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Abstract

There is an increasing expectation to demonstrate research equity awareness which charges researchers with the responsibility to methodologically attend to issues of ethical and equitable inclusion in qualitative research. While many strategies toward this end have made strides in improving research inclusion in terms of enhancing inclusive participation in research, little evidence is available to guide researchers in promoting inclusive representation in research. We examine the conceptual and practical relevance of transcription for addressing inclusive representation in qualitative research through the introduction of “transcript alignment” as an alternative to transcript cleaning, transcript checking and reviewing transcripts for verbatim accuracy. Practically, transcript alignment addresses seven common pitfalls of axio-analytic neutrality that impact inclusive representation: missing data, mistaking data, tidying data, overlooking data, shrinking data, sanitizing data, and removing data. Integrating transcript alignment into their practice, qualitative researchers can reaffirm the naturalistic tenet of “investigator as instrument,” generate “thick transcription,” and ultimately carry forward an ethos of participant inclusion throughout data analysis and presentation activities. Continued assessment and refinement of the practical application of the concept of transcript alignment has potential to enhance ethical and inclusive research conduct across all phases of the research process.

Keywords

methods in qualitative inquiry narrative social justice dimensional analysis critical theory

As equity consciousness has become more and more a part of the research enterprise, there is a growing expectation that researchers methodologically attend to issues of ethical and equitable inclusion in qualitative research. This expectation to demonstrate research equity awareness has led to an ocean of publications noting efforts toward that end. While the extent to which ethical, effective, or meaningful inclusion is in fact achieved may vary widely, this consciousness-raising has led to a proliferation of methodological innovations enhancing inclusion in research, especially for populations that may have had limited participation in research studies previously. Importantly, many strategies toward this end have made strides in improving research inclusion enabling researchers to explore phenomena under study more comprehensively, enhance transferability of research findings, and decolonize research-generated knowledge. However, the majority of research inclusion efforts tend to focus on participant recruitment (McAreavey & Das, 2013; Perez et al., 2022), sampling (Ellard-Gray et al., 2015), or engagement in data collection (Hogger et al., 2023; Kenny et al., 2023; Watharow & Wayland, 2022; Whitney & Evered, 2022) without attending to subsequent phases of the research process; while important efforts have been made to enhance inclusive participation in research, there is a dearth of evidence available to guide inclusive representation in research. We define inclusive representation as representation in the data that researchers analyze, in the findings that are generated from analyses, and in the data excerpts that are presented to illustrate and confirm interpreted findings. In order to more holistically address ethical and equitable inclusion in qualitative research overall, methodological strategies should prioritize inclusivity in research representation in addition to research participation.

We propose that researchers leverage the process of transcription to extend an ethos of inclusion in qualitative research beyond participant selection and data collection, and into the analysis of data and dissemination of findings. Transcription is defined as the generation of “any graphic representation of selective aspects of verbal, prosodic, or paralinguistic behavior” (Flick, 2014 p. 66) and most commonly results in a de-identified record of textually-transformed audio-recorded data. Though mentioned by name in the methods section of most empirical qualitative publications, transcription has been described as the “most underacknowledged aspect of the qualitative research process” (McMullin, 2023, p. 142). While the transcription process holds a cornerstone position in qualitative research, nestled between more popular peer processes of data collection and data analysis, there is very little available to practically guide researchers through the complexity and nuances of the process of transcription (Flick, 2014; McMullin, 2023). Further, the literature yields virtually no conceptual foundation for understanding the significance or potential of transcription as an approach to encourage inclusion in qualitative research.

In this article, we explore the conceptual and practical relevance of transcription for addressing inclusive representation. In doing so, we must acknowledge our position that data transformation and data de-identification are inherently data-reducing activities that are also critical for the conduct of both ethical and inclusive research (Grundy et al., 2003; Lapadat & Lindsay, 1998). While at first the reductive nature of transcription may seem in conflict with inclusion, once transcription is viewed as one piece of the research process puzzle that cannot be properly explored in isolation, the data reduction inherent to data transformation and de-identification can be understood as necessary ends to a means of greater overall inclusion in qualitative research. In other words, in order to feel comfortable consenting to participate and engaging in data collection, participants must trust that the experiences they share with researchers will be appropriately protected. However, while the “inevitable and problematic step” (Flick, 2014, p. 65) of transcription must remain “inevitable” to uphold an obligation to protect participants’ confidentiality, it need not do so a manner that is quite so “problematic,” overshadowing the preservation of participant narratives and the promotion of inclusive data presentation.

