Analyzing the Construct Validity of Work Readiness Instruments for Indonesian Law Faculty Students Using the Rasch Model
Bibliographic record
Abstract
Objective: The issue of undergraduate law students tends not to have clear work readiness, the data is obtained from several research results, the research findings have an indication of concern in interpreting the data which has an impact on inaccuracies in analyzing the data, the unclear construct of the instrument items studied, the measurements obtained depend on the characteristics of the test used, the item parameters depend on individual abilities, and measurement errors can only know for groups not individuals. Measuring instruments need to be tested for validity and reliability before being used on individuals to achieve valid and reliable goals. This study aims to test the validity and reliability of work readiness instruments based on the Work-Readiness Integrated Competence Model (WRICM) theory. Method: This research was conducted on 720 participants from several universities in Indonesia with a Cross Sectional Survey research design. The results of data collection were then analyzed through the Rasch model using the Winstep version 3.73 application. Results: The results showed a unidimensionality value of 36.1%, item reliability of 0.99. Of the 23 items created, there are 14 instrument items that have met the requirements of objective measurement. Conclusion: The Indonesian work readiness instrument obtained can be used to obtain data on work readiness needs as a foothold for determining the education strategy for law faculty students in Indonesia in terms of content, methods, and comprehensive evaluation.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".