Structural equation models and the regression bias for measuring correlates of change

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dc.contributor.authorCribbie, Robert
dc.contributor.authorJamieson, John
dc.date.accessioned2018-06-04T19:30:29Z
dc.date.available2018-06-04T19:30:29Z
dc.date.issued2000-12
dc.description.abstractANCOVA and regression both exhibit a directional bias when measuring correlates of change. This bias confounds the comparison of changes between naturally occurring groups with large pretest differences (ANCOVA), or for identifying predictors of change when the predictor is correlated with pretest (regression). This bias is described in some detail. A computer simulation study is presented, which shows that properly identified structural equation models are not susceptible to this bias. Neither gain scores (posttest minus pretest) nor structural equation models exhibit the “regression bias.” Other factors, such as skewness, that may confound measurement of change are also discussed.en_US
dc.description.sponsorshipSocial Sciences and Humanities Research Council
dc.identifier.citationCribbie, R. A. & Jamieson, J. (2000). Structural equation models and the regression bias for measuring correlates of change. Educational and Psychological Measurement, 60, 893-907. doi:10.1177/00131640021970970
dc.identifier.urihttps://doi.org/10.1177/00131640021970970en_US
dc.identifier.urihttp://hdl.handle.net/10315/34611
dc.language.isoenen_US
dc.publisherSageen_US
dc.rights.articlehttp://journals.sagepub.com/doi/abs/10.1177/00131640021970970
dc.subjectdirectional biasen_US
dc.subjectpretest differencesen_US
dc.subjectANCOVAen_US
dc.subjectregressionen_US
dc.titleStructural equation models and the regression bias for measuring correlates of change
dc.typeArticleen_US

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