Extensions of Auxiliary Variable Approaches for the Investigation of Mediation, Moderation, and Conditional Effects in Mixture Models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Person-centered analyses and mixture models, such as latent profile analyses (LPA), are becoming increasingly common in the organizational literature. However, common usage of LPA rarely extends to the estimation of moderation, conditional effects, and mediation within a single model. This can affect the accuracy of parameter estimates, and it interferes with development and investigation of complex theories. The current study provides an overview of systematic approaches that allows researchers to investigate models involving moderation, conditional effects on outcomes, and mediation. Using M plus, we offer an accessible method of testing complex statistical models that are auxiliary to the focal mixture model. We provide syntax for typical moderation, conditional effects, and mediation hypotheses, and we provide a detailed explanation of the procedures. We demonstrate these procedures with applications involving the five-factor model (FFM) of personality and several additional variables that comprise complex auxiliary statistical models. The pedagogical approach offered by this research will facilitate future theoretical developments and empirical advancements in the use of person-centered analyses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it