What were They Thinking? Using Cognitive Interviewing to Examine the Validity of Self-Reported Epistemic Beliefs
Bibliographic record
Abstract
We employed cognitive interviewing with a sample of secondary, college, undergraduate and graduate students to examine the cognitive validity of a popular epistemic beliefs self-report measure, the Discipline-Focused Epistemological Beliefs Questionnaire [DFEBQ] (Hofer, 2000). In addition, we examined cognitive validity across two domains. Analyses of interviews revealed that cognitive validity was good, wherein students ’ responses were typically within an expected range of interpretations. However, students ’ interpretations of items were not always consistent with researchers ’ intended meanings, interpretations sometimes differed across domains, and that the response option “3 ” as a neutral response was not always used as intended. To improve validity of self-report measures of epistemic beliefs more generally, we recommend that explicit anchors are used, such as “mathematician ” instead of “expert, ” and that definitions of the dimensions are presented to respondents to ensure interpretations align with researchers ’ intended meanings. We end with broader methodological implications.
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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 | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".