Using Finite Mixture Modeling to Deal with Systematic Measurement Error: A Case Study

Authors

Min Liu, University of HawaiiFollow
Gregory R. Hancock, University of MarylandFollow
Jeffrey R. Harring, University of MarylandFollow

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.

DOI

10.22237/jmasm/1304223660