Accuracy of Measurement in the Classical and the Modern Test Theory: An Empirical Study on a Children Intelligence Test
Bibliographic record
Abstract
The study aimed to compare the accuracy of assessing participants’ ability by the significance of standard error of the Classical Test Theory (CTT) and standard error of estimation of the Modern Test Theory (MTT) represented by the Two-Parameter Logistic Model (2PLM). It also aimed to compare item difficulty and arrangement in the two theories using Attriri’s Intelligence Scale for Children and a sample of 2674 students from the Republic of Yemen. Descriptive statistics (means and standard deviations), exploratory factor analysis and one-sample t-test were used for statistical treatment of data. Statistical treatment was performed by the IBM SPSS V. 20 and the Bilog-Mg3 programs. It was found that MTT represented by the 2PLM is more accurate than CTT in assessing participants’ abilities by standard error. Furthermore, the calibration of items by difficulty and the arrangement of participants’ abilities in the two theories proved to be different. Based on the study results, the researcher recommends (a) basing the development of psychological tests on the psychometric characteristics extracted according to MTT, (b) training professionals in measurement and evaluation in the use of analysis programs to extract item and ability parameters according to the different models of MTT (item response theory), and (c) making available the programs needed for the use of MTT in testing, e.g., Xcalibre and Rumm 2030 & Bilog-Mg3.
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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 | 0.037 | 0.171 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".