a structured or predetermined approach. In this case, the researcher will build categories in advance of their analysis. Then, they’ll map connections in the data to those specific categories.

Each of these qualitative analysis methods lends its benefits to the research effort. Inductive analyses will produce more nuanced findings. Meanwhile, deductive analyses allow the researcher to point to key themes essential to their research.

Successful qualitative research hinges on the accuracy of your data. This can be harder to achieve than with quantitative research. It’s easy to lose important facts and meaning as you transition qualitative data from the source to your published content. This makes transcription a vital tool in maintaining integrity and relaying information in an unbiased way that’s useful for readers and adds appropriate context to the journal or study.

How to Transcribe a Qualitative Interview

Accurate transcription begins early in the interview process, even before you start interviewing. Here are the steps to transcribing a qualitative interview.

1. Collect Feedback for Qualitative Research

There are dozens of ways to gather qualitative data. Recording and accurately transcribing interviews is among the best methods to avoid inaccuracies and data loss, and researchers should consider this approach over simply taking notes firsthand.

Make sure you have a reliable way to record, whether the interview takes place in person, over the phone, or as part of a video call. Depending on the interview method, you may record a video or an audio-only format. Here are some tips depending on where the interview takes place:

• These apps can also be used for over-the-phone interviews.
• For video interviews , we recommend taking advantage of one of our transcription integrations , such as Zoom. Rev also has an API available for those who want to streamline their workflow even further by integrating Rev directly into their processes and platforms.

2. Organize Your Research Recordings

You should ensure that your audio or video files are easy to save, compile, and share. To do this, be sure to adopt easy-to-remember naming conventions as well to ensure they stay organized. An example of a naming convention that is simple to remember and recreate includes “Date.LastNameofSource.Topic”.

3. Transcribe All the Interviews and Focus Group Recordings

The next critical step is transcription. Done manually, this is a long and tedious process that can add hours, days, or even months to your report-writing process. There are dozens of pitfalls when performing transcriptions manually as well, as it can be hard to pick up words spoken in a heavy dialect or quiet tone. You also want to avoid having to transcribe all the “umms” and “ems” that occur when a source is speaking naturally.

Rev provides a variety of transcription services that take the tedium and guesswork out of the research process. You can choose to edit out all of the “umms,” while ensuring that heavy accents or muffled voices are picked up by the recording service.

You can order transcripts from Rev with both audio and video recordings. Once you’ve received your professional transcripts from Rev, you can begin your qualitative analysis.

The 6 Steps of Qualitative Interview Data Analysis

Among qualitative interview data analysis methods, thematic content analysis is perhaps the most common and effective method. It can also be one of the most trustworthy , increasing the traceability and verification of an analysis when done correctly. The following are the six main steps of a successful thematic analysis of your transcripts.

1. Read the Transcripts

By now, you will have accessed your transcript files as digital files in the cloud or have downloaded them to your computer for offline viewing. Start by browsing through your transcripts and making notes of your first impressions. You will be able to identify common themes. This will help you with your final summation of the data.

Next, read through each transcript carefully. Evidence of themes will become stronger, helping you to hone in on important insights.

You must identify bias during this step as well. Biases can appear in the data, among the interviewees, and even within your objectives and methodologies. According to SAGE Publishing , researchers should “acknowledge preconceived notions and actively work to neutralize them” at this early step.

2. Annotate the Transcripts

Annotation is the process of labeling relevant words, phrases, sentences, or sections with codes. These codes help identify important qualitative data types and patterns. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant.  Annotations will help you organize your data for dissemination .

Be generous with your annotations—don’t hold back. You will have an opportunity to eliminate or consolidate them later. It’s best to do more here, so you don’t have to come back to find more opportunities later.

3. Conceptualize the Data

Conceptualizing qualitative data is the process of aligning data with critical themes you will use in your published content. You will have identified many of these themes during your initial review of the transcripts.

To conceptualize,  create categories and subcategories  by grouping the codes you created during annotation. You may eliminate or combine certain codes rather than using all the codes you created. Keep only the codes you deem relevant to your analysis.

4. Segment the Data

Segmentation is the process of positioning and  connecting your categories . This allows you to establish the bulk of your data cohesively. Start by labeling your categories and then describe the connections between them.

You can use these descriptions to improve your final published content.

• Create a spreadsheet  to easily compile your data.
• Then, use the columns to structure important variables of your data analysis using codes as tools for reference.
• Create a separate tab for the front of the document that contains a coding table. This glossary contains important codes used in the segmentation process. This will help you and others quickly identify what the codes are referring to.

5. Analyze the Segments

You’re now ready to take a  deep dive into your data segments . Start by determining if there is a hierarchy among your categories. Determine if one is more important than the other, or draw a figure to summarize the results. At this stage, you may also want to align qualitative data with any quantitative data you collected.

6. Write the Results

Your analysis of the content is complete—you’re ready to transition your findings into the real body of your content. Use your insights to build and verify theories, answer key questions in your field, and back aims and objectives. Describe your categories and how they are connected using a neutral, objective voice.

Although you will pull heavily from your own research, be sure to publish content in the context of your field. Interpret your results in light of relevant studies, theories, and concepts related to your study.

Why Use Interviews for Qualitative Data

Unlike quantitative data, which is certainly important, a qualitative analysis adds color to academic and business reports. It offers perspective and can make a report more readable, add context, and inspire thoughtful discussion beyond the report.

As we’ve observed, transcribing qualitative interviews is crucial to getting less measurable data from direct sources. They allow researchers to provide relatable stories and perspectives and even quote important contributors directly. Lots of qualitative data from interviews enables authors to avoid embellishment and maintain the integrity of their content as well.

So, how do you conduct interview data analysis on qualitative data to pull key insights and strengthen your reports? Transcribing interviews is one of the most useful tools available for this task.

As a researcher, you need to make the most of recorded interviews . Interview transcripts allow you to use the best qualitative analysis methods. Plus, you can focus only on tasks that add value to your research effort.

Transcription is Essential to Qualitative Research Analysis

Qualitative data is often elusive to researchers. Transcripts allow you to capture original, nuanced responses from your respondents. You get their response naturally using their own words—not a summarized version in your notes.

You can also go back to the original transcript at any time to see what was said as you gain new context. The editable digital transcript files are incredibly easy to work with, saving you time and giving you speaker tags, time marks, and other tools to ensure you can find what you need within a transcript quickly.

When creating a report, accuracy matters, but efficiency matters, as well. Rev offers a seamless way of doing the transcription for you, saving you time and allowing you to focus on high-quality work instead. Consider Rev as your transcription service provider for qualitative research analysis — try Rev’s AI or Human Transcription services today.

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Methodology

• Transcribing an Interview | 5 Steps & Transcription Software

Transcribing an Interview | 5 Steps & Transcription Software

Published on April 25, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

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, looking out for any mistakes that can lead to information bias or omitted variable bias

Transcription software comparison

Table of contents

Transcription methods, altering the transcript, example transcript, analyzing interview transcripts, transcription software, other interesting articles.

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 “uhm”, “yeah”, “you know” etc. 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 summarized 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.

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 / missing audio with ellipses: “I showed him … would be beneficial for profitability”
• Emphasizing 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 anonymized)
• 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 analyzing. There are several techniques for doing this—coding and categorizing 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 research 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.

