Elaborated by the authors from the compilation of information present in
Journal | Impact factor (JCR) | Issue | No. of papers in the issue | Papers with interviews that were transcribed | In how many papers is detailed how the interviews were transcribed? |
---|---|---|---|---|---|
6.700 | 62 (2) | 12 | 2 | 0 | |
8.080 | 45(5) | 16 | 2 | 0 | |
5.329 | 56 (3) | 8 | 5 | 0 |
a 2017 Journal Citation Reports (JCR)
Source: Elaborated by the authors (2019)
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Leandro da Silva Nascimento lead on the conceptualization, data curation, supervision and validation. Fernanda Kalil Steinbruch contributed equally to all other aspects of the paper.
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Updated: August 20, 2024
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I've been editing videos for almost a decade, both professionally and for my passion projects. So, believe me when I say I know how tedious editing even the shortest videos can be.
Fortunately, many AI video editing tools can help speed up the process of editing, whether you're a beginner or an expert videographer.
Here are 8 of the best AI video editing tools I've found and what I like most about them.
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1. adobe premiere pro.
I've been using Adobe Premiere Pro for work and for my own passion projects for 10 years, so I was intrigued when the video editing software announced that new AI tools are available on its platform and that more are on the way.
These are a few of my favorite AI tools within Adobe Premiere Pro:
This feature allows users to auto-generate their transcripts, highlight text to add clips to their timeline, edit, reorder, and refine their clips like they would a text document.
I love this feature because it makes audio editing a breeze by recognizing if a clip is music, sound effects, dialogue, or ambient sound and providing access to relevant tools for editing based on the sound type.
To make your videos more accessible, Adobe Premiere Pro‘s speech-to-text feature automatically generates transcripts in up to 18 languages. It can also distinguish between speakers and create captions that match the video’s cadence and speech pattern.
Price: Plans start at $22.99 per month.
Best For: Intermediate to professional video editors. Though beginners can use Adobe Premiere, it does have a bit of a learning curve.
DaVinci Resolve has many AI tools and has incorporated AI into its platform for years. The one I find the most interesting is its facial recognition. Now, bear in mind I have very minimal experience with DaVinci Resolve, so I don't have much experience with this feature.
That said, its facial recognition allows users to group clips based on the people who appear in them. All I'd have to do is select a group of clips and right-click to select “Analyze Clips for People.” From there, DaVinci Resolve will scan the clips, assess the faces that appear, and group clips with the same faces together.
If you're editing video of an event with a lot of people and you want to narrow down your clips to include a specific group, this feature will save time and effort.
Price: Users can download DaVinci Resolve for free, or buy the studio version for $295.
Best for: Beginner to expert video editors.
I can‘t scroll through TikTok for over a minute without seeing a video edited with CapCut. I’ve never used CapCut, but I'm truly amazed by the quality of the videos people churn out using the app.
CapCut is known for being a user-friendly video editing app that is suitable for anyone, regardless of their level of editing experience. Part of what makes CapCut so easy to use is its AI tools, which can do most of the work for you.
For example, let's say I have a long-form video I want to shorten so I can upload it to a short-form video platform like Instagram Reels or YouTube Shorts. I can take that long-form video, upload it to CapCut, and identify standout moments, and the app will generate a shorter version of the video.
Price: CapCut is free to use but paid options start at $20.84 per month.
Best for: Short-form video editing for platforms like TikTok, Instagram Reels, or YouTube Shorts.
Runway is a platform specializing in creating AI tools for video editing and other aspects of visual art. To be honest, I wouldn't recommend Runway for beginner video editors or those with no experience at all.
The platform can do a lot, but its plethora of tools and their uses can be overwhelming. Even I, an experienced video editor, was only able to assess the tip of the iceberg of what Runway can do.
That said, its AI video generator is simple to use. To test it out, I entered a prompt asking for a video of a cat frolicking in a forest and chasing after a butterfly. The images weren't perfect, but I can see them being used to figure out concepts or inspiration for shots or art.
Price: Runway is free with limited features, and paid plans start at $12 per user per month.
Best for: Expert video editors.
I'm pretty familiar with InVideo after using it to create a TikTok video a while back. You can read all about it here if you want to know my experience with InVideo.
In short, I enjoy InVideo because it's very simple to use. Unlike Runway, I definitely recommend it for marketers and creators who want to churn out simple short-form videos with little to no video editing experience.
