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dc.contributor.authorShieh, Gwowenen_US
dc.date.accessioned2017-04-21T06:56:21Z-
dc.date.available2017-04-21T06:56:21Z-
dc.date.issued2017en_US
dc.identifier.issn0027-3171en_US
dc.identifier.urihttp://dx.doi.org/10.1080/00273171.2016.1219841en_US
dc.identifier.urihttp://hdl.handle.net/11536/133225-
dc.description.abstractAnalysis of covariance (ANCOVA) is commonly used in behavioral and educational research to reduce the error variance and improve the power of analysis of variance by adjusting the covariate effects. For planning and evaluating randomized ANCOVA designs, a simple sample-size formula has been proposed to account for the variance deflation factor in the comparison of two treatment groups. The objective of this article is to highlight an overlooked and potential problem of the exiting approximation and to provide an alternative and exact solution of power and sample size assessments for testing treatment contrasts. Numerical investigations are conducted to reveal the relative performance of the two procedures as a reliable technique to accommodate the covariate features that make ANCOVA design particularly distinctive. The described approach has important advantages over the current method in general applicability, methodological justification, and overall accuracy. To enhance the practical usefulness, computer algorithms are presented to implement the recommended power calculations and sample-size determinations.en_US
dc.language.isoen_USen_US
dc.subjectAnalysis of covarianceen_US
dc.subjectcontrasten_US
dc.subjectpoweren_US
dc.subjectsample sizeen_US
dc.titlePower and Sample Size Calculations for Contrast Analysis in ANCOVAen_US
dc.identifier.doi10.1080/00273171.2016.1219841en_US
dc.identifier.journalMULTIVARIATE BEHAVIORAL RESEARCHen_US
dc.citation.volume52en_US
dc.citation.issue1en_US
dc.citation.spage1en_US
dc.citation.epage11en_US
dc.contributor.department管理科學系zh_TW
dc.contributor.departmentDepartment of Management Scienceen_US
dc.identifier.wosnumberWOS:000395121200001en_US
Appears in Collections:Articles