Transcription software comparison 2019

	Hourly rate (pay as you go)	Hourly rate (monthly plan)	Free trial?
	$13.40	$11.18	30 minutes
	–	$13.33	30 minutes
	$6	–	1 minute

Happy Scribe

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

• 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 capitalization 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 annual license fee

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

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

• Implicit bias
• Cognitive bias
• Conformity bias
• Hawthorne effect
• Availability heuristic
• Attrition bias
• Social desirability bias

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Streefkerk, R. (2023, June 22). Transcribing an Interview | 5 Steps & Transcription Software. Scribbr. Retrieved August 19, 2024, from https://www.scribbr.com/methodology/transcribe-interview/

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How To Use Themes And Tags When Transcribing Qualitative Research Interviews

Themes and tags are pivotal tools in qualitative research, helping organize and extract vital insights from interview data.

Understanding the importance of themes and tags in qualitative research interviews is fundamental for researchers seeking to extract valuable insights from their data. In essence, subjects and tags organize and categorize the vast amount of verbal or textual data gathered during interviews. They are the tools that allow researchers to break down complex narratives into manageable chunks of information that can be analyzed and interpreted.

Themes are essentially the broad topics that recur throughout the interview. They represent the main ideas or subjects that the interviewee discusses. For instance, in an interview about workplace satisfaction, subjects might include work-life balance, job security, or company culture. By identifying these subjects, researchers can get a sense of what issues are most important to the interviewee and how these issues might be interrelated.

Tags, on the other hand, are specific keywords or phrases that are used to label these themes or subjects. Tags can be single words or short phrases that capture the essence of the theme. They are utilized to identify quickly and group similar subjects together. For example, in a study about health and wellness, tags could include words like "exercise," "diet," and "stress." These tags make retrieving and analyzing all the data related to a particular theme easier.

Moreover, subjects and tags add a level of objectivity to qualitative research. By applying the same set of subjects and tags to all interviews in a study, researchers can ensure a more consistent and systematic approach to data analysis. This procedure also helps to eliminate potential biases that may occur when interpreting the data.

Finally, subjects and tags can significantly facilitate the presentation of research findings. They provide a clear and concise way to communicate complex data and results to a broader audience. By highlighting the most common subjects and utilizing tags to illustrate the frequency and spread of these subjects, researchers can paint a clear picture of their findings, making them more understandable and impactful.

A step-by-step guide to transcribing qualitative research interviews

Transcribing qualitative research interviews can seem daunting, but it is a necessary stage in the research procedure to ensure accurate, reliable, and comprehensive data. Here, we present an easy guide to help you understand how to effectively transcribe these interviews, utilizing subjects and tags for efficient organization and analysis.

1. Listen to the Interview

Begin by listening to the interview recording in its entirety. This will help you familiarize yourself with the content and context of the dialogue. Take note of any recurring ideas or subjects emerging from the conversation.

2. Transcribe Verbatim

Start transcribing the interview word for word. This step can be time-consuming, but it is crucial as it ensures every detail is preserved in translation. Transcription software aids in this procedure. Happy Scribe, for instance, enables the transcription of audio and video files into text documents on one website for professionals across different industries. Users can save proper nouns, acronyms, and other terms in a personalized vocabulary utilizing the site for later usage.

3. Identify Themes and Tags

Once the transcription is complete, read through it while actively identifying potential themes and tags. Subjects are broad categories that represent the main ideas discussed in the interview, while tags are specific keywords or phrases that highlight the details within these subjects. For example, in an interview about dietary habits, "nutrition" might be a theme, while "organic food" or "fast food" could be tagged.

4. Apply Tags and Themes

Review your transcript and apply the tags and subjects you've identified to the relevant sections. Some transcription software allows you to do this within the platform, but you can also do this manually utilizing color codes or symbols.

Happy Scribe contains a punctuation feature that adds commas, exclamation points, full stops, commas, interrogation marks, and exclamation points automatically throughout the text.

5. Review and Refine

After tagging and categorizing the data, review your work to ensure that the subjects and tags accurately represent the content of the interview. Refining or even adding new tags and subjects may be necessary as you review the transcript more closely.

6. Analyze and Interpret

Now, you can begin analyzing and interpreting the data with a neatly organized and categorized transcript. The subjects and tags you've identified will guide your analysis and help you draw meaningful conclusions from your qualitative research interview.

Remember, transcribing qualitative research interviews aims to capture the richness and complexity of the dialogue in a way that allows for thorough analysis. Utilizing subjects and tags, you break down the data into digestible pieces, making the analysis more manageable and effective.

The art of identifying and applying themes in transcription

Identifying and applying subjects while transcribing qualitative research interviews is indeed an art. It requires a keen analytical eye and a deep understanding of the research subject matter. This procedure can significantly enhance the quality of your data analysis, as themes help to establish meaningful patterns and connections within your data.

Acquaint yourself well with the interviews

The first move towards identifying subjects is to familiarize yourself with the content of the interviews thoroughly. This means you must listen to the recordings and read the transcriptions several times. During this initial review, it's vital to make note of any recurrent ideas, words, phrases, or concepts that stand out. These repetitive elements often signal the existence of potential subjects.

Apply subjects to your transcriptions

Once potential subjects have been identified, the next stage is to apply them to your transcriptions. This typically involves coding, a procedure through which your transcriptions are labeled with the subjects they represent. For instance, if a participant frequently talks about 'work-life balance', you can create a theme with this label and apply it to every mention of this concept in the transcription. This way, when you later want to analyze all the discussions around 'work-life balance,' you can easily locate them by the theme tag.

Understand the iterative parts

Remember that identifying and applying subjects is iterative. As you proceed with your transcription and coding, your understanding of the data will deepen, and new subjects may emerge while existing ones may need refining. It's essential to remain flexible and open-minded throughout this procedure and be bold in revising and refining your topics as required.

Ultimately, the art of identifying and applying topics in transcription is about more than just organization; it's about making sense of your data. By carefully identifying topics and applying them to your transcriptions, you can uncover the deeper meanings in your interviews and achieve a more comprehensive understanding of your research subject.

How to effectively use tags in transcribing interviews

Tags play a crucial role in transcribing qualitative research interviews. You assign keywords or labels to specific portions of your interview data, making it easier to categorize, search, and analyze. Utilizing tags effectively can simplify sifting through hours of interviews and can help you quickly identify recurring patterns in your data.

Practice conscious listening

The procedure begins by actively listening to the interview as you transcribe. This will enable you to pick up on key ideas, phrases, or topics that emerge during the conversation. Each time you come across a significant point, assign a tag that encapsulates the essence of the information. This could be as simple as "budget" for discussions about finances or "customer satisfaction" for parts of the interview that touch on client feedback.

Keep tags clear and consistent

When choosing tags, it's crucial to keep them clear and consistent to avoid any confusion later on. If you start tagging segments about budgeting as "finance" halfway through your transcriptions, it might lead to missed data when looking for budget-related information.

Make your tags concise and descriptive

Another best practice is to keep your tags concise yet descriptive. Lengthy tags can clutter your transcription and may take longer to scan through. On the other hand, tags that are too short or vague may need to provide more context for you to understand what they refer to later on.

Manage your data well

Remember that tagging aims to make your data more manageable and your analysis more efficient. Therefore, the tags should reflect the most relevant categories to your research. If certain tags are rarely utilized, consider reconsidering their relevance and merging them with other, more frequently utilized ones.

Use a tagging-friendly software

Lastly, utilize transcription software that supports tagging. These tools can help you automate the tagging procedure, saving time and ensuring consistency across your transcriptions. They also usually come with features that allow you to filter or search your transcriptions based on your tags, further enhancing your data analysis.

