Engaging the Senses in Qualitative Research via Multimodal Coding: Triangulating Transcript, Audio, and Video Data in a Study With Sexual and Gender Minority Youth
Bibliographic record
Abstract
The variety of formats in which qualitative data may be collected have been explored within the methodological literature. Yet, the multiple options for coding these data formats have not been comprehensively detailed. While transcript analysis is widely used across disciplines, it may have limitations—particularly for research involving marginalized populations. This paper presents a multimodal coding approach as a methodological innovation for triangulating three data formats (transcript, audio, and video), detailed through the application of this analytic approach during a qualitative study exploring media engagement with sexual and gender minority youth (SGMY). Nineteen semi-structured interviews with SGMY were filmed and transcribed. Nine independent coders then utilized the innovative multimodal approach to code the three data formats using a constructivist grounded theory framework. Some codes were similar across modalities, such as those related to safety issues and finding identity and community through media. Others differed between modalities, such as those related to participant affect, perceived contradictions, discrepancies between verbal statements and body language, level of comfort and engagement, and distress when discussing traumatic experiences. Video coding captured the broadest range of emotions and experiences from marginalized youth, while transcripts provided the most straightforward form of data for coding. Multimodal coding may be applicable across qualitative approaches to enrich analyses and account for potential biases, thereby enhancing analytical lenses in qualitative inquiry. Methodological strategies for coding and integrating data types are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.080 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".