Confirmatory Factor Analysis of Self-efficacy Instrument among Special Education Teachers
Bibliographic record
Abstract
Self-efficacy is pivotal in education because of its influence on an individual’s personality and attitudes. It stems from the person’s belief in his or her capabilities to achieve a desired performance level and is especially important to special education teachers who deal with continual teaching challenges. Thus, to contribute to the increasingly challenging field of special education in the 21st century, this study aims to measure the validity and reliability of a self-efficacy instrument among teachers of the Integrated Special Education Program for Learning Difficulties (ISEPLD). Three subconstructs were measured, namely 1) student engagement, 2) instructional strategies, and 3) classroom management. AMOS software program version 18 was used for the data analysis and values from Comparative Fit Index, Tucker Lewis Index and RMSEA were used to retain and correlate items. An instrument with three subconstructs containing 15 items of nine-point scale was tested in this study. The instrument was administered to 500 participants across Malaysia using the proportional stratified random sampling and by the means of Confirmatory Factor Analysis, the study has confirmed that the data corresponded to the model. Therefore, it is proposed that the 15-item instrument developed from the three subconstructs can be used in measuring self-efficacy among teachers of ISEPLD in Malaysia.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".