The Love of Money and Pay Level Satisfaction: Measurement and Functional Equivalence in 29 Geopolitical Entities around the World
Bibliographic record
Abstract
Demonstrating the equivalence of constructs is a key requirement for cross-cultural empirical research. The major purpose of this paper is to demonstrate how to assess measurement and functional equivalence or invariance using the 9-item, 3-factor Love of Money Scale (LOMS, a second-order factor model) and the 4-item, 1-factor Pay Level Satisfaction Scale (PLSS, a first-order factor model) across 29 samples in six continents (N = 5973). In step 1, we tested the configural, metric and scalar invariance of the LOMS and 17 samples achieved measurement invariance. In step 2, we applied the same procedures to the PLSS and nine samples achieved measurement invariance. Five samples (Brazil, China, South Africa, Spain and the USA) passed the measurement invariance criteria for both measures. In step 3, we found that for these two measures, common method variance was non-significant. In step 4, we tested the functional equivalence between the Love of Money Scale and Pay Level Satisfaction Scale. We achieved functional equivalence for these two scales in all five samples. The results of this study suggest the critical importance of evaluating and establishing measurement equivalence in cross-cultural studies. Suggestions for remedying measurement non-equivalence are offered.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".