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Record W2278606702 · doi:10.1521/pedi_2016_30_233

The Factor Structure of the Schizotypal Personality Questionnaire in Undergraduate and Community Samples

2016· article· en· W2278606702 on OpenAlexaff
Lisa C. Zhang, Colleen A. Brenner

Bibliographic record

VenueJournal of Personality Disorders · 2016
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyGeneralizability theoryConfirmatory factor analysisExploratory factor analysisSample (material)Schizotypal personality disorderPersonalityStructural equation modelingPsychometricsDevelopmental psychologyClinical psychologySocial psychologyStatistics

Abstract

fetched live from OpenAlex

The prevailing theoretical model of the Schizotypal Personality Questionnaire (SPQ) is a three-factor model based on subscale-level analyses. However, recent item-level factor analyses of the SPQ suggest a four- or five-factor model. To examine the factor structure of the SPQ and how this structure may differ between undergraduate and community samples, the authors conducted exploratory and confirmatory item-level factor analyses of this measure on undergraduate (N = 1,850) and community participants (N = 1,464). A clear three-factor solution was found in the community sample, whereas a somewhat equivocal four-factor solution was found in the undergraduate sample. Both structures displayed gender invariance. This is the first study to address the issues of undergraduate sample generalizability and gender invariance in an item-level exploratory factor analysis of the SPQ. Given the disparate findings in the samples, this study indicates the importance of using both community and undergraduate samples when examining the factor structure of the SPQ.

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)0.007
metaresearch head score (Gemma)0.014
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 score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.309
Teacher spread0.285 · 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

Citations47
Published2016
Admission routes1
Has abstractyes

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