Analyzing the Construct Validity of Organizational Citizenship Behavior Scale Using Confirmatory Factor Analysis with Indonesian Samples
Bibliographic record
Abstract
The success of an organization is influenced by employees who not only perform their job, but also contribute their time and energy to provide assistance beside the formal obligations to the organization. This behavior is referred to as organizational citizenship behavior. This study was conducted to analyze the validity of the Organizational Citizenship Behavior Scale. Therefore, there were two main objectives in this study, namely to examine the construct validity of the Organizational Citizenship Behavior Scale using confirmatory factor analysis (CFA) and to assess the reliability of the scale. Organizational citizenship behavior was measured using three dimensions and they were helping behavior, civic virtue and sportsmanship. The study was conducted on 11 religious schools located in North, East, South, Middle and West of South Sulawesi, Indonesia. The schools consisted of 339 teachers. However, after examination of normality data, only 208 respondents were used as samples. The results of the study showed that the hypothesized model did not have a good fit to the data with Chi Square =190.168, p < 0.0001, CFI = 0.89, GFI = 0.92, TLI = 0.87, RMSEA = 0.078. Thus, this model has to be revised. The results of the revised model showed a better fit with Chi Square =118.335, p < 0.0001, CFI = 0.93, GFI = 0.92, TLI = 0.90, RMSEA = 0.07. The findings were discussed based on the suitability of the Organizational Citizenship Behavior Scale as a valid measure within Indonesian context.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".