Linking Test-Taking Process to Performance Through Mixed-Effects Regression Models: A Response Process–Based Validation Study
Bibliographic record
Abstract
Answering the call for response process–based validation, this study shows how researchers can evaluate validity evidence of test scores based on the mental “processes” test-takers use, rather than based on correlations with other “outcome” measures. The proposed methods for process-based validation studies are demonstrated using a sample of 189 adults who took two listening comprehension tasks. Immediately after completing each task, the test-takers filled out a 10-item survey to reflect on the mental processes involved in reaching their answers. These 10 process variables attempted to capture five desired and five undesired response processes in answering multiple-choice listening comprehension questions. We investigated the relationships between these process variables and the binary outcome of item score (correct vs. incorrect) using mixed-effects logistic regression models, and showed how the results could provide validity evidence (or lack thereof). By doing so, we offer an alternative approach to study response process and test performance and encourage more process-based validation studies.
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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 | 0.271 | 0.509 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".