When averaging goes wrong: The case for mixture model estimation in psychological science.

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dc.contributor.author Moreau, David en
dc.contributor.author Corballis, Michael en
dc.date.accessioned 2019-03-13T00:20:39Z en
dc.date.issued 2019-09 en
dc.identifier.issn 0096-3445 en
dc.identifier.uri http://hdl.handle.net/2292/45946 en
dc.description.abstract Recent failed attempts to replicate numerous findings in psychology have raised concerns about methodological practices in the behavioral sciences. More caution appears to be required when evaluating single studies, while systematic replications and meta-analyses are being encouraged. Here, we provide an additional element to this ongoing discussion, by proposing that typical assumptions of meta-analyses be substantiated. Specifically, we argue that when effects come from more than one underlying distributions, meta-analytic averages extracted from a series of studies can be deceptive, with potentially detrimental consequences. The underlying distribution properties, we propose, should be modeled, based on the variability in a given population of effect sizes. We describe how to test for the plurality of distribution modes adequately, how to use the resulting probabilistic assessments to refine evaluations of a body of evidence, and discuss why current models are insufficient in addressing these concerns. We also consider the advantages and limitations of this method, and demonstrate how systematic testing could lead to stronger inferences. Additional material with details regarding all the examples, algorithm, and code is provided online to facilitate replication and to allow broader use across the field of psychology. (PsycINFO Database Record (c) 2019 APA, all rights reserved). en
dc.format.medium Print-Electronic en
dc.language eng en
dc.relation.ispartofseries Journal of experimental psychology. General en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.title When averaging goes wrong: The case for mixture model estimation in psychological science. en
dc.type Journal Article en
dc.identifier.doi 10.1037/xge0000504 en
pubs.issue 9 en
pubs.begin-page 1615 en
pubs.volume 148 en
dc.rights.holder Copyright: The author en
pubs.end-page 1627 en
pubs.publication-status Published en
dc.rights.accessrights http://purl.org/eprint/accessRights/RestrictedAccess en
pubs.subtype Journal Article en
pubs.elements-id 758181 en
pubs.org-id Science en
pubs.org-id Psychology en
dc.identifier.eissn 1939-2222 en
pubs.record-created-at-source-date 2018-11-30 en
pubs.dimensions-id 30489120 en

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