Happy Scribe's tagging system offers a simple and effective way to arrange, classify, and manage your transcription files. You can improve the effectiveness of your process and find files quickly based on certain requirements by adding custom tags and taking advantage of the different utilize cases. Utilize this useful tool to get a more convenient transcription experience.

In conclusion, tagging is an essential part of transcribing qualitative research interviews that, when utilized effectively, can significantly enhance your data analysis process. It allows you to capture key subjects, categorize your data, and navigate your transcriptions with ease.

Common mistakes to avoid when using themes and tags in transcription

In your quest to effectively utilize subjects and tags when transcribing qualitative research interviews, it’s equally crucial to be aware of common mistakes that can undermine your efforts. Here are some you should acquaint yourself with:

Over-tagging and under-tagging

One such mistake is over-tagging or under-tagging. Over-tagging occurs when you assign too many tags to a single piece of data, making your transcription cluttered and confusing. Conversely, under-tagging leaves crucial data points unlabelled, which can lead to the loss of valuable information. Striking a balance is key here.

Using unclear subjects

Another standard error is utilizing overly broad or vague subjects. Your subjects should be specific enough to capture the essence of the data points they represent. Broad subjects may lead to confusion and make the analysis more difficult. On the other hand, avoid utilizing very narrow subjects that apply to very few data points. Your subjects should be comprehensive and representative of your data.

Inconsistency

Inconsistency in tagging and theming is another pitfall to avoid. Your analysis will be skewed if you’re inconsistent in assigning subjects and tags. For instance, if you tag a response as "customer dissatisfaction" in one instance and "negative customer feedback" in another, although they refer to the same concept, your analysis could become muddled. Utilize consistent subjects and tags throughout your transcription to ensure accurate analysis.

Ignoring context

Lastly, pay attention to the context. When assigning subjects and tags, it's easy to focus solely on the literal meaning of the words and forget the context in which they were said. However, context can drastically change the meaning of a statement. Always consider the context when assigning subjects and tags to ensure that the real essence of the response is captured.

In conclusion, while utilizing subjects and tags can significantly enhance the transcription process, it’s important to avoid these common mistakes. This will help ensure that your transcriptions are accurate, meaningful, and, ultimately, valuable in driving your research objectives.

Maximizing the value of your transcribed data: tips and best practices

Transcribing qualitative research interviews can be laborious, but with the right approach, you can enhance its value significantly. The first step towards maximizing the value of your transcribed data is to be methodical during the transcription. A well-structured interview transcription makes the analysis more straightforward and effective.

Use tags and subjects

Subjects and tags in your transcriptions can significantly increase their value. Subjects are a way to categorize your data based on the standard topics in your interviews. These could relate directly to the research question or emerge naturally from the interview conversation. Subjects provide a lens to understand and interpret the responses. On the other hand, tags mark specific instances within the subjects. They are like signposts that allow you to quickly navigate through the vast amounts of transcribed data.

Stay consistent

When utilizing subjects and tags, it's essential to remain consistent. Determine your subjects and tags before you start transcribing, and stick to them throughout. However, you should also allow for flexibility as new subjects emerge during transcription. If a new theme arises, feel free to add it to your list, but ensure that you retroactively apply it to previous transcripts for consistency.

Use experienced transcription software

Another best practice is to utilize professional transcription software. These tools often come with features that allow you to add tags and subjects easily. Some even have automatic tagging features, saving you a lot of time.

For instance, Happy Scribe has many tools that let visitors or collaborators mark files as "Complete" or "Reviewed" when they review. They can immediately draw attention to files that need it by labeling them with tags like "Urgent" or "High Priority."

Go through your transcribed data

Lastly, always review and refine your transcribed data. A second or third review could reveal missed subjects or tags or help you refine existing ones to suit your data better. Remember, the goal is to extract as much value as possible from your interviews, and this can only be achieved if you are thorough and meticulous in your approach.

In conclusion, subjects and tags are potent tools in qualitative research. When used correctly, they can transform your transcriptions from text into rich, valuable data that can provide new insights and drive your research forward.

Crave the best themes during qualitative research transcription. Happy Scribe is the most reliable

At Happy Scribe, we offer all your transcription needs for qualitative research interviews. Our all-in-one agency offers top-notch services for all types of transcription that can seamlessly integrate with a wide range of applications, including Zapier, YouTube, etc. Happy Scribe provides you with the most confidential service, which means your files are safe and cannot be accessed by third parties.

With features such as tagging, you can create tags depending on specific categories or themes, such as Content,' 'Education,' or 'Interviews,' to organize and look for files depending on content types seamlessly. These tags can also track the several stages of your transcription process.

Regardless of the format of your files, you will enjoy multiple exports, such as text, Doc, and more. One fantastic thing about choosing Happy Scribe is that, unlike other transcription agencies, you won't pay outrageous expenses. Our experts provide you with real-time transcription for your files quickly.

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Should I include interviews transcriptions in my thesis?

In my PhD, I ran an experiment falling within the grounded theory framework, based on interviews I conducted. I transcripted them, tagged them, etc. This "enhanced transcription" is about 130 pages long (with pretty narrow margins). I am now writing my thesis and I wonder if I should include these transcription in appendix of my PhD thesis .

My first guess would be yes, in order to enable contextualization when I quote snippets of theses interviews, but also for "open science" purposes (being honest about what I've done, archival purpose, reproducible research, etc.).

However, thesis in my field are usually ca. 150 pages long. Addind these transcriptions (i.e. 130 pages) would add a significant amount a pages with a low information density. A solution could be to display these transcripts in a very small font, so that they are only 50 pages long, but I don't think it's a good solution.

By the way, my supervisors don't have opinion about it, since using grounded theory is rather uncommon in my field - hence no "jurisprudence".

• At the time of the interviews, did you discuss with interviewees how the material might be used? There are some major potential issues here with use of personal information, though hopefully you have already considered this aspect! –  user2390246 Commented Nov 8, 2016 at 11:20
• 1 I've got their consent for audio-recording the interviews in order to transcribe it later. I was considering anonymizing any personal information (name, title of the project they worked on, and any hints that might make them recognizable), and asking again for formal authorization for publishing transcripts in their final form (i.e. sending them the file and asking " is it ok for you to publish it in that form? "). –  ebosi Commented Nov 8, 2016 at 11:29
• Despite the authorization you have currently, you probably need to go through IRB to get permission to change the way you are dealing with any data, not just getting consent again from the participants. IRB might also have opinions about your raw or transcribed data, and whether publication of material in that form is ever acceptable. –  Bryan Krause ♦ Commented Nov 8, 2016 at 23:03

Quite honestly I doubt that anybody would go and read that appendix. What I would do is that once the thesis is approved and put in the online repository of the university, you could add this appendix just as an online document and refer to it in the main thesis. That way if anybody wants to access it they can.

