Confirmatory factor analysis of the Czech translation of abbreviated form of the Revised Exsenck Personality Questionnaire (EPQR-A) among Czech students.
Bibliographic record
Abstract
There is increasing interest in the abbreviated form of the Eysenck Personality Questionnaire Revised (EPQR-A) as a research tool. A number of studies have begun to explore the psychometric properties of the EPQR-A among various cultural groups, including Australia, Canada, Israeli, Northern Ireland, South Africa and the USA using the original English language version of the questionnaire. More recently, work has also begun on examining the psychometric properties of translated versions of the EPQR-A, for example French. The aim of the present study was to evaluate the psychometric properties of a Czech translation of the EPQR-A in order to facilitate its use among Czech researchers. Data from a sample of Czech undergraduate university students were used. The dimensionality of the EPQR-A was examined. Using confirmatory factor analysis, evidence was found for the unidimensionality of the four EPQR-A sub-scales of extraversion, neuroticism, psychoticism and the lie scale. These results are consistent with those of previous research with the English and French versions of the EPQR-A. It is concluded that the Czech translation of the EPQR-A can be recommended for further use. Of primary importance is the replication of the present findings among other groups.
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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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".