Reliability and validity of the Chinese Version of Cognitive Style Questionnaire in college students
Bibliographic record
Abstract
Objective:To develop a Chinese version of the Cognitive Style Questionnaire(CSQ)and evaluate its reliability and the validity.Methods:The original version of the CSQ was translated into Chinese version and administered to 647 Chinese college students.Totally 130 students were re-tested one month later.The CSQ was compared with the Dysfunctional Attitude Scale(DAS) to analyze the relation to depression symptom.Results:The internal consistency reliability of the total scale was 0.95,and those of the four subscales ranged from 0.71 to 0.80.The test-retest reliability coefficient was 0.82.The mean inter-item correlation coefficient for the factors ranged from 0.24 to 0.42.The factors loadings ranged from 0.47 to 0.98.The results of confirmatory factor analyses(GFI=0.90,CFI=0.92,IFI=0.91,RMSEA=0.07) indicated that the four-factor structure of the CSQ was suitable for the Chinese sample.In comparison the DAS to correlation of depression indicates that the CSQ always made a unique,significant contribution in correlativity variance to the depression(β=0.22,0.20;Ps0.01),even after variance attributable to the DAS(β=0.15,P0.01) had been explained.Conclusion:It suggests that the Chinese version of the Cognitive Style Questionnaire(CSQ) has good reliability and validity.Comparing with DAS,the CSQ might have better correlation with depression.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".