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
Included in
Applied Statistics Commons, Social and Behavioral Sciences Commons, Statistical Theory Commons
