Affiliation:
1. New York University, New York, NY
Abstract
Quantitative research on sexual minority youths (SMYs) has likely contributed to misperceptions about the risk and deviance of this population. In part because it often relies on self-reported data from population-based self-administered questionnaires, this research is susceptible to misclassification bias whereby youths who are not SMYs are classified as such and vice versa. I introduce a taxonomy of classification errors, including seven unique sources, and discuss the potential effects of each error on estimates of youth outcomes. Through examples, I illustrate how researchers can detect and correct such errors and discuss how piecewise attempts to address bias can sometimes unintentionally exacerbate other forms of bias. Finally, I propose a unified conceptual model for addressing the various error sources, urging researchers to take a proactive and integrative approach to data validity by attending to errors at each step in the research process, from questionnaire development to data analysis. Although I focus on errors specific to the topic of sexuality research on youths, this argument can serve as a broader template for the development of taxonomies of error along other dimensions considered in educational research, such as race and disability status.
Publisher
American Educational Research Association (AERA)
Cited by
25 articles.
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