A Renewal of Dyadic Structural Equation Modeling With Latent Variables: Clarifications, Methodological Advantages, and New Directions
Bibliographic record
Abstract
ABSTRACT Researchers interested in the quantitative analysis of data from dyads must select a preferred statistical framework. In this review, we focus on one option that has seen relatively modest adoption: dyadic structural equation modeling with latent variables. We begin by distinguishing dyadic SEM from alternate, neighboring, and hybridized frameworks, before sharing our view on the unique—and in our opinion, considerable—value‐proposition of the dyadic SEM framework. We then provide some preliminary evidence that dyadic SEM is subordinated in terms of adoption rates versus its competitors, before offering a contextual analysis of why that may have come to be the case. Finally, we conclude with a discussion of future possibilities, some near and accessible and others farther away and more technical, that researchers in the field might pursue (and benefit from) with the help of dyadic SEM with latent variables.
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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 | 0.178 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.012 |
| 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; 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".