Challenging response latencies in faking detection: The case of few items and no warnings
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
AbstractFaking detection is an ongoing challenge in psychological assessment. A notable approach for detecting fakers involves the inspection of response latencies and is based on the congruence model of faking. According to this model, respondents who fake good will provide favorable responses (i.e., congruent answers) faster than they provide unfavorable (i.e., incongruent) responses. Although the model has been validated in various experimental faking studies, to date, research supporting the congruence model has focused on scales with large numbers of items. Furthermore, in this previous research, fakers have usually been warned that faking could be detected. In view of the trend to use increasingly shorter scales in assessment, it becomes important to investigate whether the congruence model also applies to self-report measures with small numbers of items. In addition, it is unclear whether warning participants about faking detection is necessary for a successful application of the congruence model. To address these issues, we reanalyzed data sets of two studies that investigated faking good and faking bad on extraversion (n = 255) and need for cognition (n = 146) scales. Reanalyses demonstrated that having only a few items per scale and not warning participants represent a challenge for the congruence model. The congruence model of faking was only partly confirmed under such conditions. Although faking good on extraversion was associated with the expected longer latencies for incongruent answers, all other conditions remained nonsignificant. Thus, properties of the measurement and properties of the procedure affect the successful application of the congruence model.
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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 | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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