Exploratory Factor Analysis (EFA) and Reliability Analysis of Financial Literacy Instrument Among Trainee Teachers
Bibliographic record
Abstract
This study aims to evaluate the reliability and validity of tools designed to assess financial Literacy (FL) of trainee teachers. A structured questionnaire was utilized to collect data from a randomly selected sample of 100 trainee teachers from the Teacher Education Institute (IPG). EFA techniques were used by deploying IBM-SPSS version 22.0. The EFA results demonstrate four components of FL with the eigenvalue exceeding 1.0 for each construct: financial knowledge 1, financial knowledge 2, financial behaviour and financial attitude. However, through the EFA procedure, the inquiry established that only 38 items, each with weighting factors exceeding 0.50, were retained and considered suitable for assessing the FL construct. The reliability of construct knowledge 1 was 0.916, construct knowledge 2 was 0.947, construct behaviour was 0.958, and construct attitude was 0.962. This study affirms the validity and reliability of the dimensions guiding FL measurement. This research contributes to the body of knowledge on FL among pre-service teachers who would serve as mentors to students after graduating. It also intends to keep up with its ongoing research projects and pursue additional financial studies in the future.
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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 | 0.017 | 0.046 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".