A guide to exploratory structural equation modeling (ESEM) and bifactor-ESEM in body image research
Why this work is in the frame
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Bibliographic record
Abstract
Traditionally, assessments of factor validity of body image instruments have relied on exploratory or confirmatory factor analysis. However, the emergence of exploratory structural equation modeling (ESEM), a resurgence of interest in bifactor models, and the ability to combine both models (bifactor-ESEM) is beginning to shape the future of body image research. For these analytic approaches to truly advance body image research, scholars will need to have a deep understanding of their use and application. To facilitate such understanding, we describe ESEM and bifactor-ESEM models for body image researchers and provide them with the tools they need to apply these methods in their own work. Specifically, we provide an overview of ESEM and bifactor-ESEM models, and describe their broad applicability to body image research. Next, we describe how ESEM and bifactor models can be used and, using an existing dataset of responses to the Acceptance of Cosmetic Surgery Scale, demonstrate how ESEM and bifactor-ESEM models can be deployed. To facilitate wider application of these ideas, we provide our Mplus syntax (inputs) in Supplementary Materials. Through this manuscript, we hope to assist researchers to better understand the strengths ESEM and bifactor models, and to use these approaches in their own work.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it