Nonprobability and probability-based sampling strategies in sexual science

JA Catania, MM Dolcini, R Orellana…�- Journal of Sex�…, 2015 - Taylor & Francis
JA Catania, MM Dolcini, R Orellana, V Narayanan
Journal of Sex Research, 2015Taylor & Francis
With few exceptions, much of sexual science builds upon data from opportunistic
nonprobability samples of limited generalizability. Although probability-based studies are
considered the gold standard in terms of generalizability, they are costly to apply to many of
the hard-to-reach populations of interest to sexologists. The present article discusses recent
conclusions by sampling experts that have relevance to sexual science that advocates for
nonprobability methods. In this regard, we provide an overview of Internet sampling as a�…
With few exceptions, much of sexual science builds upon data from opportunistic nonprobability samples of limited generalizability. Although probability-based studies are considered the gold standard in terms of generalizability, they are costly to apply to many of the hard-to-reach populations of interest to sexologists. The present article discusses recent conclusions by sampling experts that have relevance to sexual science that advocates for nonprobability methods. In this regard, we provide an overview of Internet sampling as a useful, cost-efficient, nonprobability sampling method of value to sex researchers conducting modeling work or clinical trials. We also argue that probability-based sampling methods may be more readily applied in sex research with hard-to-reach populations than is typically thought. In this context, we provide three case studies that utilize qualitative and quantitative techniques directed at reducing limitations in applying probability-based sampling to hard-to-reach populations: indigenous Peruvians, African American youth, and urban men who have sex with men (MSM). Recommendations are made with regard to presampling studies, adaptive and disproportionate sampling methods, and strategies that may be utilized in evaluating nonprobability and probability-based sampling methods.
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