Validity and reliability of digital self-efficacy scale in Iranian sample
Bibliographic record
Abstract
Background and Aim: The digital self-efficacy scale was prepared and developed in 2022 by Ulfert and Schmidt in order to measure the subject's competencies in using digital technologies. This scale consists of 25 items and measures digital self-efficacy in five dimensions, which are: 1) information and data literacy; 2) collaboration and communication; 3) production of digital content; 4) security; 5) Problem-solving. The purpose of the present study was to investigate the validity and reliability of the digital self-efficacy scale in the Iranian sample. Methods: The current research was applied in terms of purpose and validation research. The current research was a quantitative study in which validity and reliability determination methods were used to validate the digital self-efficacy scale. The statistical population of the present study included all students of Islamic Azad University, South Tehran branch, who were studying in the 2022-2023 academic year. The statistical sample of this research included 500 students who were selected by the available sampling method and completed the questionnaire. In order to statistically analyze the data, confirmatory factor analysis, exploratory factor analysis, KMO and Bartlett test, Cronbach's alpha, combined reliability coefficient (CR) and Pearson correlation coefficient were used. Statistical analysis of data was done with SPSS software version 23. Results: After confirming the face validity, 5 factors with a greater eigenvalue of 1 were identified through exploratory factor analysis, so that the 5 factors obtained in total were able to explain 70.55% of digital self-efficacy. Confirmatory factor analysis also showed the significance of the items of all 5 factors. Moreover, the coefficients obtained for convergent validity, test-retest reliability, Cronbach's alpha and composite reliability were all higher than 0.70, which indicates the appropriate reliability of this questionnaire. Conclusion: It can be concluded that the digital self-efficacy scale has good validity and reliability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".