Rasch Calibration and Differential Item Functioning (DIF) Analysis of the Indonesian National Assessment Program-Language (INAP-L)
Bibliographic record
Abstract
This study aimed to evaluate the psychometric properties of the INAP-L instrument by applying the Rasch model. The psychometric evaluation includes item calibration and differential item functioning (DIF) assessment. Participants in this study were 6153 high school students (2326 boys and 3827 girls) aged 14-19 years (mean age = 15.76, SD = 0.78) from 280 schools spread across 34 provinces in Indonesia. The results of the Rasch model analysis show that the assumptions of unidimensionality and local independence are met. The internal consistency analysis of the INAP-L instrument (PSR = 0.80; Ordinal α = 0.81) and item-person targeting showed quite good results. At the item level, it was discovered that four of the forty items did not fit the Rasch model. Meanwhile, the gender-based DIF analysis revealed that all items were free from gender DIF and that two out of forty items showed DIF based on school type. It can be concluded that the INAP-L instrument has good psychometric properties. Furthermore, multilevel analysis was performed to determine the effect of clustering in the data, and it was discovered that INAP-L has a multilevel data structure with ICC = 0.185 and a design effect > 2.00. This means that future research on the relationship among variables on the INAP-L score must consider a multilevel approach. Even though the developer has already published an interim report on INAP-L, the findings of this study can be used as a reference in improving the instrument before conducting INAP-L 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 | 0.028 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".