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dc.contributor.authorAguirre-Urreta, Miguel I.
dc.contributor.authorRönkkö, Mikko
dc.contributor.authorMcIntosh, Cameron N.
dc.date.accessioned2019-05-23T09:32:14Z
dc.date.available2019-05-23T09:32:14Z
dc.date.issued2019
dc.identifier.citationAguirre-Urreta, M. I., Rönkkö, M., & McIntosh, C. N. (2019). A cautionary note on the finite sample behavior of maximal reliability. <i>Psychological Methods</i>, <i>24</i>(2), 236-252. <a href="https://doi.org/10.1037/met0000176" target="_blank">https://doi.org/10.1037/met0000176</a>
dc.identifier.otherCONVID_28231311
dc.identifier.otherTUTKAID_78638
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/64145
dc.description.abstractSeveral calls have been made for replacing coefficient α with more contemporary model-based reliability coefficients in psychological research. Under the assumption of unidimensional measurement scales and independent measurement errors, two leading alternatives are composite reliability and maximal reliability. Of these two, the maximal reliability statistic, or equivalently Hancock's H, has received a significant amount of attention in recent years. The difference between composite reliability and maximal reliability is that the former is a reliability index for a scale mean (or unweighted sum), whereas the latter estimates the reliability of a scale score where indicators are weighted differently based on their estimated reliabilities. The formula for the maximal reliability weights has been derived using population quantities; however, their finite-sample behavior has not been extensively examined. Particularly, there are two types of bias when the maximal reliability statistic is calculated from sample data: (a) the sample maximal reliability estimator is a positively biased estimator of population maximal reliability, and (b) the true reliability of composites formed with maximal reliability weights calculated from sample data is on average less than the population reliability. Both effects are more pronounced in small-sample scenarios (e.g., <100). We also demonstrate that the composite reliability estimator for equally weighted composite exhibits substantially less bias, which makes it a more appropriate choice for the small-sample case.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherAmerican Psychological Association
dc.relation.ispartofseriesPsychological Methods
dc.rightsIn Copyright
dc.subject.otherreliability estimators
dc.subject.othermaximal reliability
dc.subject.othercomposite reliability
dc.subject.othersample size
dc.titleA cautionary note on the finite sample behavior of maximal reliability
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-201905222731
dc.contributor.laitosInformaatioteknologian tiedekuntafi
dc.contributor.laitosFaculty of Information Technologyen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.date.updated2019-05-22T12:15:17Z
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.format.pagerange236-252
dc.relation.issn1082-989X
dc.relation.numberinseries2
dc.relation.volume24
dc.type.versionacceptedVersion
dc.rights.copyright© American Psychological Association, 2018.
dc.rights.accesslevelopenAccessfi
dc.relation.grantnumber311309
dc.subject.ysotilastomenetelmät
dc.subject.ysoreliabiliteetti
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p3127
jyx.subject.urihttp://www.yso.fi/onto/yso/p9970
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.relation.doi10.1037/met0000176
dc.relation.funderSuomen Akatemiafi
dc.relation.funderResearch Council of Finlanden
jyx.fundingprogramTutkijatohtori, SAfi
jyx.fundingprogramPostdoctoral Researcher, AoFen
jyx.fundinginformationThis research was supported in part by a grant from the Academy of Finland (grant 311309). Parts of this research were carried out while Mikko Rönkkö was at the Department of Industrial Engineering and Management, Aalto University. We acknowledge the computational resources provided by the Aalto Science-IT project.
dc.type.okmA1


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