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

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Latent variable modeling in congruence research: current problems and future directions. / Edwards, Jeffrey R.
In: Organizational Research Methods, Vol. 12, No. 1, 2009, p. 34-62.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Edwards, JR 2009, 'Latent variable modeling in congruence research: current problems and future directions', Organizational Research Methods, vol. 12, no. 1, pp. 34-62. https://doi.org/10.1177/1094428107308920

APA

Edwards, J. R. (2009). Latent variable modeling in congruence research: current problems and future directions. Organizational Research Methods, 12(1), 34-62. https://doi.org/10.1177/1094428107308920

Vancouver

Edwards JR. Latent variable modeling in congruence research: current problems and future directions. Organizational Research Methods. 2009;12(1):34-62. doi: 10.1177/1094428107308920

Author

Edwards, Jeffrey R. / Latent variable modeling in congruence research : current problems and future directions. In: Organizational Research Methods. 2009 ; Vol. 12, No. 1. pp. 34-62.

Bibtex

@article{97333793829948349c915944e03cff2e,
title = "Latent variable modeling in congruence research: current problems and future directions",
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.",
keywords = "congruence, difference scores , polynomial regression , latent variables , structural equation modeling",
author = "Edwards, {Jeffrey R.}",
year = "2009",
doi = "10.1177/1094428107308920",
language = "English",
volume = "12",
pages = "34--62",
journal = "Organizational Research Methods",
issn = "1094-4281",
publisher = "SAGE Publications Inc.",
number = "1",

}

RIS

TY - JOUR

T1 - Latent variable modeling in congruence research

T2 - current problems and future directions

AU - Edwards, Jeffrey R.

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - congruence

KW - difference scores

KW - polynomial regression

KW - latent variables

KW - structural equation modeling

U2 - 10.1177/1094428107308920

DO - 10.1177/1094428107308920

M3 - Journal article

VL - 12

SP - 34

EP - 62

JO - Organizational Research Methods

JF - Organizational Research Methods

SN - 1094-4281

IS - 1

ER -