Development and Psychometric Validation of the Sexual Fantasies and Behaviors Inventory
Bibliographic record
Abstract
To date, no measures of sexual fantasies and behavior have been tested using modern structural equation modelling techniques. A total of 4,280 adults from the US, UK, Canada, and Ireland completed a measure of diverse (paraphilic and normophilic) sexual fantasies and behaviors. Data were randomly split in half for a two-part analysis. First, an exploratory factor analysis (EFA) was performed to reduce the item pool and determine general factor structure. Second, we tested several models using confirmatory factor analysis (CFA) and exploratory structural equation modelling (ESEM). These were followed by tests of measurement invariance (based on sex and sexual orientation) and criterion validity. For both the fantasies and behaviors, Bifactor ESEMs were the most appropriate models. Similar specific factors emerged: 1) normophilia, 2) rough sex, 3) interest of intrusion, 4) assuming power, and 5) relinquishing power. Findings suggest that sexual interests show a hierarchical measurement structure. Males and non-heterosexuals had higher general fantasy scores; females and non-heterosexuals had higher general behavior scores. Heterosexuals generally scored lower than non-heterosexuals. Fantasy and behavior scores were positively related to Dark Triad Traits and sociosexuality; there was little to no relationship with depression and anxiety. Results support the psychometric validation of the Sexual Fantasies and Behaviors Inventory.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".