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Survey Experiments: Testing Causality in Diverse Samples
Date
2019-01-18 (Creation date: 2019-01-18)
Main contributor
Mize, Trenton
Summary
Experimental designs remain the gold standard for assessing causality; perhaps because of this, the use of experiments has grown rapidly in most social science fields such as economics, political science, sociology, and others. While laboratory studies remain popular in some fields, there is increasing interest in bringing the power of experimental designs to more diverse samples. Survey experiments offer the capability to assess causality in a broad range of samples, including targeted samples of specific populations or in large-scale nationally representative samples. The rise of online workplaces and the TESS program offer the ability to bring these samples to applied researchers at a minimal cost, greatly expanding the possibilities for research. This workshop will focus on how to design quality survey experiments, giving researchers the tools to implement best practices. I will also advocate for survey experiments as a tool for tests of intersectionality and other theoretical questions requiring diverse samples.
Publisher
IU Workshop in Methods
Collection
Workshop in Methods
Unit
Social Science Research Commons
Related Item
Accompanying materials on IU ScholarWorks
Notes

Performers

Trent Mize is an assistant professor of sociology at Purdue University and core faculty for the cluster in advanced methodologies for the social, behavioral, and health sciences at Purdue (AMAP). His research covers three core areas: (1) how gender and sexuality shape workplace interaction and labor market outcomes; (2) experimental methodology and statistical approaches for causal inference, cross-model comparisons, and for modeling categorical dependent variables; and (3) how social roles and relationships shape health behavior and health inequalities. Recent work has appeared in the American Sociological Review, Social Problems, Social Psychology Quarterly, and Social Science & Medicine.