Using finite mixture modeling to deal with systematic measurement error: A case study

Abstract

Conventional methods and analyses view measurement error as random. A scenario is presented where a variable was measured with systematic error. Mixture models with systematic parameter constraints were used to test hypotheses in the context of general linear models; this accommodated the heterogeneity arising due to systematic measurement error. © 2011 JMASM, Inc.