• I do agree that it's unlikely someone would go through these interviews! If I upload it online, how should I refer to it in my thesis? (i.e. when I quote a sentence, I'd like to say "Lorem ipsum dolor sit amet" (cf. Project X, Participant Y, at time mm:ss) . However I feel a bit dishonest if thesis reporters cannot access transcript at the time of their report... –  ebosi Commented Nov 8, 2016 at 9:24
• @ebo you can still add an online file also when you submit the thesis, in the main text you will give a refence to the section of the appendix. If they want they follow the link you provide, what's the issue? –  Herman Toothrot Commented Nov 8, 2016 at 13:16

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“The interviews were transcribed”, but how? Reflections on management research

RAUSP Management Journal

ISSN : 2531-0488

Article publication date: 30 September 2019

Issue publication date: 9 December 2019

In qualitative research, it is recurrent to conduct data collection through interviews, which must be first transcribed for the data to be analyzed. Although there is a relationship between the stages of the interview and the data analysis, the link between them (i.e. the transcription) seems to be a neglected methodological procedure. This occurs because, in papers, it is generally reported that “the interviews were transcribed”, without any details about the transcriptions conduction. From this methodological gap, this paper aims to discuss the relevance of detailing the methodological procedures adopted in the transcription in research reports in the management field.

Design/methodology/approach

This paper takes the form of a methodological essay.

The discussion focuses on the concepts of naturalized and denaturalized transcription, the relevance of adopting transcription norms and the need for reflexivity in conducting transcriptions – elements that must be explained in research reports to improve the methodological quality.

Practical implications

This paper explores methodological details that management students and researchers can adopt when performing transcriptions. Consequently, journal editors and reviewers will have more subsidies on the methodological quality employed in researches, which contributes to a better evaluation process.

Originality/value

This study demonstrates the relevance of a neglected methodological technique – transcription, which needs to be detailed in research reports, to contribute to the increase of methodological accuracy and to provide essential information to readers, allowing them to evaluate the rigor of the research. Thus, it is proposed that transcription should be considered a quality criterion in qualitative research.

• Transcription
• Methodology
• Qualitative research
• Naturalized transcription
• Denaturalized transcription

Nascimento, L.d.S. and Steinbruch, F.K. (2019), "“The interviews were transcribed”, but how? Reflections on management research", RAUSP Management Journal , Vol. 54 No. 4, pp. 413-429. https://doi.org/10.1108/RAUSP-05-2019-0092

Emerald Publishing Limited

Copyright © 2019, Leandro da Silva Nascimento and Fernanda Kalil Steinbruch.

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1. Introduction

The qualitative research has a set of specificities and characteristics that, notably, in the Social Sciences, runs through numerous philosophical conceptions based on a range of data collection, analysis and interpretation methods ( Creswell, 2010 ). This variety of techniques and procedures inherent in the scope of qualitative research is detailed in the method section of reports (e.g. papers, dissertations and theses) that originated from scientific investigations. Such description is essential for rigor analysis ( Gioia, Corley, & Hamilton, 2012 ), reliability analysis and validation analysis ( Flick, 2013 ) used in studies, because it reflects the acceptability of the research by its readers ( Mitchell & Clark, 2018 ).

Specifically, in the management field, there is a concern with the rigor used in researches ( Bertero, Alcadipani, Cabral, Faria, & Rossoni, 2013 ), which can be exposed and analyzed from the description of the method or from the methodological procedures used in studies. In this way, when elaborating scientific documents, there is a prerogative that the authors attempt to explain the methodological approaches adopted in the research, pointing out characteristics, possibilities and limitations of the adopted techniques or methods ( Gerhardt & Silveira, 2009 ).

This detailing becomes even more relevant in qualitative research since it presents specificities and restrictions ( Cavalcanti, 2017 ) that prevent the generalization of results, as many authors postulate ( Mattos, 2011 ). Before the reader's acceptance, the evaluation process of scientific works, especially papers, requires the agreement of the study between the peers, which occurs through the justification, the contribution and the relevance of the research ( Lukosevicius, Guimarães, & Zouain, 2019 ), as well as through the description of the methodological course adopted.

The methodological description includes several steps, one of which being data collection, that occurs in qualitative research mainly through interviews, observation or documents ( Creswell, 2010 ). The interview is a privileged technique of data collection in qualitative studies ( Duarte, 2004 ), especially in the management field and it is understood to be a form of non-standardized research with the objective to obtain individual views of certain interviewees or respondents on a specific thematic ( Flick, 2013 ). There is a variety of interview styles for data collection ( Gerhardt, Ramos, Riquinho, & Santos, 2009 ) and the methodology manuals (e.g. Gil, 2007 ) usually detail the procedures that researchers must adopt before and during each interview.

This demonstrates the methodological relevance of conducting interviews and the need to clarify the steps and procedures adopted with this technique, such as the question of validation and acceptance of the research, as well as other techniques and methods of qualitative research. After delimiting the procedures to conduct the interviews, it is common to detail the procedures adopted for the data analysis of the elements of the interviews, through the methods of content analysis ( Bardin, 2016 ; Flick, 2013 ) and discourse analysis ( Caregnato & Mutti, 2006 ; Gonçalves, 2016 ), which are widely adopted in qualitative research. It is also necessary to detail the course followed in executing the analysis based on these methods, the same way it is done with interviews.

However, even with the advancement of publications in management ( Lukosevicius et al. , 2019 ), it seems that there is still no concern in this field with the transition stage between the interviews and the analysis in qualitative research, i.e. the transcriptions. Every research involving the conduction of interviews also involves the transcription of the conversation into text to facilitate the analysis process, as well as to expose the reports or quotes to readers. Yet, qualitative studies in management do not usually emphasize the transcription technique in method sections, and generally, there is only a simple sentence saying that “the interviews were transcribed”. Therefore, some questions about the transcription technique emerge: How were the interviews transcribed? What are the procedures for transcription? What are the limitations? What guarantees the quality of a transcription?

About these issues, it is relevant to emphasize that some methodology manuals adopted in the management field do not detail the procedures that should be used in conducting transcriptions. When analyzing the books of Creswell (2010) and Flick (2013) , which are widely spread and used as methodological guides in management graduate programs in several universities, it is noticed that the authors do not describe the transcription technique. In the few cases in which the transcription is cited in these works, it appears as a necessary action (for conducting data analysis), but that, a priori, has no significant relevance since it is not discussed.

Considering the methodological gap presented, this paper aims to discuss the relevance of detailing the methodological procedures adopted in the transcription in research reports in the management field. This research is based on the understanding that many times, and without apparent justification, the transcription is not discussed in works of methodology in Social Sciences ( Farías & Montero, 2005 ), especially in the management field. This can lead to an (erroneous) understanding of transcription as an objective and pragmatic action ( Davidson, 2009 ). Instead of an objective action, transcription is a representational process immersed in the subjective interpretation of the researcher ( Bucholtz, 2000 ), requiring reflexivity of the person who is conducting the transcription process ( Oliver, Serovich, & Mason, 2005 ).

Thus, this study shows the different aspects that permeate the transcription technique and the way that detailing the procedures can further increase the qualitative research validity in the management field. Such understanding goes beyond the purely theoretical debate since it becomes essential to construct scientific reports. Therefore, this paper contributes to the methodological description that management students and researchers can adopt when making transcriptions.

Based on this detailing, especially in scientific papers, the editors and reviewers of management journals will have more elements on the methodological quality employed in qualitative research, which will contribute to the evaluation process and the acceptance of papers. In addition, this study collaborates to the stimulus of the analytical spreading of an important technique of qualitative research ( Davidson, 2018 ) that has not yet received attention in management studies.

This paper is characterized as a methodological essay, i.e. a theoretical essay oriented to a methodological discussion or approach. Thus, it is independent of empirical evidence and has an amorphous structure ( Soares, Picolli, & Casagrande, 2018 ). Besides that, this methodological essay focuses on the development of reflections that impact the essential discussions for the advancement of the (administrative) science, and the orientation of this type of study is not based on a search for true answers and statements, but on questions that guide and assist people to have deeper thoughts on certain aspects ( Meneghetti, 2011 ).