We aim to elucidate the critical implications of the transcription process for improving inclusion in qualitative research. We begin by providing a conceptual foundation for understanding the different conceptual perspectives that can frame how the transcription process takes shape. Then we introduce transcript alignment as a strategy to generate “thick transcription” that promotes inclusion for data analysis and data presentation. Ultimately, we advance the process and principles of transcript alignment to practically attend to inclusion – once participants have shared their lived experiences, the recording device has been turned off, and the resultant data are left in the hands of the researcher.

Conceptual Perspectives on the Process of Transcription

The conceptual perspective of a researcher – be it explicit or implicit – drives the operationalization of the transcription process. In our observations, the dominant default perspective on transcription in the current context of qualitative social science research is that of ‘axio-analytic neutrality.’ This perspective holds that the elements of the transcription process are ‘objective’ and disconnected from potential bias or influence from the transcribing entity’s choices, analytic abilities, or individual, institutional, or socio-cultural values. Such a perspective – whether intentionally or unintentionally adopted – conceptualizes both the process and products of transcription as value-free and analytically inconsequential. Notably, this conceptualization is at odds with the underpinnings of most qualitative research (Grundy et al., 2003; Tilley, 2002). We argue that despite, and indeed because of, its predominance in practice, the perspective of axio-analytic neutrality should concern qualitative researchers, in its inherent abandonment of the idea of “investigator as instrument” – a deeply held tenet of the naturalistic research paradigm.

In practice, researchers adopting a perspective of axio-analytic neutrality explicitly or implicitly claim to be objective in all aspects of transcription designated with conceptually muddying descriptors. They may describe elements of the transcription process using distinctions that conflate the linguistic and practical meanings behind the term “transcription,” such as linguistic descriptors of graphic representation (i.e. “transcription,” “description,” “notation,” etc.); practical steps in the research process (i.e. “rough transcription,” “transcription,” “data de-identification,” etc.); or some mix of both. Importantly, these distinctions can lead researchers to generate problematic transformations of data that are severed both from the underpinnings of the research approach selected to collect and analyze those data and from elements of acoustic realization that reflect the authentic context from which the data were generated.

It should be noted that we do not purport that acoustic realization alone should be – or even can be – achieved for the purposes of inclusive representation in data analysis and presentation. In fact, we conceptualize acoustic realization as acoustic data represented just as they were articulated by their source – including verbatim spoken verbal components, paralinguistic components, and prosodic components (Flick, 2014) – and as an ideal; the pursuit of which would end in a failed asymptotic endeavor toward ‘perfect transformation of audio data’ (or else as a rendezvous with axio-analytic neutrality in frustration with the impossibility of the endeavor) (Witcher, 2010). Rather, we suggest that investigators preserve the acoustic realization of their data to the greatest extent that is possible, within the context of what is ‘meaningful’ for their selected research approach. In other words, in order to attend to inclusion in the transcription process, investigators should aim to produce graphic representations of authentic participant narratives in a manner aligned with tenets of naturalistic inquiry.

We propose an alternative perspective of ‘instrumental dimensionality,’ which rejects the notion of axio-analytic neutrality, reaffirms the belief that “investigator is instrument,” builds on the premise that transcription is inherently an act of interpretation (Grundy et al., 2003; Kvale, 1996; Lapadat & Lindsay, 1988; Tilley, 2002), and advances the notion that “transcription is investigation” as well. This perspective draws on the interactionist construct of dimensionality (Schatzman, 1991), which is defined as “turn[ing] language towards interrogative and analytic processes,” in order to ensure one’s interpretations are “situationally sufficient” (Schatzman, 1991, p. 309). In other words, instrumental dimensionality expands the researcher’s capacity as an instrument to attend to ‘what all is involved’ without privileging prior assumptions or primed schemas – such as the perspective of axio-analytic neutrality – which constrain that attention. Importantly, this notion applies to anyone involved in transcription activities including researchers themselves, third party paid professional transcriptionists, services that use artificial intelligence technology to generate transcripts, research assistants whose skills in research methodology may not be adequately developed or applied for the purposes of the inquiry at hand, and/or research participants engaging in member checking of their own transcripts. A perspective of instrumental dimensionality conceptualizes any individual engaged in transcription activities as an “investigator” in their own right and holds them responsible for the axiological and analytic interactions they have with the data as well as the resultant impact on the findings that are to be generated from the data. In elevating the process of transcription to the level of investigation, the perspective of instrumental dimensionality reveals the opportunity and responsibility that researchers have to address issues of inclusion related to transcription – no matter how or by whom initial transcripts were generated – carrying forward what are already common expectations for recruitment, sampling, and data collection activities.

