Evaluating the Use of Covariance‐Based Structural Equation Modelling with Reflective Measurement in Organizational and Management Research: A Review and Recommendations for Best Practice
Bibliographic record
Abstract
Abstract Covariance‐based structural equation modelling (CB‐SEM) with reflective measurement has been a popular data analysis tool in organizational and management research. Extensive studies and guidelines have been published on what constitutes its best practice. What is much less known is the extent to which CB‐SEM users in organizational and management research comprehend and adhere to the standards and principles behind this advanced analytical technique. In this study, we first devised an evaluation scheme to assess the quality of CB‐SEM performed in a study, and then utilized this scheme to examine 144 CB‐SEM studies published in 12 top organizational and management journals between 2011 and 2016. The evaluation of the published studies revealed a pressing need for more systematic and standardized approaches to planning, conducting and reporting CB‐SEM studies. We discussed the implication of the findings for future work.
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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 | 0.226 | 0.340 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.022 | 0.025 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".