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An Inconvenient Truth: Arbitrary Distinctions Between Organizational, Mechanical Turk, and Other Convenience Samples

  • Richard N. Landers (a1) and Tara S. Behrend (a2)


Sampling strategy has critical implications for the validity of a researcher's conclusions. Despite this, sampling is frequently neglected in research methods textbooks, during the research design process, and in the reporting of our journals. The lack of guidance on this issue often leads reviewers and journal editors to rely on simple rules of thumb, myth, and tradition for judgments about sampling, which promotes the unnecessary and counterproductive characterization of sampling strategies as universally “good” or “bad.” Such oversimplification, especially by journal editors and reviewers, slows the progress of the social sciences by considering legitimate data sources to be categorically unacceptable. Instead, we argue that sampling is better understood in methodological terms of range restriction and omitted variables bias. This considered approach has far-reaching implications because in industrial–organizational (I-O) psychology, as in most social sciences, virtually all of the samples are convenience samples. Organizational samples are not gold standard research sources; instead, they are merely a specific type of convenience sample with their own positive and negative implications for validity. This fact does not condemn the science of I-O psychology but does highlight the need for more careful consideration of how and when a finding may generalize based on the particular mix of validity-related affordances provided by each sample source that might be used to investigate a particular research question. We call for researchers to explore such considerations cautiously and explicitly both in the publication and in the review of research.


Corresponding author

Correspondence concerning this article should be addressed to Richard N. Landers, 250 Mills Godwin Building, Department of Psychology, Old Dominion University, Norfolk, VA 23529,


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