Using Cognitive Interviewing and Behavioral Coding to Determine Measurement Equivalence across Linguistic and Cultural Groups
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The present study aimed to examine and compare results from two questionnaire pretesting methods (i.e., behavioral coding and cognitive interviewing) in order to assess systematic measurement bias in survey questions for adult smokers across six countries (USA, Australia, Uruguay, Mexico, Malaysia and Thailand). Protocol development and translation involved multiple bilingual partners in each linguistic/cultural group. The study was conducted with convenience samples of 20 adult smokers in each country. Behavioral coding and cognitive interviewing methods produced similar conclusions regarding measurement bias for some questions; however, cognitive interviewing was more likely to identify potential response errors than behavioral coding. Coordinated survey qualitative pretesting (or post-survey evaluation) is feasible across cultural groups, and can provide important information on comprehension and comparability. Cognitive interviewing appears a more robust technique than behavioral coding, although combinations of the two might be even better.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it