Structurally, in addition to this introduction, the characteristics of transcriptions are presented and the applicability of that is discussed using two techniques. Also, the relations of these transcription techniques with content analysis and discourse analysis are described, and it is demonstrated how transcription has been neglected in management studies. Thus, the paper seeks to call the students and researchers’ attention to the potential of the methodological description of transcriptions as a way to improve the rigor of qualitative research in management, i.e. it is proposed that transcription should be considered a quality criterion in qualitative research.

2. What is known about transcriptions?

In qualitative studies, researchers, especially in the Social Sciences, use interviews as a way to discover or interpret the meaning of certain events or facts reported by informants ( Farías & Montero, 2005 ). Interviews can be understood as a social rhythm ( Kvale & Brinkmann, 2009 ) or an interactional event ( Mondada, 1997 ) between actors, based on realities and cultural language practices that may be different for the actors involved in the process. In this way, the linguistic constructions that occur in the scope of the interviews are developed through socio interactional processes ( Santos, 2012 ), where the (social, cultural or political) reality of each actor is modified, constructed and/or deconstructed amid the reciprocal influence of language and personal values.

It is possible to affirm that interviews are socio-cultural constructions based on the subjectivity of the involved actors, requiring the attention of the researchers to perform interpretative actions of the meanings coming from the interviews. This interpretative process is part of the data analysis stage, but for the analytical deepening, it is necessary to transcribe the interviews (that are usually recorded), which is a central practice in qualitative research ( Davidson, 2009 ). At the same time, the transcriptions present many complexities and can be performed through different routes ( Oliver et al. , 2005 ). Thus, transcription seeks to transpose meanings and sociocultural representations from a spoken language to a written language ( Ong, 1982 ), being a process permeated by social, cultural, political and epistemological judgments of the researcher ( Kvale & Brinkmann, 2009 ).

Historically, studies about transcription emerged in Linguistics’ field as the seminal work developed by Ochs (1979) . Currently, many fields in Social Sciences, such as management, adopt transcriptions in qualitative research. In relation to the concept, the transcription literature provides many definitions of transcription ranging from the perspective of conversation analysts to researchers in linguistic anthropology and sociolinguistics ( Davidson, 2009 ). For Davidson (2009) , the different definitions come to a common understanding, in which the transcriptions are understood as data used by researchers, being developed through a selective process composed by definitions and reflexive theoretical objectives ( Ochs, 1979 ).

The transcription has been used over time as a way to show evidence about certain phenomena that constitute research interest in a study ( Duranti, 2006 ). The author states that transcription can be understood as a cultural practice. From this perspective, it is possible to infer that the act of transcribing can be influenced by cultural characteristics related to an institutional environment, academic environment and the background of the researcher who conducts the transcription process. Thus, a researcher from the north region of a given country can transcribe an interview, and the transcript will have certain characteristics that are not present in another transcript coming from the same interview, but transcribed by a researcher from the south region of this country.

This distinction occurs because of cultural differences, showing that transcription should not be understood and conducted as a mathematical process, in which there is a path to follow and a single result to achieve. That is, the act of transcribing is not a static process, but it is permeated by several factors that can influence the configuration of the transcript. This is consistent with Bucholtz's (2000) view of transcription as an interpretive process about what is transcribed and how it will be transcribed. In this way, it is possible to affirm that reflexivity is an inherent component of the transcription process, as stated by Bucholtz (2000) and Oliver et al. (2005) , which leads to the understanding of transcription as a process not only objective (in certain aspects), but also subjective ( Bucholtz, 2000 ), since it characterizes a representational procedure (the transcript) of the data coming from the interviews that are shaped by the researcher ( Green, Franquiz, & Dixon, 1997 ).

The act of transcribing can be based on certain pragmatic logics since they can facilitate the reader's understanding. For example, if the interviewee speaks with a very low voice, it will probably not be possible to understand some words recorded in the audio. Hence, in the transcript, the researcher will need to inform the reader about this situation, which can be demonstrated by the following expression: “(incomprehensible)”, as Marcuschi (2007) argues.

Thus, it is perceivable that objectivity is part of the transcription process, as well as subjectivity, since in the mentioned case above, if the researcher hears the recorded audio again and realizes that the respondent said a certain word but is not sure, the researcher can put this word in parentheses in the text, stating that there is no certainty that the word was spoken ( Marcuschi, 2007 ). This example demonstrates the subjective nature of the transcription, because another researcher can listen to the same audio and understand a different word, and with this, two transcripts from the same interview and its respective recording may present different words, putting in risk the meaning of the transcription and, consequently, the result of the data analysis.

Deepening the discussion about the need of standardization (objectivity) for a better understanding of transcription by readers, Marcuschi (2007) presents some norms that can be adopted in the transcription process. The norms are relevant to the transcription process because they help to minimize inconsistencies that may arise due to technical issues of the recording equipment and to noise or interferences that occur during the interview ( Farías, & Montero, 2005 ; Oliver et al. , 2005 ).

Based on the norms postulated by Marcuschi (2007) , it is possible to format the transcript so the reader can understand the adopted situations by the researcher when transcribing the interview, being evident in the transcript, for example, when the respondent emphasized a word. Some of the norms described by Marcuschi (2007) were grouped in Table I , along with the presentation of some illustrative examples of the application of the norms in transcripts. It is necessary to emphasize that many other norms are described in the work of Marcuschi (2007) , but this paper does not intend to discuss such norms, only to emphasize their relevance.

Besides Marcuschi’s (2007) work, there are other works (e.g. Atkinson & Heritage, 1999 ; O’Connell & Kowal, 1994 ) that discuss standardization in transcription (i.e. notation or norms) and it is relevant for the researcher to inform in the research report the source of the norms that supported the transcription. Thus, the reader is not taken by surprise when encountering unknown symbols in a report from a transcribed speech, because the reader will have the source information of the adopted norm and will be able to consult it. Also, the researcher can use some norms of Marcuschi (2007) , for example, and some norms from other works in the same transcript, as long as the researcher makes clear to readers the sources used and the rules corresponding to each author (work). Likewise, the researcher can create transcription norms, considering that the researcher explicitly informs and, if possible, graphically presents such norms created for the reader’s better understanding.

Although transcription norms are pragmatic concerning standardizing the transcription process, there will always be specific questions that require subjective actions in the transcription process. An example of that is the researcher's decision to present the written text as it was spoken (tending to a more natural or informal language, if any) or making adaptations between what was said and what was written to present a text in accordance with the language’s norms (formal language). Such reflections are essential in defining transcription, and they form the core of the following discussion.

3. Naturalized and denaturalized transcription

There are two basic types of transcription: naturalized and denaturalized, and they often represent extremes in the variety of transcription options ( Bucholtz, 2000 ). These positions correspond to two language representation views ( Oliver et al. , 2005 ). Both forms of transcription have the potential to serve as politicized tools of linguistic representation and, in some cases, are more similar than different ( Bucholtz, 2000 ). The discussion on this theme takes shape from two main views, which may be contradictory and at the same time, complementary. These views are here denominated as theoretical perspectives.

