Application of Rasch Model on Resilience in Higher Education: An Examination of Validity and Reliability of Malaysian Academician Happiness Index (MAHI)
Why this work is in the frame
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Bibliographic record
Abstract
This preliminary study was conducted to examine and verify the validity and reliability of the instrument on the Malaysian Academician Happiness Index (MAHI) on resilience. MAHI could be seen as a tool to measure the level of happiness and stress of academicians before determining how resilient the academicians were. Resilience can be defined as a mental ability of a person to recover quickly from illness or depression. MAHI instrument consisted of 66 items. The instrument was distributed to 40 academicians from three groups of universities which were the Focus University, Comprehensive University and Research University is using a survey technique. The instrument was developed to measure three main constructs which were the organization, individual and social that would affect the happiness and stress levels of academicians. This preliminary study employed the Rasch Measurement Model uses Winsteps software version 3.69.1.11. to examine the validity and reliability of the items. The results of the analysis of the MAHI instrument showed that the item reliability was 0.87, person reliability was 0.83 and value of Alpha Cronbach was 0.84. Meanwhile, misfit analysis showed that only there was one item with 1.46 logit that could be considered for dropping or needed improvement. Therefore, it highlighted that most of the items met the constructs’ need and can be used as a measurement indicator of MAHI. The implication of this instrument can help Malaysian academicians to be more resilient in facing challenges in the future.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it