Conceptually, the perspective of instrumental dimensionality attends to the explicit and implicit objectives of an investigator and research endeavor by conceptualizing the elements of transcription as four dimensions: (1) Perception of aspects of audio-recorded data, (2) Selection of aspects of perceived audio-recorded data, (3) Depiction of selected aspects of audio-recorded data into a graphic representation, and (4) Protection of participant data that may be identifiable. Because it elevates transcription as investigation and investigator as instrument, the perspective of instrumental dimensionality prioritizes the careful design of and critical reflection on decisions that guide which aspects of verbal, prosodic, or paralinguistic behavior are selected for data transformation, why such aspects are selected, and how such selected aspects are to be transformed.

Through the perspective of instrumental dimensionality, we propose ‘transcript alignment’ as an ethos and approach that investigators can use to make decisions about and throughout the dimensions of the transcription process: data ‘perception,’ ‘selection,’ ‘depiction,’ and ‘protection.’ Transcript alignment offers an approach to addressing these dimensions that differs from efforts commonly referred to as transcript cleaning (Frohwirth et al., 2023; Hecker & Kalpokas, n.d.), transcript checking (Ricks & Warren, 2021), or the familiar reviewing of transcripts for verbatim accuracy (Inoue, 2018) (where supposed “cleaners,” “checkers,” and “reviewers’’ inevitably and uncritically – even if unintentionally – introduce their own assumptions, preferences, or priorities into their transformations of the audio-recorded data). Rather, transcript alignment rejects the stance of axio-analytic neutrality and enables investigators to intentionally address dimensions of transcription that are necessary for inclusive representation.

Strategies for Inclusive Alignment of ‘What Has Been Transcribed’

Transcript alignment is intended to be adaptable and sensitive to the realities of research practice. It can be operationalized in a variety of ways, inclusive of a range of independent or combined, co-occurring or sequential, and linear or iterative variations. In other words, transcript alignment can be adopted as an approach for the entire transcription process or to augment outsourced transcription activities and products in order to generate “thick transcription.” Thus, using this approach, investigators can align or realign transcripts with the acoustic realization of participant narratives and relevant ontological, epistemological, and methodological considerations for their distinct research project.

Specifically, transcript alignment has the ability to address seven common pitfalls of axio-analytic neutrality that impact inclusive representation. Each of these pitfalls – missing data, mistaking data, tidying data, overlooking data, shrinking data, sanitizing data, and removing data – are detailed in Table 1, where they are mapped across the four dimensions of transcription. In this section, we draw on examples from the transcription of semi-structured individual interviews conducted with care partners of people with cancer for the Partners in Cancer Care Research (CaRe) Study (see Table 1) to detail how investigators can use transcript alignment to methodologically attend to inclusion through a priori(ish) transcription design and in vivo transcription decisions.

Table 1.

Illustrating the Impact of Transcription Pitfalls on Data Representation.

