Factor Analysis of Development Identity for Graduates in the Faculty of Forestry, Kasetsart University
Bibliographic record
Abstract
This study aims to analyze the identity components of graduates from the Faculty of Forestry at Kasetsart University using exploratory factor analysis and confirmatory factor analysis. Two sample groups were involved: Group 1 (400 individuals) for exploratory factor analysis and Group 2 (710 individuals) for confirmatory factor analysis. Both groups responded to a 5-point Likert scale questionnaire. The exploratory factor analysis results showed a strong Kaiser-Meyer-Olkin measure of .930 and Bartlett's Test of Sphericity with a chi-square value of 10437.275, 1770 degrees of freedom, and a significance level of .0001. Nine identity components were identified through factor rotation using the Varimax method: teamwork, commitment to task completion, application of knowledge, integrity towards oneself and others, adaptability to the environment, humility, willingness to help and share, non-egoism, and perseverance. The confirmatory factor analysis confirmed a good fit of the model to the observed data, indicated by indices such as p = .148, χ²/df = 1.314, RMSEA = .02, GFI = .991, AGFI = .98, CFI = .998, and RMR = .097. Notably, "teamwork" had the highest standardized weight of .825, followed by "willingness to help and share" (.749) and "commitment to task completion" (.739). The standardized weights ranged from .825 to .636. All variables had statistically significant p-values, and the coefficients of determination (R²) ranged from .680 to .405 when measured by Square Multiple Correlation.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".