3.1 First perspective – the view of Bucholtz (2000 )

In the denaturalized transcription , the text has links with the forms of the oral discourse, making that, due to its fidelity to the spoken language, the written text loses sense in some cases and this ends up generating a paradox of using written texts to represent spoken languages ( Bucholtz, 2000 ). When the transcription is denaturalized, a description of the speech’s details occurs, and they are also exposed in the written report ( Bucholtz, 2000 ). In such cases, oral details are included through technical transcription systems – norms. Thus, denaturalized transcription preserves the characteristics of oral language ( Davidson, 2009 ). With that, the text that reflects a speech ends up being less comprehensible for readers who are not accustomed to finding oral discourse characteristics in a written text ( Bucholtz, 2000 ). For this author, the more the text reflects the “orality” of the speech, the less transparent and clear it will be for readers who are not used to interpreting oral characteristics in a written text.

In naturalized transcription , the text is in agreement with the written discursive conventions (formal language); that is, the written oral discourse is privileged. The risk in this type of transcription is not emphasizing enough the linguistic form and its transformation from the speech to transcripts ( Bucholtz, 2000 ). The naturalized transcription occurs when the written characteristics of the speech take precedence over those of the speech; therefore, the description of the interviews has many characteristics of the written language that, in fact, do not occur in the spoken dialogue, like commas, long stops and sentence completion ( Davidson, 2009 ).

3.2 Second perspective – the view of Oliver et al. (2005 )

Contrary to what was suggested by Bucholtz (2000) , Oliver et al. (2005) believe that in naturalized transcription , the whole sentence is captured with as many details as possible. Thus, this transcription represents a “real world” approach ( Cameron, 2001 ). In naturalized transcription, speech is expressed as it is, without being overly filtered by transcribers ( Oliver et al. , 2005 ). The goal is to present the data in a natural, objective and accurate manner. This naturalized view of the conversation is captured in the transcription structure and in the discourse representation itself ( Oliver et al. , 2005 ). This kind of transcription is suitable for those researchers who are interested in oral language intricacies. Oliver et al. (2005) point out that the effect of naturalism can alter our understanding of the social context of discourse because when naturally transcribing a recorded interview, assumptions can be made about what is standard and what is non-standard. According to the authors, a naturalistic approach provides details that may obscure substantial interview questions, and this may have some impact on data analysis.

In the opposite, denaturalized transcription preaches the constant grammar correction, the removal of the existing noises in the interviews and the standardization of non-standard speeches and accents ( Oliver et al. , 2005 ). According to these authors, this transcription results in “clean” data, which is free of socio-cultural characteristics and information, and may even improve the study results. This approach also suggests a textual description of speech, and although it seeks a complete and faithful transcription ( Cameron, 1995 ), denaturalized transcription does not consider the accents of involuntary vocalization and the representation of it in speech ( Oliver et al. , 2005 ). In other words, for denaturalization, precision is fundamental for the interviews’ transcription, pondering the meanings and perceptions made during the interview ( Oliver et al. , 2005 ).

Based on the arguments listed in both perspectives, the concepts of naturalized and denaturalized transcription are synthesized in Figure 1 .

As shown throughout this section, the two perspectives share similar and complementary views on the same phenomena (the two transcription techniques), since the concepts (naturalized and denaturalized) given for each phenomenon are inverse if the two perspectives are compared (according to subtitles 1 and 2 of Figure 1 ). This is clearly illustrated in Figure 1 , because the concept of naturalized transcription for Bucholtz (2000) is the same as the one of denaturalized transcription for Oliver et al. (2005) , and this inverse logic also occurs in the understanding of denaturalized transcription for Bucholtz (2000) and its equivalence to naturalized transcription for Oliver et al. (2005) .

How to proceed when facing this confusion of concepts? To minimize this confusion, it is necessary to understand that transcription is a cultural practice ( Duranti, 2006 ), in which the researcher needs to recognize and be sensitive to the linguistic and cultural nuances of transcription as a social practice ( Marcuschi, 2007 ). In this way, transcription can be understood as a cultural activity in which its transcript has a socio-historical character and can be considered a cultural artifact ( Duranti, 2006 ) capable of relating the linguistic form to the social world ( Jaffe, 2000 ).

From this understanding, it can be deduced that even though Bucholtz's (2000) work introduces the perception of transcription as a sociocultural practice ( Jaffe & Walton, 2000 ), the conceptual essence of naturalized transcription adopted by that author is not a purely cultural understanding, since it starts to denote a pragmatic (and even hegemonic) understanding supported by formal language.

On the other hand, the concept of naturalized transcription from Oliver et al. (2005) , which is related to informal language (a socially and culturally practiced in everyday life), better denotes the character of transcription as a cultural practice. Such understanding comes from the fact that formal language is preceded by the informal language, since linguistic variations arise in everyday practice and over time are legitimized and institutionalized as a standard, that is, formal language.

In this way, the concepts used in this paper (naturalized and denaturalized) are the ones proposed by Oliver et al. (2005) , because they better represent the role of transcription as a process permeated by the knowledge of social, political and cultural dynamics ( Pelzang, & Hutchinson, 2018 ) specific to a research context. Having clarified the confusion of concepts, it is important to note that the work of Bucholtz (2000) is complementary to that of Oliver et al. (2005) , as shown in subtitles 3 and 4 of Figure 1 . Thus, the concept of naturalized transcription ( Oliver et al. , 2005 ) is complemented with that of denaturalized ( Bucholtz, 2000 ) transcription, because they are congruent (according to subtitle 3 of Figure 1 ) and vice versa for denaturalized ( Oliver et al. , 2005 ) and naturalized ( Bucholtz, 2000 ) transcription (according to subtitle 4 of Figure 1 ).

3.3 Naturalized or denaturalized: is there a better technique?

On the one hand, the transcription classified as naturalized ( Oliver et al. , 2005 ) can generate conflicting interpretations, because readers who are not accustomed to deciphering these interventions in the dialogue, such as noises, pauses in speech, slang, accents, etc. end up decoding these elements in the way they believe are the best, and this can cause different understandings from the same study.

However, by leaving the interview in its purest and least altered form, the researcher would be as transparent as possible with the readers to demonstrate the reason for the analysis. By keeping the transcripts on their original form, respondents end up speaking for themselves ( Schegloff, 1997 ), since dialogue is not simply an exchange of ideas, but it also involves verbal and non-verbal cues that can alter the tone of the conversation and, consequently, its meaning ( Oliver et al. , 2005 ).

On the other hand, when considering the denaturalized transcription ( Oliver et al. , 2005 ), the author interprets what was said, the way it was said, the noises and interferences in speech, being responsible for this understanding, making the necessary adjustments in the transcription and passing this data to readers, thus everyone will have the same view of what was said. However, depending on the author who does this refinement of the interviews, the results may vary, as well as the data analysis.

Therefore, the great dilemma between the two transcription techniques lies on how much the researcher wants to make the details and the interferences of the interview available and on how much they believe these elements are relevant to the study. This choice considers that, inevitably, transcription is an act of power, since it consists of interpretation and representation actions, involving the context in which speech is inserted ( Bucholtz, 2000 ).

Additionally, it is not possible to develop a perfect transcription process with no ideological positions and personal interpretations ( Bucholtz, 2000 ). For this author, the act of transcribing is authorial and requires creativity, because it considers that humans are not machines, but people who interpret a text and therefore select the most important points according to their own view (or epistemology). Hence, Bucholtz (2000) believes that the act of transcription must be done with responsibility, but not necessarily with neutrality. However, in this paper, it is understood that the choice made should be clearly exposed to readers, as a transparency action, allowing them to discern on the methodological course adopted and have a basis to critically reflect the level of the methodological rigor used (in the process of transcription).