Dimension	Pitfall	Definition	Representation	Examples^a
Dimension	Pitfall	Definition	Representation	Unaligned	Aligned
Perception	Missing data	Not sufficiently perceiving verbal data	Represented by placeholders that indicate the presence of insufficiently perceived data in the transcript	Uh, you know, we, we grew from that, so uh, yeah, everything, everything changes you, if you pay attention. If you don’t pay attention, you just go in life [inaudible].	Uh, you know, we, we grew from that, so uh, yeah, everything, everything changes you, if you pay attention. If you don’t pay attention, you just go in life, not prepared.
	Mistaking data	Incorrectly perceiving verbal data	Inaccurately represented inaccurately in the transcript	So that’s a whole nother ballax.	So that’s a whole nother ball of wax.
Selection	Tidying data	Not selecting verbal data as meaningful	Not represented in the transcript	Interviewer:You mentioned the emotional weight of it. What have you been doing for yourself to deal with these emotions and care for yourself?	Interviewer:You mentioned like, the emotional weight of it. What have you been doing for yourself to deal with these --Participant:Mm. Mm. Mm. Mm. Mm. Mm.Interviewer:-- emotions and care for yourself?
	Overlooking data	Not selecting paralinguistic or prosodic features of verbal data as meaningful	Not represented in the transcript	I’ve loved gardens and so I went to [the garden], which I’ve been to a zillion times. I’d be a member if I lived there.	I’ve loved gardens and so I went to [the garden], [loudly and enthusiastically] which I’ve been to a zillion times. I’d be a member if I lived there.
Depiction	Shrinking data	Depicting paralinguistic or prosodic features of verbal data through description of the features, rather than transcription of the features	Incompletely represented by placeholders in the transcript	Anyway. At some point between now (laughter) and 2015, it had spread to her liver.	Anyway. At some point between now (exhaled laugh) and 2015, it had spread to her liver.
	Sanitizing data	Depicted verbal data through a reconstruction of the data, that conforms to academic, cultural, or journalistic norms pertaining to grammar, spelling, or syntax	Represented in a distorted form in the transcript	Because like I said, when somebody dies, I have a hard time giving comfort because what do you say to those types of losses?	‘Cause like I said, when somebody die, I have a hard time giving comfort because what do you say to those type losses?
Protection	Removing data	Selecting verbal data as containing information that may pose a risk to the confidentiality of participants or entities to which they refer; and subsequently omitting them from the transcript	Incompletely represented by placeholders that indicate the omission of data or the nature of data that were omitted in the transcript	I Guess he was sixty– he was my, age 64, when he came in and, and he died at 73, so he lived with us for almost 10 years.	I Guess he was [starts to say age], he was my age, [age], when he came in and, and he died at 73, so he lived with us for almost 10 years.

^aParticipants provided verbal consent to participate in the Partners in Cancer Care Research (CaRe) Study, which was determined to be exempt by the University of Pennsylvania Institutional Review Board.

A Priori(ish) Transcription Design

Transcript alignment guides investigators in avoiding preventable pitfalls by standardizing an approach to transcription rooted in inclusion. Using transcript alignment, investigators plan their design for transcription using an a priori(ish) approach, which assumes a continuous process of critical reflexivity rather than an inflexible prescription for all transcription decisions that are to be made. In this way, transcription design should be conceptualized as an initial methodological and ceremonial process of engaging in critical reflexivity to break the ground for continued reflection throughout the entirety of the transcription process. When preparing an a priori(ish) design for transcription, investigators must ask themselves how they will address the potential pitfalls of tidying, overlooking, shrinking, sanitizing, and de-identifying data by standardizing transcription decisions.

Investigators should be mindful of choices surrounding the representation of aspects of the audio-recording that are outside the scope of the participant’s verbal communication. If investigators choose not to represent these aspects, such as coughing or doors opening and closing, they overlook what may have meaning, such as the participants’ current health status or the level of privacy or social dimensions of their environment. Instead of choosing not to represent overlooked data, investigators can preserve these data by representing them as [background noise] and [coughs] or even [husband lets cat out of room] and [clears throat again].

Similarly, investigators must decide whether and how to replace potentially identifiable data from the audio-recording. If investigators choose to replace these aspects of data – such as names or cities – with placeholders merely indicating their absence from the transcript, investigators remove aspects of data that may be meaningful. Rather than denoting identifiable data as [removed] and [omitted], investigators can represent those data as [person] and [location] or even [younger daughter] and [common nickname for city of residence]. By aligning – rather than removing – de-identified data, an investigator preserves the meaning of data that would otherwise be lost, such as dimensions of a participant’s relationship to the removed data (e.g. “Even though [younger daughter] lived far away, [older son] was always by my side”) or how the removed data relates to other data in the participant’s narrative (e.g. “I guess the train’s not actually too bad… but it sometimes feels like a long trip from [common nickname for city of residence] to [nearby large city]”).

