Opportunities for Improving Consumer Research through Latent Variable Structural Equation Modeling

This article discusses several advantages of latent variable structural equation modeling (LVSEM), and the potential it has for solving some fundamental problems hindering research in the field. The advantages highlighted include the ability to control for measurement error; an enhanced ability to test the effects of experimental manipulations; the ability to test complex theoretical structures; the ability to link micro and macro perspectives; and more powerful ways to assess measure reliability and validity. My hope is to sensitize researchers to some of the key limitations of currently used alternative methodologies, and demonstrate how LVSEM can help to improve theory testing and development in our discipline.

[1]  Michael D. Shields,et al.  Mapping Management Accounting: Graphics and Guidelines for Theory-Consistent Empirical Research , 2003 .

[2]  S. Kozlowski,et al.  Multilevel Theory, Research, and Methods in Organizations: Foundations, Extensions, and New Directions , 2000 .

[3]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[4]  J. Steenkamp,et al.  Assessing Measurement Invariance in Cross-National Consumer Research , 1998 .

[5]  David Kaplan,et al.  A didactic example of multilevel structural equation modeling applicable to the study of organizations , 1997 .

[6]  Roger L. Brown Assessing specific mediational effects in complex theoretical models , 1997 .

[7]  John G. Lynch,et al.  A Bayesian Analysis of the Information Value of Manipulation and Confounding Checks in Theory Tests , 1995 .

[8]  Terry E. Duncan,et al.  Modeling the processes of development via latent variable growth curve methodology , 1995 .

[9]  M. Browne,et al.  Alternative Ways of Assessing Model Fit , 1992 .

[10]  Richard A. Spreng,et al.  How Does Motivation Moderate the Impact of Central and Peripheral Processing on Brand Attitudes and Intentions , 1992 .

[11]  R. Lennox,et al.  Conventional wisdom on measurement: A structural equation perspective. , 1991 .

[12]  M. Ronald Buckley,et al.  Measurement Error and Theory Testing in Consumer Research: An Illustration of the Importance of Construct Validation , 1988 .

[13]  Joseph A. Cote,et al.  Estimating Trait, Method, and Error Variance: Generalizing across 70 Construct Validation Studies , 1987 .

[14]  John O. Summers,et al.  Checking the Success of Manipulations in Marketing Experiments , 1986 .

[15]  Scott B. MacKenzie,et al.  The Role of Attitude toward the Ad as a Mediator of Advertising Effectiveness: A Test of Competing Explanations: , 1986 .

[16]  G. Albaum,et al.  A Meta-Analysis of Effect Sizes in Consumer Behavior Experiments , 1985 .

[17]  William R. Darden,et al.  Causal Models in Marketing , 1980 .

[18]  T. Cook,et al.  Quasi-experimentation: Design & analysis issues for field settings , 1979 .