My favorite feature of InVideo is its AI text-to-video tool. When I used InVideo to create a TikTok, I entered a script into the text box, selected a few images, and the platform generated a video based on the script in just a minute.
Price: InVideo is Free, and price plans start at $15 per month.
Best for: Editing and generating short-form videos on a tight schedule with little to no experience.
Clipchamp is a video editing app by Microsoft that allows users to easily make videos even if they have no video-editing experience. A standout AI tool is its AI text-to-speech, which boasts over 400 voice styles in 170 languages.
Users can choose a mood, pitch, accent, and gender for the voice.
Price: Free for beginners, and prices start at $11.99 for everyday editors.
Best for: Beginners looking to make short-form videos.
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I found Descript to be pretty easy to use. It‘s a platform boasting a variety of AI tools, and one I find the most convenient is its AI transcript generator. Uploaded a video of myself talking to my cat (don’t judge me), and the AI transcribed it perfectly.
I then uploaded a clip from a concert I went to where the singer shouted the name of the next song over a crowd of screaming fans. The AI misheard the singer, so the transcription was incorrect (the song's name is actually “Space Ghost Coast to Coast”).
However, Descript allows me to edit the transcription so it's correct.
Price: Limited features available for free and pricing starts at $12 per person per month.
Best for: Beginners editing quick-hit videos.
Wondershare Filmora is a cross-platform video-editing software that is known to be ideal for beginner and intermediate video editors and creators. I was impressed by the number of versatile AI tools available I found while browsing its site.
One tool that sticks out to me is the AI Frame Interpolation. This feature enhances video visuals without losing quality, increases low frame rate automatically, and supports a variety of video formats including GIFs.
If I want to slow down a video without deteriorating its appearance, the AI Frame Interpolation would be most useful.
Wondershare Filmora's AI Vocal Remover is also interesting. User can separate vocal and music from a song and keep high quality audio while removing vocals.
Price: There is free version with limited features, and pricing starts at $49.99 per year.
Best for: Beginners and intermediate editors looking to create long or short-form videos.
Video editing can take anywhere from from several minutes to a few hours, depending on the project. If you're curious about how AI can streamline the process for you and give some of your time back, you now have new tools you can test out for yourself.
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Department of Politics & Society, Aalborg University, Aalborg, Denmark
While there is a vast literature that considers the collection and analysis of qualitative data, there has been limited attention to audio transcription as part of this process. In this paper, I address this gap by discussing the main considerations, challenges and implications of audio transcription for qualitative research on the third sector. I present a framework for conducting audio transcription for researchers and transcribers, as well as recommendations for writing up transcription in qualitative research articles.
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.
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.
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.
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.
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 matters for third sector research because qualitative research methodologies make up a large percentage of studies undertaken on nonprofits—as much as 40–80% of research published in this field (Igalla et al., 2019 ; Laurett & Ferreira, 2018 ; von Schnurbein et al., 2018 ). Audio transcription is particularly important for third sector research for several reasons. In conducting qualitative research (which aims to produce rich, rigorous description) and as third sector researchers (who study organizations that seek to improve society and who may be working with traditionally disenfranchised or disadvantaged communities), we have a particular ethical obligation to ensure that our research provides an accurate depiction of our participants’ lives and the organizations with which they are involved.
However, transcription is perhaps the most underacknowledged aspect of the qualitative research process, and this is also evident in the way that transcription is discussed in research articles. In order to survey the current depiction of the transcription process in third sector research, I undertook a review of the 212 most recent papers in Voluntas that include the word ‘interview’ to explore how qualitative research articles discuss transcription as part of their methodology. 1 Of these papers, 79 were deemed not applicable (because they were quantitative research papers that mentioned interviews in another context, or used the word interview to denote the administering of a structured questionnaire, or systematic review papers reporting on other research). This left 133 articles which were analyzed to explore the extent to which transcription was described—if at all—as part of the research methodology. 2
The analysis (illustrated in Fig. 1 ) found that 41% of papers employing interviews as a research method did not mention transcription at all, while 11% mentioned transcripts but not the process of transcription. It was not clear from these whether or not interviews were recorded or if researchers relied upon written notes taken during interviews, or how information from the oral interview was converted into analyzable text. The most common discussion of transcription (19%) was a simple sentence along the lines of “interviews were recorded and transcribed”, while 26% gave some further information including who undertook the transcription (the researcher(s), a research assistant or a commercial company) or that the interviews were transcribed ‘verbatim’ (with none explaining what they mean by this term). These findings are not dissimilar to a study of qualitative research in nursing, where it was found that 66% of articles reporting solely that interviews were transcribed, and the remaining articles indicated only “full” or “verbatim” to clarify the process (Wellard & McKenna, 2001 ). I also surveyed the first authors’ departmental affiliations/field of study to gauge any differences between academic fields (Table (Table1) 1 ) although there were not considerable differences.
