Can Non-full-probability Internet Surveys Yield Useful Data? A Comparison with Full-probability Face-to-face Surveys in the Domain of Race and Social Inequality Attitudes

By Alicia D. Simmons, L. Bobo. 2015

178 citations recorded by the source corpus.

The authors investigate the potential utility of Web-based surveys of non-full-probabilistically sampled respondents for social science research. Specifically, they compare demographic, attitude response, and multivariate model results produced by two distinct survey modalities: the traditional full-probability sample face-to-face survey and the non-full-probability Web survey. Using data from the 2009 Race Cues, Attitudes, and Punitiveness Survey (RCAPS), the 2008 General Social Survey (GSS), and the 2008 American National Election Study (ANES), the authors find that (1) the unweighted demographic differences between surveys tend to be slight; (2) in comparison with GSS and ANES respondents, RCAPS respondents are more interested in politics and ideologically polarized; (3) in comparison with ANES respondents, RCAPS respondents are more racially and socially conservative, often selecting the most extreme response option; (4) when the dependent variable is a more general and abstract measure of social attitudes, the multivariate models generated by the ANES and RCAPS show several differences that are trivial in magnitude, but when the dependent variable is a more specific and concrete measure, the models show remarkable similarity; and (5) RCAPS multivariate models consistently explain more variance than ANES models. Overall, these findings show both substantial similarities across the two survey modalities as well as a few clear, reasonably well-specified differences.

Subfields: Communication, Political Science and International Relations, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/027074db52f774389887ebf2956832b911785165

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Citations in the corpus

Advantages of Matched Over Unmatched Opt-in Samples for Studying Criminal Justice Attitudes: A Research Note (2020, background): “Simmons and Bobo (2015), for example, found evidence suggesting YouGov’s sample matching method eliminated bias in regression models predicting egalitarianism, but not in those predicting racial resentment.”
Hate Knows No Boundaries: Online Hate in Six Nations (2020, result): “Previous work found evidence that probability and nonprobability sampling yields similar results when using particular internet surveying platforms (Simmons and Bobo 2015; Weinberg, Freese, and McElhattan 2014).”