Additionally, investigators should also ensure that they intentionally consider the transformations of tidied data (e.g. whether to omit verbal utterances “Well, I mean I guess I always kind of knew that was a possibility,” or represent them “Well, I–… I mean I guess I always, um. kind of knew that was a possibility”) as well as shrunken data (e.g. whether to represent paralinguistic and prosodic features as [laughter] or [laughs]; [nervous laughter] or [sarcastic laugh]; or even “ha ha he ha” or “HA!”). These pitfalls can result in blunting, attenuating, or even obscuring the specificity of the narrative data participants have contributed; not unlike moving from higher levels of measurement to lower levels of measurement of quantitative data. Just as transforming data collected at the level of interval/ratio into categorical or binary level data determines how these data can be analyzed statistically, transcription decisions inherently transform the nature of qualitative data and thus (re)shape their interpretation and presentation.

Perhaps most importantly, investigators must consider how they will attend to data that do not conform to academic, cultural, or journalistic norms pertaining to grammar, spelling, or syntax. If investigators choose to reconstruct these data, they effectively sanitize the data through the conversion of meaningful aspects, such as speech patterns that are specific to a person’s socialized use of language, to an entirely different structure of speech. Investigators should examine whether their a priori(ish) transcription design decisions in fact contribute data that can be presented as evidence of participants’ authentic narratives to illustrate their subjective interpretations, or whether they result in “cleaned up” material that can tell a compelling story in a manner that is appealing to a particular audience. We acknowledge previous arguments made in favor of this type of sanitization in an effort to prevent stigmatization of participants (Kvale, 1996; McMullin, 2023). However, we assert that addressing socio-cultural linguistic stigmatization in this manner could in fact be counterproductive. Rather, normalizing the acoustic realization of inclusively represented participants more broadly would champion these efforts more effectively and systemically. Using transcript alignment to overcome the pitfall of sanitizing data, investigators prevent and correct the unethical and anti-inclusive transformations of data that stem from ableist, classist, nationalist, and racist processes of social oppression as they prepare their data for analysis and presentation.

Using transcript alignment to guide a priori(ish) transcription design, investigators can establish a plan to meaningfully represent participants’ narratives in data analysis and presentation from the outset of the transcription process. Investigators can err on the side of lay readability and risk losing the opportunity to note dimensions of meaning in analysis, or they can err on the side of acoustic realization and risk losing resonance for their intended audience. When conducting team science, they may opt to simplify their transcription process to reduce confusion or ensure consistency, or they may adopt a more complex approach that prioritizes subjective analytic diversity. Transcript alignment does not prescribe what transcription decisions to make. Rather, it urges investigators to acknowledge these choices as meaningful analytic decisions in themselves, to be made intentionally in alignment with the goals of the specific objectives of the research endeavor and the shared goal of inclusive representation.

In Vivo Transcription Decisions

Transcript alignment also guides investigators to leverage content expectation and context sensitization to avoid emergent pitfalls in the transcription process. Using transcript alignment, investigators carry forward the intentionality and critical reflexivity that they cultivated in their a priori(ish) design, into emergent in vivo transcription decisions that fall outside of or are in conflict with an initial design for transcription. In this way, investigators continually revise the design they had constructed a priori based on the various in vivo transcription decisions that they will consider and reconsider throughout the entire transcription process. Additionally, investigators need to address the unanticipated potential pitfalls of missing and mistaking data, through again asking themselves how they will determine which details of data are meaningful for inclusive representation in their analysis and presentation of data.

When data are insufficiently or entirely not perceived, investigators must determine how to address the meaning that is lost when data are not represented in the transcript. For example, investigators could indicate missing data – such as a few seconds of inaudible participant narrative in the middle of a participant’s description of their wife requesting a particular religious sacrament – by denoting their absence in the transcript (e.g. [inaudible] or […]). However, investigators might also explore whether the missed data result from an unpreventable issue of data perception related to limitations of human hearing or technological capabilities, or whether additional context sensitization may be able to sufficiently enhance the level of perception necessary to represent the data. If the latter, investigators can draw on various sources for perception (e.g. automated transcript service or one or more non-interviewer investigators listening to audio-recording). Additionally, investigators can seek out means of enhancing the level of context sensitization applied to the perception of the data by drawing on the interactional context of the data collection itself (e.g. having the interviewer listen to the audio-recording) or context related to the content of the participant narrative or (consulting the institution’s chaplain to listen to the portion of inaudible data and surrounding data represented in the transcript).

