Reliability Generalization as a Seal of Quality of Substantive Meta-Analyses: The Case of the VIA Inventory of Strengths (VIA-IS) and Their Relationships to Life Satisfaction
Bibliographic record
Abstract
Reliable test scores are essential to interpret the results obtained in statistical analyses correctly. In this study, we used the Values in Action Inventory of Strengths (VIA-IS) as an example of a widely applied assessment instrument to analyze its metric quality in what is known as reliability generalization (RG). In addition, we conducted a meta-analysis of the correlations between character strengths and life satisfaction to examine the potential relationship between the reliability of test scores and the intensity of these correlations. The overall variability of alpha coefficients supports the argument that reliability is sample dependent. Indeed, there were statistically significant mean reliability differences for scores across the 24 scales, with the highest level of reliability observed for Creativity and the lowest for scores on Self-regulation. Significant moderators such as the standard deviation of the scores and the sample type contribute to understand the high variability observed in the reliability estimation. The second meta-analysis showed that Zest, Hope, Gratitude, Curiosity, and Love were the character strengths that were highly related to life satisfaction, while Modesty and Prudence were less related to life satisfaction. Furthermore, the high heterogeneity between samples might be an indicator of the relationship between the variability of reliability of character strengths' scores and the intensity of their correlations with life satisfaction. Those character strengths with high-potential RG are related or unrelated to life satisfaction, whereas character strengths with less-potential RG showed unstable correlation patterns. The results of both studies point out the role of the relationship between the reliability of test scores and substantive studies, such as Pearson's correlations meta-analysis.
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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 | 0.380 | 0.595 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.024 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".