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Decreases in posttest variance and the measurement of change

dc.contributor.authorCribbie, Robert
dc.contributor.authorJamieson, John
dc.date.accessioned2018-06-05T16:44:52Z
dc.date.available2018-06-05T16:44:52Z
dc.date.issued2004
dc.description.abstractA Monte Carlo study was used to evaluate the effects of reductions in posttest variance on several methods for detecting predictors of change in a two-wave design. When the predictor was dichotomous, the analysis of covariance approach was compared to the analysis of variance on difference scores. For a continuous predictor, partial correlations, difference score correlations with the predictor and latent change correlations with the predictor in structural equation growth models were used. When posttest variance decreased (e.g., ceiling effect) difference scores lost power, while the power of regression based methods (analysis of covariance and partial correlations) and structural equation measures of change were unaffected.en_US
dc.description.sponsorshipSocial Sciences and Humanities Research Council
dc.identifier.citationCribbie, R. A. & Jamieson, J. (2004). Decreases in posttest variance and the measurement of change. Methods of Psychological Research Online, 9, 37-55.
dc.identifier.urihttp://hdl.handle.net/10315/34621
dc.language.isoenen_US
dc.publisherAmerican Psychological Associationen_US
dc.rights.articlehttp://psycnet.apa.org/record/2005-01820-003
dc.subjectposttest varianceen_US
dc.subjectpredictors of changeen_US
dc.titleDecreases in posttest variance and the measurement of change
dc.typeArticleen_US

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