Exploratory Factor Analysis for Technostress Among Primary School Teachers
Bibliographic record
Abstract
The aim of this study was to explore and develop instruments for measuring technostress among primary school teachers in Malaysia. The researchers adapted 28 items from previous study and modified the statement to suit current study. Then the items statement was translated into Malay language to suit the local setting. The instruments underwent expert verification for content validity, face validity and criterion validity. The study amended the item statement accordingly based experts’ comment. For pilot study, some 106 school-teachers were selected randomly for data collection. The data were explored and validated through exploratory factor analysis (EFA) procedure. The results of the EFA procedure revealed the 28 items fall into five underlying components. The components are renamed as technical oriented, profession oriented, social oriented, personal oriented and teaching-learning process oriented. The items under these five components explained 71.1% of the total variance. The internal reliability of the technostress construct was 0.95. In addition to adding to the current body of knowledge, the findings provide a reliable source of information for researchers and professional practitioners interested in future research in technostress for educators, particularly primary school teachers.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".