Deriving Estimators and Their Standard Errors in Dyadic Data Analysis: Examples Using a Symbolic Computation Program
Bibliographic record
Abstract
Michael Browne’s foundational papers on the analysis of covariance structures (e.g., Browne, 1974, 1982) were influential in our conceptualization of models for dyadic data analysis. He provided a framework for working with covariance structures that makes it relatively easy to derive new results. We began working on dyadic models in the early 1990s, before the explosion of research on multilevel models and random effect models. Motivated by the ubiquity of dyadic data in social psychological research, we recognized that a structural equation modeling (SEM) approach with latent variables representing dyadic similarity would be helpful in modelingmultivariate dyadic data.Multilevelmodels, let alonemultivariatemultilevel models, were relatively new at that time and we explored their usefulness for dyadic data analysis. But it was the SEM approach and the Browne (1974, 1982) papers that we found most helpful in our initial model formulation.
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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 | 0.023 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".