Does the Paraphilia Scale Work for Everyone? Confirmatory Factor Analysis and Measurement Invariance Across Gender and Sexual Orientation Groups
Bibliographic record
Abstract
We conducted three studies to examine the factor structure and measurement invariance of the Paraphilia Scale, a measure of paraphilic interests used in multiple studies. In the first study, we conducted a confirmatory factor analysis (CFA) testing different a priori models with a community sample of 1,040 adults previously reported by Seto et al. (2021), and found support for a hierarchical four-factor model: An agonistic continuum involving coercion or physical pain (biastophilia, sexual sadism, masochism), chronophilias (pedophilia, hebephilia), courtship disorders (voyeurism, exhibitionism, and frotteurism), and fetishism (object fetishism, transvestic fetishism, urophilia-coprophilia). This factor structure was replicated in a second study comprising a combined sample of 400 mTurk participants and 870 university students. The third study analyzed the community sample and found evidence of configural invariance but not scalar or metric invariance across gender (man or woman) and sexual orientation for gender (heterosexual or other sexual orientation). This indicates that the factor structure of the Paraphilia Scale is robust for gender and sexual orientation for gender, but factor loadings differ across these groups, as do the loadings of individual items on the four factors. Implications for research on gender and sexual orientation differences in paraphilic interests are discussed.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".