Are Relational Inferences from Crowdsourced and Opt-in Samples Generalizable? Comparing Criminal Justice Attitudes in the GSS and Five Online Samples

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 int...

Full description

Saved in:  
Bibliographic Details
Authors: Thompson, Andrew J. (Author) ; Pickett, Justin T. (Author)
Format: Electronic Article
Language:English
Published: 2020
In: Journal of quantitative criminology
Year: 2020, Volume: 36, Issue: 4, Pages: 907-932
Online Access: Volltext (Resolving-System)
Journals Online & Print:
Drawer...
Check availability: HBZ Gateway
Keywords:
Opt-in panel
Amazon Mechanical Turk
Collider variable
Selection Bias
Web survey
Selection bias
Description
Summary: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.
ISSN:1573-7799
DOI:10.1007/s10940-019-09436-7