Latent variable modeling in congruence research:current problems and future directions

Edwards, Jeffrey R. (2009) Latent variable modeling in congruence research:current problems and future directions. Organizational Research Methods, 12 (1). pp. 34-62. ISSN 1094-4281

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Abstract

During the past decade, the use of polynomial regression has become increasingly prevalent in congruence research. One drawback of polynomial regression is that it relies on the assumption that variables are measured without error. This assumption is relaxed by structural equation modeling with latent variables. One application of structural equation modeling to congruence research is the latent congruence model (LCM). Although the LCM takes measurement error into account and allows tests of measurement equivalence, it is framed around the mean and algebraic difference of the components of congruence (e.g., the person and organization), which creates various interpretational problems. This article discusses problems with the LCM and shows how these problems are resolved by a linear structural equation model that uses the components of congruence as predictors and outcomes. Extensions of the linear model to quadratic equations used in polynomial regression analysis are discussed.

Item Type:
Journal Article
Journal or Publication Title:
Organizational Research Methods
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1400/1405
Subjects:
ID Code:
64710
Deposited By:
Deposited On:
22 May 2013 09:08
Refereed?:
Yes
Published?:
Published
Last Modified:
11 Feb 2020 08:11