RT Article
T1 Are Relational Inferences from Crowdsourced and Opt-in Samples Generalizable? Comparing Criminal Justice Attitudes in the GSS and Five Online Samples
JF Journal of quantitative criminology
VO 36
IS 4
SP 907
OP 932
A1 Thompson, Andrew J.
A2 Pickett, Justin T.
LA English
YR 2020
UL https://krimdok.uni-tuebingen.de/Record/1745751750
AB Similar to researchers in other disciplines, criminologists increasingly are using online crowdsourcing and opt-in panels for sampling, because of their low cost and convenience. However, online non-probability samples’ “fitness for use” will depend on the inference type and outcome variables of interest. Many studies use these samples to analyze relationships between variables. We explain how selection bias—when selection is a collider variable—and effect heterogeneity may undermine, respectively, the internal and external validity of relational inferences from crowdsourced and opt-in samples. We then examine whether such samples yield generalizable inferences about the correlates of criminal justice attitudes specifically.
K1 Opt-in panel
K1 Amazon Mechanical Turk
K1 Collider variable
K1 Selection Bias
K1 Web survey
K1 Selection bias
DO 10.1007/s10940-019-09436-7