That said, the best technique of transcription is […] wait! There is no technique better than the other! The choice of one of the techniques is dependent on the researcher's reflexivity ( Oliver et al. , 2005 ) and there may be even a half term – using a mix of the two techniques in the same transcription. Regardless of the option, this should be explicitly informed to the reader, as discussed previously. Even if the option is made by the researcher, there are some factors that induce the use of one technique or another, such as the method of data analysis, which is discussed below.

4. Naturalized and denaturalized transcription, norms of transcription, content and discourse analysis: plausible relations

The techniques of naturalized and denaturalized transcription come from the method of data analysis that was defined in the methodological course, being emphasized those of content analysis and discourse analysis. These methods were selected (among many others) because they are widely used and known in qualitative research in the management field, contributing to a better understanding of the relations here discussed. A priori, these relations may sometimes not be identified by the researcher, and this ends up generating an incongruence (little noticeable for some researchers) between the method of analysis and the technique of transcription prior to this stage.

A method of data analysis is a form of interpretation, having specific procedures or techniques that prepare the data for the analysis, since this process aims to make sense in texts ( Creswell, 2010 ). Clearly, the choice of the most appropriate analysis procedure depends on the study purposes, the researcher's ideology and, of course, on the analyzed data ( Chizzotti, 2006 ). Referring to a reverse flow to what was proposed by Chizzotti (2006) , it is believed that the adoption of a transcription technique depends on the method of analysis employed. Therefore, after going through all the methodological planning until the decision by the method of analysis that best fits the research, the researcher must return to the previous stage and decide if it will be used naturalized or denaturalized transcription.

How to know which transcription technique is most appropriate for a given method of analysis? The literature already points to discussions that help in this decision, because as Davidson (2009) states, several methods of analysis can be related to the two transcription techniques. As Oliver et al. (2005) suggest, discourse analysis is related to denaturalized transcription. However, it is argued that such a relationship is fragile (and even incompatible), because it is understood that the discourse analysis has a direct relation with the naturalized transcription , according to the arguments that follow.

This relation is possible because the discourse analysis tries to ponder the mechanisms of domination that are hidden under the language ( Orlandi, 2013 ). The discourse analysis studies words and expressions as well as their form and structure, the use of language, context, interpretations and meanings of discursive practices ( Putnam & Fairhurst, 2001 ), given that it seeks inference through discursive surface effects ( Bardin, 2016 ).

Thus, in the discourse analysis, there is something (subjective and implicit) that goes beyond what was simply said or written, since it investigates how the content is used to achieve a certain effect, is imperative that the researcher has the sensitivity of capturing different subjective interpretations and implied speech in a discourse ( Vergara, 2012 ). That is, to capture the essence of a discourse, it is necessary to analyze it also from the expressions that were spoken or expressed physically by the interviewee. This can only be demonstrated in the transcript from the literal transcription of the interview, without modifications adapting the text to the formal language, that is, the naturalized transcription.

Therefore, language addictions, slangs, syllabic repetitions and even facial expressions or body movements are able to indicate the presence of emotional situations that can reinforce or contradict the words said by the respondents, leading to certain inferences during the analysis. And in order for the researcher to have access to this information in the discourse analysis stage, it is necessary to develop the transcription in a naturalized way.

Just as discourse analysis is for naturalized transcription, content analysis is for denaturalized transcription . Content analysis includes the explanation, systematization and expression of speech content so that logical deductions can be made according to the context of these speeches, and to who issued them and what effects are intended ( Bardin, 2016 ). This method relates the semantic structures to the sociological structures of the statements and analyzes the texts in a way that it is possible to identify their characteristics ( Minayo, 2001 ). The purpose of the method is to understand the meaning of the communications performed, the manifest and latent content, as well as the meanings in the speech ( Chizzotti, 2006 ), which allows to treat the collected data in a way that it is possible to identify the meaning of what was said ( Vergara, 2012 ).

In this way, while the discourse analysis focuses on the sense of discourse, the content analysis focuses on the content of the text ( Caregnato & Mutti, 2006 ), not being much relevant for the latter, for example, the expressions of informal language or other emotional aspects expressed by respondents during an interview. Thus, there is no need to perform a naturalized transcription, which allows the researcher to use reflexivity to leave the text in accordance with the formal language and extract the necessary analytical content to conduct the research.

And where transcription norms enter into these relationships? Norms, like those postulated by Marcuschi (2007) , are essential for the naturalized transcription, because they help to include spoken phenomena in the written text, which will help the conduction of discourse analysis, for example. Thus, it is relevant to use norms to demonstrate when the respondent repeats certain expressions such as “hm […] hm […]”, which may indicate that the respondent is uncertain about the response or speech. Also, the writing of this repetition assists in conducting inferences in discourse analysis, leaving such inference in a clear way (by demonstrating the interview reports or quotes) to the readers of the research report.

In the content analysis, because there is no need to detail informal expressions of the language, the norms are not faithfully followed, since denaturalized transcription prevails. However, it is understood that some norms are necessary for this type of transcription, as the indication that at a certain moment the researcher did not understand a word that the interviewee said, as the use of (incomprehensible). In addition, if the transcript highlight slangs or unknown expressions, such reports from transcription presented in a paper, for example, can make the reading of these reports tedious or incomprehensible to the readers since the focus is on content and not in speech. Thus, if a respondent has a low level of education and speaks in a wrong way (grammatically), the ideal is to demonstrate these words correctly (formal language/denaturalized language) in transcripts, leaving the text concise and clear to readers.

In this way, it is noticeable that there are relations between the transcription techniques to be adopted (naturalized or denaturalized), the transcription norms and the methods of data analysis (content or discourse analysis), since the last two induce to one transcription technique that must be based on transcription norms. These norms must be strongly present in the naturalized transcription and not so present in the denaturalized transcription. The presence (although minimal) of transcription norms in the denaturalized technique is justified by the reflexivity that is also necessary in this technique, and in some cases, it will be necessary to use some norms to demonstrate where there is the researcher’s interpretation (subjective aspect) in the transcription.

5. From relevance to anonymity or from anonymity to relevance

Although this paper has discussed the role of transcription for qualitative research in management and the relevance of detailing (in research reports) the methodological procedure used for conducting the transcription, it is relevant to reinforce the argument that structures the construction of this paper: the neglect of transcription as a component of scientific rigor in qualitative academic studies in the management field.

That said, and taking advantage of the amorphous structure inherent in the construction of a methodological essay, a simple theoretical analysis is presented below to enhance the central argument of this paper. This analysis consisted of the reading of scientific papers published in three generalist top journals of management with a high impact factor. After selecting the journals, it was accessed the current issue of each of the three (on May 10th, 2019) and all papers that conducted data collection by interviews were selected. Sequentially, it was sought to identify in these papers how the interviews were transcribed and, as expected, the papers generally stated that “the interviews were transcribed”, but did not inform how they were transcribed. Table II compiles the information presented in this paragraph.

Based on the information presented in Table II , it is possible to affirm that the methodological detailing of transcriptions in the qualitative research in management is supported by an anonymous perspective. Thus, it is understood from this that transcription is a given, static and institutionalized fact, and it is not necessary to detail the steps that shape the transcription process nor the choices subjectively adopted by the researcher during this process. This is nothing more than the lack of attention to an important methodological step for qualitative research since it is from the transcription that the data analysis is carried out within the scope of the interviews.