Investigators may similarly rely on enhanced context sensitization in order to address the pitfall of mistaking data, when transcribed data (e.g. “and my pan”) are not consistent with the corresponding data in the audio-recording (e.g. “an iPad”), which is particularly important in considering the positionality of the investigators engaged in transcription and the participants whose narratives they are transcribing: Are investigators sufficiently sensitive to the contexts that inform the technical and colloquial terms or phrases used by participants?

Transcript alignment offers direction for methodologically attending to inclusion through constructing an a priori(ish) transcription design as well as responding to emerging in vivo transcription considerations. Practically, investigators can attend to the pitfalls of axio-analytic neutrality in the transcription process by asking and re-asking, “Would this transformation of data offer the most meaning and greatest inclusive representation for data analysis and presentation?”

Discussion

Current practice of transcription in qualitative research is often conducted through a perspective of axio-analytic neutrality and thus disconnected from the naturalistic tenet of “investigator as instrument.” A perspective of instrumental dimensionality helps guide investigators in making transcription decisions that generate “thick transcription,” which promotes inclusive representation in data analysis and presentation. We introduce the concept and term of “transcript alignment” as a preferred alternative for how researchers can manage or refine their transcribed data. Specifically, transcript alignment helps investigators operationalize instrumental dimensionality through addressing the pitfalls of the transcription process via a priori(ish) design as well as in vivo decision making. Investigators carrying out the transcription process with transcript alignment are able to balance acoustic realization of participant narratives with the ontological, epistemological, and methodological underpinnings of their research.

Transcript aligning is distinct from “transcript checking” which lacks analytically informed intentionality, as well as “transcript cleaning” which implies that aspects of data must be tidied, sanitized, or otherwise washed (e.g. white-washed, class-washed, etc.). This conceptual and semantic evolution joins in movements toward linguistic justice in research such as replacing descriptors like “hard to reach” and “vulnerable” with “historically-underrepresented in research” or “structurally disadvantaged” (Boag-Munroe & Evangelou, 2012; Freimuth & Mettger, 1990; Holloway, 2011; Munari et al., 2021).

Transcript alignment offers investigators the opportunity to enhance the trustworthiness of the research findings. For example, transcript alignment addresses the scientific aspect identified by Guba as internal consistency and enhances the ‘credibility’ of qualitative findings (Guba, 1981). Transcript alignment ensures the data included for analysis are most representative of a participant’s narrative for the purpose of interpreting meaning by reducing the amount of missed, mistaken, omitted, reduced, converted, or removed elements of meaningful data in transcripts prepared for analysis activities. Similarly, ensuring meaningful elements are represented in the presentation of data enhances the ‘confirmability’ of findings, indicating that investigators’ interpretations are generated from the data. Detailing their intentionally developed a priori(ish) transcription designs as well as the in vivo transcription decisions they make throughout the transcription process, investigators also enhance the ‘dependability’ of their findings by functionally rendering an audit trail for data transcription. Finally, much like how generating “thick descriptions” enhances the ‘transferability’ of research findings, investigators using transcript alignment can generate “thick transcriptions” to ensure effective representation of personal, social, environmental, relational, colloquial, and cultural meaning in data.

Transcript alignment reconceptualizes the opportunities available across the continuum of a research project where researchers can enhance inclusive representation. These opportunities are increasingly important given current and longstanding dialogic debates about transcription practices centered on decisions about who and how to transcribe data (Easton et al., 2000; Oliver et al., 2005). Key debates include considerations in using artificial intelligence (see Jiang, et al., 2021; Point & Baruche, 2023Point & Baruch, 2023) and arguments for or against outsourcing transcription activities (see Bird, 2005). By using transcript alignment to generate “thick transcription” in accordance whatever approach they use for initial transcription, researchers can actualize their commitment to ethical, effective, or meaningful inclusion.

