STRUCTURAL EQUATION MODELING IN PRACTICE: A REVIEW AND RECOMMENDED TWO-STEP APPROACH

@article{Anderson1988STRUCTURALEM,
  title={STRUCTURAL EQUATION MODELING IN PRACTICE: A REVIEW AND RECOMMENDED TWO-STEP APPROACH},
  author={James C. Anderson and David W. Gerbing},
  journal={Psychological Bulletin},
  year={1988},
  volume={103},
  pages={411-423}
}
In this article, we provide guidance for substantive researchers on the use of structural equation modeling in practice for theory testing and development. We present a comprehensive, two-step modeling approach that employs a series of nested models and sequential chi-square difference tests. We discuss the comparative advantages of this approach over a one-step approach. Considerations in specification, assessment of fit, and respecification of measurement models using confirmatory factor… 

Figures from this paper

Issues in applied structural equation modeling research

When using the popular structural equation modeling (SEM) methodology, the issues of sample size, method of parameter estimation, assessment of model fit, and capitalization on chance are of great

A Brief Guide to Structural Equation Modeling

To complement recent articles in this journal on structural equation modeling (SEM) practice and principles by Martens and by Quintana and Maxwell, respectively, the authors offer a consumer’s guide

Structural Equation Modeling and Regression: Guidelines for Research Practice

The article presents a running example which analyzes the same dataset via three very different statistical techniques and compares two classes of SEM: covariance-based SEM and partial-least-squaresbased SEM, and discusses linear regression models and guidelines as to when SEM techniques and when regression techniques should be used.

Structural Equation Modeling: Principles, Processes, and Practices

This is an excellent discussion of the best practices for applying structural equation modeling (SEM) and how to design a study for a within-group factor comparison unit of analysis research strategy.

Structural equation modeling with longitudinal data: strategies for examining group differences and reciprocal relationships.

  • A. Farrell
  • Psychology
    Journal of consulting and clinical psychology
  • 1994
This article describes the use of structural equation modeling with latent variables to examine group differences and test competing models about cause-effect relationships in passive longitudinal

A Comparison of Approaches to Forming Composite Measures in Structural Equation Models

A common practice in applications of structural equation modeling techniques is to create composite measures from individual items. The purpose of this article was to provide an empirical comparison

A Five-Step Guide to Conducting SEM Analysis in Counseling Research

The use of structural equation modeling (SEM), a second-generation multivariate analysis technique that determines the degree to which a theoretical model is supported by the sample data, is becoming

Covariance-Based Structural Equation Modeling in the Journal of Advertising: Review and Recommendations

In this article, we review applications of covariance-based structural equation modeling (SEM) in the Journal of Advertising (JA) starting with the first issue in 1972. We identify 111 articles from

Data Analysis: Structure Equation Modeling (SEM)

This chapter first provides a brief introduction about Structure Equation Modeling (SEM) and its definition and types, and reports data analysis of the measurement models for Learning Style, Managerial Tacit Knowledge, Adaptive Flexibility, and Expatriate Adjustment.

Assessment of Path Model Fit: Evidence of Effectiveness and Recommendations for use of the RMSEA-P

We review the development of path model fit measures for latent variable models and highlight how they are different from global fit measures. Next, we consider findings from two published simulation
...

References

SHOWING 1-10 OF 78 REFERENCES

Confirmatory Factor-Analytic Structures and the Theory Construction Process

The confirmatory factor-analytic model of analysis is discussed in terms of the general process of constructing social theory-the generation of parameter estimates for a proposed structure, the

Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory

In marketing applications of structural equation models with unobservable variables, researchers have relied almost exclusively on LISREL for parameter estimation. Apparently they have been little

A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators

A structural equation model is proposed with a generalized measurement part, allowing for dichotomous and ordered categorical variables (indicators) in addition to continuous ones. A computationally

Structural modeling and psychometrika: An historical perspective on growth and achievements

The field of linear structural equation modeling with continuous variables is reviewed. Trends in psychometric theory and data analysis across the five decades of publication ofPsychometrika are

Testing a simple structure hypothesis in factor analysis

The maximum-likelihood method is used to estimate the factor matrix and the factor correlation matrix directly without the use of rotation methods, and the likelihood-ratio technique isused to test the simple structure hypothesis.

Some Methods for Respecifying Measurement Models to Obtain Unidimensional Construct Measurement

Lack of unidimensionality in structural equation models most often represents misspecification. The authors review the necessary conditions for unidimensional measurement of constructs. Two methods

Methodology Review: Assessing Unidimensionality of Tests and ltenls

Various methods for determining unidimensionality are reviewed and the rationale of these methods is as sessed. Indices based on answer patterns, reliability, components and factor analysis, and

Multistructure Statistical Model Applied To Factor Analysis.

  • P. Bentler
  • Mathematics
    Multivariate behavioral research
  • 1976
A general statistical model for the multivariate analysis of mean and covariance structures is described, which has common-factor loadings that are invariant with respect to variable scaling and unique variances that must be positive.

Some contributions to efficient statistics in structural models: Specification and estimation of moment structures

Current practice in structural modeling of observed continuous random variables is limited to representation systems for first and second moments (e.g., means and covariances), and to distribution

The Effects of Sampling Error and Model Characteristics on Parameter Estimation for Maximum Likelihood Confirmatory Factor Analysis.

Monte Carlo methods were used to systematically study the effects of sampling error and model characteristics upon parameter estimates and their associated standard errors in maximum likelihood
...