Cross-cultural examination of the Big Five Personality Trait Short Questionnaire: Measurement invariance testing and associations with mental health
Bibliographic record
Abstract
The present study examined the measurement invariance of the Big Five Personality Trait Short Questionnaire (BFPTSQ) across language (Spanish and English), Spanish-speaking country of origin (Argentina and Spain) and gender groups (female and male). Evidence of criterion-related validity was examined via associations (i.e., correlations) between the BFPTSQ domains and a wide variety of mental health outcomes. College students (n = 2158) from the USA (n = 1117 [63.21% female]), Argentina (n = 353 [65.72% female]) and Spain (n = 688 [66.86% female]) completed an online survey. Of the tested models, an Exploratory Structural Equation Model (ESEM) fit the data best. Multigroup ESEM and ESEM-within-CFA generally supported the measurement invariance of the questionnaire across groups. Internalizing symptomatology, rumination and low happiness were related mainly to low emotional stability across countries, while low agreeableness and low conscientiousness were related chiefly to externalizing symptomology (i.e., antisocial behavior and drug outcomes). Some correlational differences arose across countries and are discussed. Our findings generally support the BFPTSQ as an adequate measure to assess the Big Five personality domains in Spanish- and English-speaking young adults.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".