A short form of the Maximization Scale: Factor structure, reliability and validity studies
Bibliographic record
Abstract
Abstract We conducted an analysis of the 13-item Maximization Scale (Schwartz et al., 2002) with the goal of establishing its factor structure, reliability and validity. We also investigated the psychometric properties of several proposed refined versions of the scale. Four sets of analyses are reported. The first analysis confirms the 3-part factor structure of the scale and assesses its reliability. The second analysis identifies those items that do not perform well on the basis of internal, external, and judgmental criteria, and develops three shorter versions of the scale. In the third analysis, the three refined versions of the scale are cross-validated to confirm dimensionality, reliability, and validity. The fourth analysis uses an experiment in an investment decision making context to assess the reliability and nomological validity of the refined scales. These analyses lead us to conclude that a shorter, 6-item Maximization Scale performs best and should be used by future researchers. It is hoped that clarification of the conceptual underpinnings of the maximization construct and development of a refined scale will enhance its use among researchers across several of the social science disciplines.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".