Bibliographic record
Abstract
Exploratory factor analysis (EFA) is one of the standard analytical techniques used in EFL studies. It helps researchers to identify hidden constructs and check whether the survey instruments capture learners’ beliefs, feelings and attitudes in EFL classrooms. However, many struggle with the application of EFA due to a lack of clear rationale for each step and practical advice on how to conduct the analysis. This article therefore provides a comprehensive guide to EFA, focusing specifically on the EFL context. To make implementation systematic, the 7Rs steps—Reflecting, Recruiting, Reviewing, Reducing, Retaining, Rotating and Renaming—are presented as a step-by-step guide to performing the technique. Using these steps, researchers will be able to perform EFA in a way that is consistent with the theory being tested while meeting basic statistical standards. Readers are thus provided with practical tools for creating questionnaires that are not only valid and reliable but can also be used in EFL contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.119 | 0.243 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.032 | 0.021 |
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".