Effect Size Interpretation in Structural Equation Models
Bibliographic record
Abstract
Structural equation modeling (SEM) involves many complex statistical issues, but the ultimate purpose is to obtain parameter estimates that answer research questions about the associations among variables. These estimates provide key effect-size information, making it critical to report and interpret them well. Although there are many resources on effect-size reporting, none focus on SEM. Furthermore, reporting standards for SEM neglect the connection between parameter estimates and effect sizes and provide little interpretational guidance. Thus, this paper provides an overview of effect-size reporting and interpretation within SEM. After defining the effect-size concept broadly, we explain how effect sizes are represented in three common types of SEM: path analysis, confirmatory factor analysis, and structural regression models. Then, based on a brief literature review, we discuss the need for higher-quality effect-size interpretation in studies reporting SEM results.
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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 | 0.307 | 0.707 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".