As with all endeavors to promote inclusion in research, transcript alignment requires resources including time, cognitive effort, personnel, and funding. Researchers should be equipped with sufficient resources to carry out their projects including the time and funding necessary to promote inclusion in research representation as well as research participation. To some extent, the limitations and realities of resources for conducting research may attenuate the extent to which investigators can feasibly apply the approach of transcript alignment. Fortunately, transcript alignment is intended to be flexible, enabling researchers to adapt how they apply it within the constraints or considerations of their research project, research approach, and resource availability.

The introduction of “transcript alignment” – as with any new concept or term – poses an opportunity for its assessment and adaptation across research contexts, methods, and teams.

We write from our own experiences developing, applying, and observing the impact of transcript alignment in a range of projects using interpretive methods and methods that focus on public dissemination. These projects have involved teams that include many investigators engaging in transcript alignment, representing nursing and interprofessional health science disciplines. Importantly, as is common in extant literature (Davidson, 2009), our experiences with transcript alignment have been with interview data that were collected, transcribed, analyzed, and presented in English. We anticipate that when researchers take up and apply transcript alignment in their work, they will surface further implications for methodological approaches to analysis and data presentation, team science structure and engagement, disciplinary perspectives, and non-English language considerations. Specifically, we expect transcript alignment may have different implications for projects applying non-interpretive methods, incorporating a wide range of data sources, or involving a variety of project team structures and disciplines. Using transcript alignment in concert language translation may introduce still other considerations or identify additional pitfalls of axio-analytic neutrality in the transcription process (Clark et al., 2017; Davidson, 2018; Karam et al., 2017). As transcript alignment becomes integrated and adopted into common research practice for the promotion of inclusive representation, work synthesizing its implications across contexts, methods, and teams can further refine and hopefully extend the concept.

While the intention of transcript alignment is to promote inclusive representation in research, it may be a positive externality for inclusive participation in research as well. Future research should empirically explore whether and how transcript alignment may enhance participants’ enrollment and engagement in qualitative studies. For example, potential participants may be more likely to choose to participate in research if they feel confident that their narratives will be valued. Similarly, participants may be more comfortable sharing their experiences if they feel confident that their voices will be authentically preserved. Signaling to potential participants that measures to ensure that their narratives as they’ve shared them will be intentionally captured and preserved – in addition to carefully protected, stored, and kept confidential – may increase their trust and participation in the research project.

Additionally, while here the concept of transcript alignment is discussed mainly in terms of inclusive representation, it may also offer important implications for the overall ethical conduct of qualitative research. Extant methodological innovations demonstrate that ethically-grounded processes (Quirke et al., 2022) and protocols (Whitney & Evered, 2022) have important implications for inclusion, exposing the interplay and intersection of inclusivity and ethics. Future exploration may uncover whether and how the use of transcript alignment, and the perspective of instrumental dimensionality, might also enhance the ethical nature of qualitative inquiry.

Conclusion

When participants in qualitative research have finished sharing their experiences, narratives, and stories, researchers must not abandon their obligation to continue promoting inclusion. Equipped with an approach to align the acoustic realization of participant narratives with the ontological, epistemological, and methodological underpinnings of the research at hand, investigators can methodologically address common pitfalls in the transcription process that threaten inclusive representation. Transcript alignment can adapt to the specific objectives, structures, and processes of a variety of research endeavors, enhancing trustworthiness of findings through a priori(ish) design and in vivo decisions. Integrating transcript alignment into their practice, qualitative researchers can reaffirm their positions as instruments of inquiry and generate “thick transcription,” ultimately carrying forward an ethos of participant inclusion throughout data analysis and presentation activities.

Footnotes

Acknowledgements

The authors would like to acknowledge Dr Sarah Kagan as well as the Partners in CaRe clinical partners at Abramson Cancer Center at Penn Medicine. We express our gratitude to Chaplain Amy Karriker at Stony Brook Medicine, all of the care partners who entrusted us with their stories, and the data engagement team members who have listened to and aligned those stories. The authors would like to acknowledge the Rita and Alex Hillman Foundation as well as the Stuart and Sherry Greene Foundation. We express our gratitude to Sarah H. Kagan, PhD, RN, FAAN, our clinical partners, all of the care partners who entrusted us with their stories, and the data engagement team members who have listened to and aligned those stories.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Rita & Alex Hillman Foundation [Advancing Early Research Opportunities (AERO) Grant] and the Stuart and Sherry Greene Foundation.

ORCID iD

Clare Whitney

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