How to Conduct Power Analysis for Structural Equation Models: A Practical Primer
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) is popular, but planning for studies that use SEM for data analysis can be difficult. As power analysis becomes standard practice in many fields of psychology, researchers who use SEM for data analysis can benefit from knowing how to conduct power analysis for their studies. With this article, I offer a gentle, practical introduction to power analysis for SEM. First, I connect two goals that researchers often have when using SEM—to interpret the overall model and to detect target effects within the model—to power analysis. Then, I conceptually describe power to detect target effects and power to detect model misfit, summarizing what determines each and common approaches to conducting each type of power analysis. Finally, I provide an illustrative example of conducting power analysis for SEM with a concrete research scenario. Throughout the article, I prioritize plain language and practical guidance over technical depth, with the hope that it makes power analysis for SEM less daunting.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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