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Record W2100810482 · doi:10.1027/1866-5888/a000016

Speeding Personality Measures to Reduce Faking

2010· article· en· W2100810482 on OpenAlexaff
Shawn Komar, Jennifer A. Komar, Chet Robie, Simon Taggar

Bibliographic record

VenueJournal of Personnel Psychology · 2010
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsWilfrid Laurier UniversityUniversity of Waterloo
Fundersnot available
KeywordsPsychologyPersonalitySocial psychologyPersonnel selectionConstraint (computer-aided design)CognitionSelection (genetic algorithm)Impression managementApplied psychologyComputer scienceManagement

Abstract

fetched live from OpenAlex

The purpose of the present study was to examine the effects of imposing a time constraint on respondents completing the Big Five personality Inventory (John & Srivastava, 1999) based on a self-regulatory model of response distortion. A completely crossed 2 × 2 experimental design was used in which instructions (neutral standard instruction or a job applicant instruction) and speed (with or without a time limit) were manipulated. While speeding personality tests reduced socially desirable responding, consistent with resource allocation theory (Ackerman, 1986), this effect was only seen in low cognitive ability individuals. Speeding was not perceived negatively by participants. This study is the first to find any evidence of a possible influence of speed on impression management and suggests that manipulating time limits for completing personality measures in selection is not advised at the present time as it is likely to have the unintended effect of removing applicants with high cognitive ability from the applicant pool.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.107
GPT teacher head0.428
Teacher spread0.321 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations28
Published2010
Admission routes1
Has abstractyes

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