On the effect of measurement model misspecification in PLS Path Modeling: the reflective case
Bibliographic record
Abstract
The specification of a measurement model as reflective or formative is the object of a lively debate.Part of the existing literature focuses on measurement model misspecification.This means that a true model is assumed and the impact on the path coefficients of using a wrong model is investigated.The majority of these studies is restricted to Structural Equation Modeling (SEM).Regarding PLS-Path Modeling (PLS-PM), a few authors have carried out simulation studies to investigate the robustness of the estimates, but their focus is the comparison with SEM.The present paper discusses the misspecification problem in the PLS-PM context from a novel perspective.First, a real application on Alumni Satisfaction will be used to verify whether different assumptions for the measurements models influence the results.Second, the results of a Monte-Carlo simulation study, in the reflective case, will help to bring some clarity on a complex problem that has not been sufficiently studied yet.
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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 | 0.286 | 0.709 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".