A person‐centered approach to commitment research: Theory, research, and methodology
Bibliographic record
Abstract
Summary There has been a recent increase in the application of person‐centered research strategies in the investigation of workplace commitments. To date, research has focused primarily on the identification, within a population, of subgroups presenting different cross‐sectional or longitudinal configurations of commitment mindsets (affective, normative, and continuance) and/or targets (e.g., organization, occupation, and supervisor), but other applications are possible. In an effort to promote a substantive methodological synergy, we begin by explaining why some aspects of commitment theory are best tested using a person‐centered approach. We then summarize the results of existing research and suggest applications to other research questions. Next, we turn our attention to methodological issues, including strategies for identifying the best profile structure, testing for consistency across samples, time, culture, and so on, and incorporating other variables in the models to test theory regarding profile development, consequences, and change trajectories. We conclude with a discussion of the practical implications of taking a person‐centered approach to the study of commitment as a complement to the more traditional variable‐centered approach. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.059 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".