Interaction in qualitative questionnaires: From self-report to intersubjective achievement
Bibliographic record
Abstract
Abstract Survey questionnaires are among the most widely-used research methods in applied linguistics, adopted for everything from large-scale quantitative studies measuring social-psychological variables to qualitative studies that solicit participant views on a range of different topics. Despite the variety of purposes that survey questionnaires are used for, the most common approaches to analysis of the data they yield involve content analysis using descriptive or inferential statistics and/or enumeration of emergent themes. The study reported in this article conceives of questionnaire data in notably different terms: as occasioned (conditionally-relevant responses sequentially-projected by a question), recipient-designed (devised for the research context and researcher), and thus, as thoroughly interactional phenomena (Drew 2006; Sacks 1992). The study examines the identity construction of French as a second language (FSL) teachers on a professional development sojourn in France, drawing on a data-set in part comprised of participants’ open-ended responses to a 48-item questionnaire concerning whether their “confidence as French language teachers” increased as a result of their involvement in the sojourn. However, rather than conceiving of participants’ answers as revelations of changes in their interior states, the study draws on insights from conversation analysis and membership categorization analysis to examine how “confidence” was recruited as a discursive resource to “do being” a particular kind of L2 French teacher. We demonstrate how the presence and problematics of a (French) native-speaker archetype for the FSL teachers was in part formulated through close analytic attention to both the sequential and categorial features of researcher/respondent interactions occasioned by one open-ended response item in the questionnaire. This alternative approach to analyzing questionnaire data offers important insights about L2 teacher identity. It also addresses more fundamental questions concerning the discursive and interactional basis of ostensibly non-interactional research methods like survey questionnaires. Additionally, the insistence on an explicit methodological framing of the research process promotes greater theoretical and methodological consistency, and of particular importance, a significantly expanded conception of and accounting for researcher reflexivity.
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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 | 0.179 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".