Assessing Negative Response Bias Using Self-Report Measures: New Articles, New Issues
Bibliographic record
Abstract
Abstract In psychological injury and related forensic evaluations, two types of tests are commonly used to assess Negative Response Bias (NRB): Symptom Validity Tests (SVTs) and Performance Validity Tests (PVTs). SVTs assess the credibility of self-reported symptoms, whereas PVTs assess the credibility of observed performance on cognitive tasks. Compared to the large and ever-growing number of published PVTs, there are still relatively few validated self-report SVTs available to professionals for assessing symptom validity. In addition, while several studies have examined how to combine and integrate the results of multiple independent PVTs, there are few studies to date that have addressed the combination and integration of information obtained from multiple self-report SVTs. The Special Issue ofPsychological Injury and Lawintroduced in this article aims to help fill these gaps in the literature by providing readers with detailed information about the convergent and incremental validity, strengths and weaknesses, and applicability of a number of selected measures of NRB under different conditions and in different assessment contexts. Each of the articles in this Special Issue focuses on a particular self-report SVT or set of SVTs and summarizes their conditions of use, strengths, weaknesses, and possible cut scores and relative hit rates. Here, we review the psychometric properties of the 19 selected SVTs and discuss their advantages and disadvantages. In addition, we make tentative proposals for the field to consider regarding the number of SVTs to be used in an assessment, the number of SVT failures required to invalidate test results, and the issue of redundancy when selecting multiple SVTs for an assessment.
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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 | 0.054 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".