Linking Test-Taking Process to Performance Through Mixed-Effects Regression Models: A Response Process–Based Validation Study
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
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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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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