Bayesian construct validation leveraging expert knowledge for questionnaire instruments used in primary care research and practice
Bibliographic record
Abstract
Confirmatory Factor Analysis (CFA) is the standard statistical approach for assessing construct validity of questionnaire instruments using representative survey samples.Conventional CFA, however, does not take external information such as evidence from the literature, data from preliminary studies or expert knowledge into account.We propose a Bayesian inference framework for estimating item-domain correlations (i.e., 'factor loadings') , the target parameters of CFA, that enables incorporation of prior information obtained from domain experts.We conducted a large-scale Monte-Carlo simulation study to illustrate the performance of the proposed approach and provide recommendations regarding the number of experts to be involved.Our findings suggest that incorporating expert prior information increases estimation efficiency.Posterior intervals of item-domain correlations are sensitive regarding expert input, even it the number of experts is relatively low compared to the empirical sample size. CONSTRUCT VALIDATION OF THE MEA-D QUESTIONNAIRE -UTILITY OF BAYESIAN CONFIRMATORY FACTOR ANALYSIS (MANUSCRIPT 4) lidity i.e. item-domain correlations, when compared to conventional validation approaches.Conclusion The proposed Bayesian approach for construct validation enables more comprehensive, i.e. stakeholder-inclusive, efficient and resource-saving developments of questionnaire instruments for evidence-based practice and research.
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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 | 0.420 | 0.618 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".