Intermittent faking of personality profiles in high-stakes assessments: A grade of membership analysis.
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
in the "real" and "ideal" profiles are defined. This approach overcomes the limitation of existing psychometric models that assume faking behavior to be consistent across test items. To estimate the proposed faking-as-grade-of-membership (F-GoM) model, two-level factor mixture analysis is used, with two latent classes at the response (within) level, allowing grade of membership in "real" and "ideal" profiles, each underpinned by its own factor structure, at the person (between) level. For collected data, units of analysis can be item or scale scores, with the latter enabling analysis of questionnaires with many measured scales. The performance of the F-GoM model is evaluated in a simulation study, and compared against existing methods for statistical control of faking in an empirical application using archival recruitment data, which supported the validity of latent factors and classes assumed by the model using multiple control variables. The proposed approach is particularly useful for high-stakes assessment data and can be implemented with standard software packages. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.044 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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