Structural equation modelling in male sexual violence against women research: best practices in statistical reporting and interpretation
Bibliographic record
Abstract
Structural equation modelling (SEM), which is a family of statistical techniques in multivariate analysis, is increasingly used in sexual violence perpetration research. SEM is a versatile tool that allows for the examination of several latent variables in one cohesive analysis rather than conducting a series of sequential analyses. This advantage is of particular importance to sexual violence perpetration researchers, who often use several scales simultaneously to measure latent risk factors and outcome variables within one study. The current systematic review coded SEM statistical reporting practices in 28 sexual violence perpetration articles. We discussed statistical reporting trends across these studies and recommendations for best practice around SEM reporting techniques in sexual violence perpetration research. Researchers using SEM should be informed of best practices for reporting and interpreting SEM models to ensure adequate transparency in research communication, particularly given replicability crisis in social sciences, and the emergence of open science practices.PRACTICE IMPACT STATEMENT This review identifies several trends in the reporting of structural equation modelling in sexual violence perpetration research. By analysing these trends alongside known best practices for reporting, we make recommendations to help future researchers design their studies and evaluate theoretical models of sexual violence perpetration using structural equation modelling.
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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 | 0.523 | 0.799 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.018 | 0.032 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".