Examining the Psychometric Properties of the Sexual Excitation/Sexual Inhibition Inventory for Women (SESII-W) in a Sample of Lesbian and Bisexual Women
Bibliographic record
Abstract
The Sexual Excitation/Sexual Inhibition Inventory for Women (SESII-W) assesses propensities for sexual excitation (SE) and inhibition (SI). Previous research utilizing the SESII-W included samples comprised exclusively or almost entirely of heterosexual women. The purpose of this study was to examine the psychometric properties of the SESII-W and assess its relation to aspects of sexual function within a sample of lesbian and bisexual women. The sample included 974 self-identified bisexual (n = 733) or lesbian/homosexual (n = 241) women who completed an online survey including items assessing women's sexual behaviors, feelings, and functioning, sociodemographics, and the SESII-W. The sample was split; exploratory factor analyses were conducted on the first half, yielding eight lower-order factors with two higher-order factors. Confirmatory factor analysis was conducted on the second half and suggested reasonable model fit. SI was positively correlated with sexual problems and negatively correlated with sexual pleasure; the correlations were significant but small. Hierarchical regression analyses were conducted to examine the relationships between SESII-W scores and sexual problems/sexual pleasure, controlling for age, relationship duration, and relationship status. Four lower-order factors predicted reports of sexual problems. Findings indicated the SESII-W has similar psychometric properties among sexual minority women as it does among heterosexual women.
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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 | 0.002 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".