Expanding Statistical Frontiers in Sexual Science: Taxometric, Invariance, and Equivalence Testing
Bibliographic record
Abstract
Sexual scientists must choose from among myriad methodological and analytical approaches when investigating their research questions. How can scholars learn whether sexualities are discrete or continuous? How is sexuality constructed? And to what extent are sexuality-related groups similar to or different from one another? Though commonplace, quantitative attempts at addressing these research questions require users to possess an increasingly deep repertoire of statistical knowledge and programming skills. Recently developed open-source software offers powerful yet accessible capacity to researchers wishing to perform strong quantitative tests. Taking advantage of these new statistical opportunities will require sexual scientists to become familiar with new analyses, including taxometric analysis, tests of measurement variability and differential item functioning, and equivalence testing. In the current article, I discuss each of these analyses, providing conceptual and historical overviews. I also address common misunderstandings for each analysis that may discourage researchers from implementing them. Finally, I describe current best practices when using each analysis, providing reproducible coding examples and interpretations along the way, in an attempt to reduce barriers to the uptake of these analyses. By aspiring to explore these new statistical frontiers in sexual science, sexuality researchers will be better positioned to test their substantive theories of interest.
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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 | 0.134 | 0.273 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.023 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".