Effect Size Interpretation in Structural Equation 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
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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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