In contrast to the current academic state of methodological neglect of transcription, it is necessary to include the methodological detail adopted by the researchers to conduct the transcription in the research reports, especially in papers. It is believed that this action further strengthens the quality employed by the researchers during the conduction of the research, as well as generates transparency so that readers can discern about the methodological rigor employed during the conduction of the work.

6. In search of the final remarks

Nowadays, it is identified that the method sections are being shortened in research reports, especially in papers, which makes researchers deduct that this essential part should be succinct. However, considering its essence and specificities, qualitative research needs more detailing about the methodological procedures. This ensures more clarity to the reader to understand the course adopted, the motivations that led to the course chosen instead of others and the constraints faced. Therefore, detailing allows greater transparency regarding the reliability of the research results and can be a quality criterion to evaluate the research rigor ( Godoy, 2005 ).

In this way, it is necessary to consider the transcription as one quality criterion in qualitative research, which can be demonstrated by means of the methodological detailing in scientific reports. This quality criterion contributes to enrich the methodological rigor, i.e. quality itself ( Lincoln, 1995 ) and demonstrates the researcher sincerity (self-reflexivity and subjective aspects) in conducting the research ( Tracy, 2010 ). Thus, considering that there are many ways of enhancing validity in qualitative research (Mays, & Pope, 2000), the methodological detailing of transcription as a quality criterion can be one of these ways, which contributes to the quality increase in qualitative research ( Golafshani, 2003 ).

Therefore, how to indicate the adopted transcription procedures in a research report? There is no unique recipe or template to be followed. The methodological description will depend on the authors’ intention to clarify certain aspects that were relevant during the transcription process. Nonetheless, it is suggested that the researcher indicates which technique (between naturalized or denaturalized transcription) was adopted (according to the classification already discussed in this paper) or if a variation between them was adopted using reflexivity.

Based on reflexivity, the researcher can report, for example, that the naturalized technique was used, but that at times it was used a more subjective and reflexive basis to change the spoken language so that it became clear (formal language format) in the transcript, avoiding language confusion for readers. Going further, it is relevant to relate the adopted transcription technique with the method of data analysis used in the research, making it clear to readers that the denaturalized transcription was adopted with the purpose to facilitate the content analysis process, or that the naturalized transcription provides greater robustness for the discourse analysis.

It is also relevant to show the reader the adopted transcription norms and its sources. Thus, if the researcher creates norms, a table illustrating such norms can be created, as shown in Table I . This contributes mainly to the clarity in the reading of the reports coming from the interviews since the reader needs to know the norms to be aware of how to identify and understand them by reading interview quotes in scientific reports.

Such detailing is necessary because researchers need to look at their own choices of transcription, including their own limitations, and it is important to explicit them to readers ( Bucholtz, 2007 ), since there are analytical and/or political biases ( Jaffe, 2000 ) that can induce the process of interpretation between spoken and written language, which can change, consecutively, the result of the research. Thus, detailing methodological procedures of transcription in papers can help the evaluation process in journals, since the transcription as a quality criterion can provide more bases for the reviewers to analyze the methodological rigor (including other methodological procedures of the research) from the evidenced transparency in the text.

This paper also presents a potential contribution to the advancement of qualitative research in the management field, since it stimulates reflection on researchers, especially on those who do not have a deep knowledge of the transcription process. Moreover, by using a simplified language, this methodological study can serve as a guide to undergraduate and graduate management students, because it allows them to learn about transcription procedures and techniques, which will be relevant for their academic maturity on methodology. Hence, students will be able to conduct qualitative research with more reflexivity, precision, and detailing on transcription processes, avoiding (or reducing) possible confusion and misunderstandings in data analysis.

There is a variety of methods for data analysis in qualitative research ( Gonçalves, 2016 ; Miles & Huberman, 1994 ), such as conversation analysis ( Marcuschi, 2007 ), dialectical hermeneutic analysis ( Minayo, 2006 ), narrative analysis ( Ryan & Bernard, 2000 ), content analysis ( Bardin, 2016 ) and discourse analysis ( Orlandi, 2013 ). In this way, the choice for delimiting the unit of analysis of transcriptions only for content analysis and discourse analysis (for being more recognized in management) characterizes a limitation of this paper.

From this limitation and from the understanding that all of the methods of analysis above are based on different philosophical foundations ( Chizzotti, 2010 ) and cultural practices, the following question arises: how do the naturalized and denaturalized transcription techniques, norms of transcription and reflexivity relate to each of these methods of analysis? This questioning is necessary to conduct any research based on interviews, allowing a better adaptation of the transcription technique (naturalized or denaturalized) to be adopted to (the specificities of) each method of analysis and can be investigated in future research.

Taking advantage of the discussion about specificities and the choice of discourse analysis and content analysis to support reflection on transcription techniques, another limitation of this paper is given by the consideration of these methods in a universal way. However, the method of content analysis is composed of a set of analysis techniques ( Campos, 2004 ) that correspond to some variations of the content analysis, which also occurs with the method of discourse analysis. Thus, there are variations of both content analysis (Fonseca, 2009 ) and discourse analysis ( Flick, 2013 ; Gonçalves, 2016 ) that were not considered in this discussion.

Some of these variations or techniques are associative content analysis, statement content analysis, thematic or categorical content analysis ( Bardin, 2016 ), Foucauldian discourse analysis, semiotic discourse analysis and critical discourse analysis ( Chizzotti, 2010 ), among others. Thus, future research may consider the particularities of each variation of content analysis or discourse analysis to better suit the transcription technique (naturalized or denaturalized) to be adopted for each one, from a higher or lower level of reflexivity (which will be determined by the specificities).

Considering the relevance of detailing the methodological procedures adopted for transcriptions conduction, it is noticed that the migration of the current paradigm in which relevance is obscured by anonymity to another possibility is a challenge. Thus, is it possible that relevance surpasses and supplants anonymity about the methodological detailing of transcriptions in qualitative research in management? This is an issue that remains to be thought out and rethought.

Finally, to the readers of this paper, a question is proposed: during your academic journey, how many papers have you read, written or reviewed that explain the methodological details adopted to conduct a transcription? With this, the possibilities are open to sympathizers and even to critics of the arguments presented here to take this discussion to the various academic circles that legitimize and institutionalize the (administrative) science.

Concepts of naturalized transcription and denaturalized transcription

Transcription norms

Occurrence	Description	Signs	Examples
Doubts and assumptions	It is common not to understand parts of speech. In this case, the place is marked with parentheses, having two options: (a) indicate them with the expression “incomprehensible” or (b) write in them what the transcriber think to have heard	( )	Interviewee: There is the (impact). There are in my view three important impacts
Sudden truncations	When an interviewee cuts a unit, it is possible mark the suit with a bar	/	Interviewee: So, we have here sev/85 per cent preservation
Emphasis or strong accent	When a syllable or a word is pronounced with emphasis or accent stronger than usual, the fact is written in uppercase	UPPERCASE	Interviewee: The customer, in turn, when he goes to buy, he asks for ALL of this documentation before closing the deal
Reviews from the Analyst	To comment something that occurs, double parentheses are used at the occurrence moment or just before the segment to which it refers	(( ))	Interviewee: ((hesitates to answer the question)) See … What … it was said very long ago that it was impacting on the region in question
Pause filled, hesitation or attention signals	Basically there are reproductions of sounds whose spelling is much discussed, but some are more or less clear, like: “hm” and several others		Interviewee: Later, it goes to (incomprehensible)… That's the preview, then comes ah … ah… are three licenses

Elaborated by the authors from the compilation of information present in