Measurement Invariance and Psychometric Analysis of Oxford Happiness Inventory Scale across Gender and Marital Status
Bibliographic record
Abstract
BACKGROUND: The Oxford Happiness Inventory (OHI) is a self-report tool to measure happiness. A brief review of previous studies on OHI showed the lack of evaluation of OHI fairness/equivalence in measuring happiness among identified groups. METHODS: To examine the psychometric properties and measurement invariance of the OHI, responses of 500 university students were analyzed using item response theory and ordinal logistic regression (OLR). Relevant measures of effect size were utilized to interpret the results. Differential test functioning was also evaluated to determine whether there is an overall bias at the test level. RESULTS: OLR analysis detected four items across gender and two items across marital status to function differentially. An assessment of effect sizes implied negligible differences for practical considerations. CONCLUSIONS: This study was a significant step towards providing theoretical and practical information regarding the assessment of happiness by presenting adequate evidence regarding the psychometric properties of OHI.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".