Editing Questionnaire Items using the Delphi Method: Integrating Qualitative and Quantitative Methods
Bibliographic record
Abstract
The authors of this article argue that instrument development studies can be situated within constructive realism, an intermediary ontology between the representative constructed items and objectivist goals of measurement. The Delphi method uses constructionist processes by gathering expert opinions about the variable they wish to measure. Despite its popularity, little pragmatic guidance exists for researchers using the method in instrument development studies and authors of instrument development studies rarely describe the strategies used to decide when to keep, edit, or delete items when merging both quantitative and qualitative assessments of the developing items. This article, therefore, describes mixed methods decision-making strategies as they were implemented during the Delphi phase of the Situated Academic Writing Self-Efficacy Scale (SAWSES) validation project. Five case-study items are presented to highlight the strategies used to integrate the qualitative and quantitative data provided by a Delphi panel. Data were integrated by categorizing the quantitative data as having strong evidence for inclusion, deletion, or neutrality. Concurrently, qualitative data were integrated with the quantitative data by contemplating panellists’ individual and collective opinions about item value and wording, as well as stream-of-consciousness reflections from panellists about the nature of writing self-efficacy. This article contributes to the literature by describing, through use of specific examples, how qualitative and quantitative data can be effectively integrated to make decisions in mixed methods instrument development research and should be useful for all beginning and seasoned researchers attempting tool development.
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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 | 0.256 | 0.282 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".