a Department of Psychology , University of Victoria.
J Sex Res. 2019 May-Jun;56(4-5):475-510. doi: 10.1080/00224499.2019.1568377. Epub 2019 Feb 22.
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.
性科学研究者在研究他们的问题时，必须从无数的方法和分析方法中进行选择。学者们如何才能知道性是离散的还是连续的？性是如何构建的？与性相关的群体在多大程度上彼此相似或不同？虽然常见，但定量方法研究这些问题需要用户具备越来越深的统计知识和编程技能。最近开发的开源软件为希望进行强有力的定量测试的研究人员提供了强大而易于使用的能力。利用这些新的统计机会，性科学研究者需要熟悉新的分析方法，包括分类分析、测量变异性和差异项目功能测试以及等效性测试。在当前的文章中，我将讨论每一种分析方法，提供概念和历史概述。我还解决了每种分析方法中常见的误解，这些误解可能会阻止研究人员实施这些方法。最后，我描述了使用每种分析方法的当前最佳实践，同时提供了可重复的编码示例和解释，以试图减少采用这些分析方法的障碍。通过渴望探索性科学中的这些新的统计前沿，性研究人员将更好地定位来检验他们感兴趣的实质性理论。
