A Multi-Rater Assessment of Organizational Commitment: Are Self-Report Measures Biased?
Bibliographic record
Abstract
Most investigations of organizational commitment have been conducted using self-report measures, however, the veracity of self-reports is often questioned. In a sample of 79 public-sector administrative staff, we assessed two types of organizational commitment (affective and continuance) from the perspective of three different sources of raters (self, peer, and supervisor) to test three explanations of the factors influencing self-report measures (observational opportunities, simple defensiveness, and moderated defensiveness). The pattern of correlations among the measures, analyzed using the composite direct product multitrait–multirater approach, suggested that self-report commitment measures are affected mainly by observations or experiences of the self-reporter rather than by systematic bias related to defensive responding. This increases our confidence that scores from self-report measures of affective and continuance commitment are veridical. Further, self- and peer-based measures of commitment were largely redundant in the prediction of a job-performance criterion whereas supervisory measures added unique predictive variance. Implications are discussed.
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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 | 0.142 | 0.359 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".