Transcription in Voluntas qualitative articles
Description of transcription and field of first authors
Field | Transcription not discussed or passive mention of transcripts | Discussion of transcription | Interviews not recorded | Total |
---|---|---|---|---|
Business, management & economics | 13 | 17 | 1 | 31 |
42% | 55% | 3% | ||
Political science and international development | 18 | 8 | 0 | 26 |
69% | 31% | |||
Sociology | 8 | 12 | 0 | 20 |
40% | 60% | |||
Public policy & public administration | 12 | 6 | 0 | 18 |
67% | 33% | |||
Social work | 4 | 7 | 1 | 12 |
33% | 58% | 8% | ||
Other or field unclear | 14 | 10 | 2 | 26 |
54% | 38% | 8% | ||
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.
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.
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 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.
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 ).
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.
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).
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.
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.
No funding was received to assist with the preparation of this manuscript.
The author declares that they have no conflict of interest.
1 While this approach may have obscured other methods that employ transcription, such as focus groups, the intention of the survey is to provide a snapshot illustration of transcription and qualitative methods rather than a systematic review.
2 Articles reviewed are listed in Appendix 1.
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COMMENTS
Understanding the transcription process. 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 ...
Introduction. 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 ...
During the time when I developed my research design, I was part of an inter-disciplinary seminar of narrative researchers. Our discussions inspired me to tran-scribe the first round of interviews by hand. The graduate students and faculty in my research seminar viewed hand transcription as a best practice: transcription
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.
The first step in transcribing research interviews is to choose the right transcription method. There are three main methods to consider: verbatim, intelligent verbatim, and edited transcription. Verbatim transcription includes every utterance from the interview, including filler words and non-verbal communication.
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 ...
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.
Transcription is a practice central to qualitative research, yet the literature that addresses transcription presents it as taken for granted in qualitative studies. In this article the author provides a review of three decades of literature on transcription between 1979 and 2009. The review establishes core understandings and issues that have ...
A transcription guide was developed, and interviews were transcribed verbatim using semi-naturalized transcription [32, 33]. We refer to this data set in the results as "interview data". ...
Transcription is a practice central to qualitative research, yet the literature that addresses transcription presents it as taken for granted in qualitative studies. In this article the author provides a review of three decades of literature on transcription between 1979 and 2009. The review establishes core understandings and issues that have ...
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 ...
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.
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.
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 ...
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.
Any researcher designing and undertaking interview-based research must make various decisions. In my research, a major consideration was that I would be undertaking semi-structured interviews in the context of domestic abuse, a field that is considered sensitive (Buchanan & Wendt, 2018).Sensitive research can be defined as any research that poses a potential risk to those being researched and ...
1. Introduction. Over the years, transcription has become a norm in qualitative research (Cassell and Bishop, 2019, Francis and Holloway, 2007).Transcription results from the traditional protocol and convention in qualitative research to record and transcribe interviews (Flick, 2014) and more widely qualitative data.Transcription has many different definitions considering the particular ...
One of the primary reasons why researchers transcribe qualitative interviews is the importance of correctness. Every word, pause, and emotional inflection can provide valuable insights vital to the research procedure. Read on to find out more of the benefits transcription poses regarding correctness:
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.
1. 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.
Transcribing interviews has been described as a 'boring drudgery' (Johnson, 2011, p. 92), a 'painful' (Sarkar, 2021, p. 141) and 'notoriously time-consuming and often tedious task' (McMullin, 2021, p. 141) that can be a 'nightmare' (Bailey, 2023, p. 112) and that is therefore 'often contracted out' (Horrocks & King, 2010, p. 119) for people 'underestimate the amount of ...
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If you're editing video of an event with a lot of people and you want to narrow down your clips to include a specific group, this feature will save time and effort. Price: Users can download DaVinci Resolve for free, or buy the studio version for $295. Best for: Beginner to expert video editors. 3. CapCut
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 ...
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