Psychometric Analysis of Economics Achievement Test Using Item Response Theory
Bibliographic record
Abstract
This study determined the psychometric properties of the Economics Achievement Test (EAT) using Item Response Theory (IRT). Two popular IRT models namely, one-parameter logistics (1PL) and two-parameter logistics (2PL) models were utilized. The researcher adopted instrumentation research design. Four research questions and two hypotheses were formulated to guide the study. The population size is five thousand, three hundred and sixty-two (5,362) from thirty-seven (37) schools. The sample for the study was 1,180 senior secondary school students (SSS3) drawn using multi-stage sampling procedure. The 1,180 students were stratified according to gender which resulted to 885 females and 295 males. The instrument for the study consisted of 50 multiple-choice test items on the economics achievement test, developed by the researchers. Reliability and validity for each item and for the whole test were established according to the one-parameter and two-parameter logistic models. Research question one was answered using 1PLM, while research questions two and three were answered using 2PLM IRT model. Hypothesis one was tested using t-test analysis of difference between the difficulty parameters estimated using 1PLM and 2PLM while hypothesis two was tested using Chi-square. The finding of the study revealed significant difference between the item difficulties estimated using 1PLM and 2PLM. Also the observed scores of the testees on the test items fit the 1PL 2PL models.
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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 | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".