EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT THOMPSON AND COOK STABILITY OF THE RELIABILITY OF LibQUAL+ ™ SCORES: A RELIABILITY GENERALIZATION META-ANALYSIS STUDY
Bibliographic record
Abstract
The present study reports a reliability generalization (RG)meta-analysis of subscale and total scale scores on the Web-administered LibQUAL+ ™ protocol. Data were provided by 18,161 participants from 43 universities in the United States and Canada. Results indicate that score reliabilities were remarkably invariant across campuses and different user groups. In 1998, Vacha-Haase proposed her reliability generalization (RG) method as a measurement meta-analytic method similar to validity general-ization (Hunter & Schmidt, 1990; Schmidt & Hunter, 1977). RG character-izes: (a) the typical reliability of scores for a given test across studies, (b) the amount of variability in reliability coefficients for givenmeasures, and (c) the sources of variability in reliability coefficients across studies. RG methods have been applied to study the characteristics of scores from awide variety of
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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 | 0.085 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.021 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".