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Record W1991381875 · doi:10.1037/a0012952

Validity of the MMPI-2-RF (restructured form) L-r and K-r scales in detecting underreporting in clinical and nonclinical samples.

2008· article· en· W1991381875 on OpenAlexaff
Martin Sellbom, R. Michael Bagby

Bibliographic record

VenuePsychological Assessment · 2008
Typearticle
Languageen
FieldPsychology
TopicPsychological Testing and Assessment
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsMinnesota Multiphasic Personality InventoryPsychologyPersonality testPersonalityTest validityTest (biology)Clinical psychologyPsychometricsSocial psychology

Abstract

fetched live from OpenAlex

In the current investigation, the authors examined the validity of the L-r and K-r scales on the recently developed Minnesota Multiphasic Personality Inventory-2-Restructured Form (MMPI-2-RF; Y. S. Ben-Porath & A. Tellegen, in press) in measuring underreported response bias. Three archival samples previously collected for examining MMPI-2 validity scales were reanalyzed in 2 studies. In Study 1 L-r and K-r significantly differentiated 2 groups of participants (patients with schizophrenia and university students) who had been instructed to underreport on the MMPI-2 from participants who took the test under standard instructions. L-r and K-r also added incremental predictive variance to one another in differentiating these groups. In Study 2 a similar set of outcomes emerged through the use of a differential prevalence design in which L-r and K-r significantly differentiated a group of child custody litigants who were administered the MMPI-2 from university students taking the test under standard instructions.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.031
metaresearch head score (Gemma)0.073
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.162

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.073
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.306
GPT teacher head0.504
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations68
Published2008
Admission routes1
Has abstractyes

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