Representation of measurement error in marketing variables: Review of approaches and extension to three-facet designs

Abstract

This paper explores approaches for modeling measurement error in marketing research, including random, method and measure specific sources of error. The following approaches are considered: classic confirmatory factor analysis, second-order models, panel models, additive trait-method models, correlated uniqueness models, covariance components analysis, additive trait-method-measure specific-error models, and the direct product model, where traits and methods interact. Finally, a three-facet multiplicative model is addressed wherein latent variables underlying a phenomenon under investigation are shown to interact with multiple methods and occasions of measurement. The three-facet model is illustrated on a study of consumer attitudes toward losing weight explicitly conducted for this paper.