Detecting a lack of association: An equivalence testing approach

dc.contributor.authorGoertzen, J. R.
dc.contributor.authorCribbie, Robert
dc.date.accessioned2018-06-03T18:50:53Z
dc.date.available2018-06-03T18:50:53Z
dc.date.issued2010
dc.description.abstractResearchers often test for a lack of association between variables. A lack of association is usually established by demonstrating a non-significant relationship with a traditional test (e.g., Pearson’s r). However, for logical as well as statistical reasons, such conclusions are problematic. In this paper, we discuss and compare the empirical Type I error and power rates of three lack of association tests. The results indicate that large, sometimes very large , sample sizes are required for the test statistics to be appropriate. What is especially problematic is that the required sample sizes may exceed what is practically feasible for the conditions that are expected to be common among researchers in psychology. This paper highlights the importance of using available lack of association tests, instead of traditional tests of association, for demonstrating the independence of variables, and qualifies the conditions under which these tests are appropriate.en_US
dc.description.sponsorshipSocial Sciences and Humanities Research Council (SSHRC)
dc.identifier.citationGoertzen, J. R. & Cribbie, R. A. (2010). Detecting a lack of association: An equivalence testing approach. British Journal of Mathematical and Statistical Psychology, 63, 527-537. doi: 10.1348/000711009X475853
dc.identifier.issn0007-1102
dc.identifier.urihttps://doi.org/10.1348/000711009X475853en_US
dc.identifier.urihttp://hdl.handle.net/10315/34594
dc.language.isoenen_US
dc.publisherThe British Psychological Societyen_US
dc.rights.articlehttps://onlinelibrary.wiley.com/doi/pdf/10.1348/000711009X475853
dc.rights.journalhttps://onlinelibrary.wiley.com/journal/20448317en_US
dc.rights.publisherwww.bpsjournals.co.uken_US
dc.subjectequivalence testingen_US
dc.subjectindependence of variablesen_US
dc.titleDetecting a lack of association: An equivalence testing approach
dc.typeArticleen_US

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