A New Approach to Desirable Responding: Multidimensional Item Response Model of Overclaiming Data
Bibliographic record
Abstract
A variety of approaches have been presented for assessing desirable responding in self-report measures. Among them, the overclaiming technique asks respondents to rate their familiarity with a large set of real and nonexistent items (foils). The application of signal detection formulas to the endorsement rates of real items and foils yields indices of (a) knowledge accuracy and (b) knowledge bias. This overclaiming technique reflects both cognitive ability and personality. Here, we develop an alternative measurement model based on multidimensional item response theory (MIRT). We report three studies demonstrating this new model’s capacity to analyze overclaiming data. First, a simulation study illustrates that MIRT and signal detection theory yield comparable indices of accuracy and bias—although MIRT provides important additional information. Two empirical examples—one based on mathematical terms and one based on Chinese idioms—are then elaborated. Together, they demonstrate the utility of this new approach for group comparisons and item selection. The implications of this research are illustrated and discussed.
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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 | 0.069 | 0.148 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".