Meta-Analysis of Coefficient Alpha: Empirical Demonstration Using English Language Teaching Reflection Inventory
Bibliographic record
Abstract
Cronbach’s alpha is a reliability coefficient commonly reported in second language (L2) and English language teaching (ELT) studies. The alpha coefficient provides information on the internal consistency of a measuring instrument. The reported alpha coefficients are obtained from, and apply only to, the research sample. However, the estimation of the alpha coefficient for the population has not received the attention of L2 and ELT researchers. This study aims to provide an overview of the alpha coefficient estimation procedure of a measuring instrument for a population with the reliability generalization method, commonly known as alpha coefficient meta-analysis. An example alpha coefficient meta-analysis study—using empirical data of the 29-item English Language Teaching Reflection Inventory (ELTRI) from 27 independent study samples—was conducted to provide an overview of the procedure for applying the method and the information that needs to be reported from the results of the analysis. The results of the study using a random-effect model show that the population alpha of ELTRI was 0.872, indicating excellent reliability; this is followed by application of a mixed-effect model that shows that article type and means of teaching experience significantly impacted ELTRI reliability. Implications for future research are discussed.
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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 | 0.171 | 0.402 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.044 |
| Bibliometrics | 0.019 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".