Cross-Cultural Validation of the Sexual Desire Inventory (SDI-2) in 42 Countries and 26 Languages
Bibliographic record
Abstract
Sexual desire is a complex construct with important implications for sexual functioning and well-being. In this research, we translated the Sexual Desire Inventory (SDI-2), a widely used scale for assessing sexual (desire), into 25 languages from English and used data from the International Sex Survey (ISS) to (a) investigate its psychometric properties (i.e. factorial structure, reliability, validity, and measurement invariance) and (b) explore the expression of sexual desire across different countries, genders, and sexual orientations. A total of 82,243 participants from 42 countries completed the SDI-2, along with other sexuality-related scales. Confirmatory factor analysis supported a three-factor solution for the SDI-2 (CFI = .980; RMSEA = .060), encompassing the domains of "Partner-related," "Attractive-person-related," and "Solitary" sexual desire. The reliability of the total score and subscales were excellent. Likewise, correlations with other sexuality-related variables were positive yet weak-to-moderate in effect size. Measurement invariance tests supported its use across countries, languages, genders, and sexual orientations. Analysis of SDI-2 scores according to these variables supported its ability to capture group-based differences in sexual desire. In sum, the SDI-2 constitutes a psychometrically robust measure for the assessment of sexual desire in non-clinical samples with utility in large-scale cross-cultural studies.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".