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551

Title *

Convenience Samples and Measurement Equivalence in Replication Researchuse asterix (*) to get italics

Authors *

Lindsay J. Alley, Jordan Axt, Jessica Kay FlakePlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"

Year *

2023

Picture

Abstract *

<p>A great deal of research in psychology employs either university student or online crowdsourced convenience samples (Chandler & Shapiro, 2016; Strickland & Stoops, 2019) and there is evidence that these groups differ in meaningful ways (Behrend et al., 2011). This practice could result in the presence of unaccounted-for measurement differences across convenience sample sources, which may bias results when these groups are compared or the resulting data are pooled. In this registered report, we used the openly available data from the Many Labs replication projects to test for measurement equivalence across different convenience sample sources. We examined 89 measures that showed acceptable baseline model fit and tested them for non-equivalence across convenience samples from different sources, including university participant pools, MTurk, and Project Implicit. We then examined whether replication results are robust to non-equivalence by fitting partial invariance models and sensitivity analyses of replication results. Many of the measures examined were not equivalent across student and crowdsourced convenience samples, or across different types of convenience samples. Only two tests, comparing lab and online student samples, retained strict equivalence, while 14 of 30 tests rejected configural equivalence. However, correcting for non-equivalence changed the estimated effect sizes of the replication effects very little. Based on these results, we advise researchers to test for measurement equivalence when combining or comparing data from different convenience samples. At the same time, due to a lack of validity evidence for many of the measures and variable power of our tests, we interpret results with caution.</p>

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Keywords (optional)

measurement, psychometrics, equivalence, invariance, metascience, replication

Methods that require specific expertise (optional)

NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).

Thematic fields *

Social sciences

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Submission date

2023-08-31 20:26:43

Recommender

Corina Logan

Reviewers

Alison Young Reusser

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