Structural Equation Modeling: A Practical Introduction

Shaun McQuitty · Marco Wolf

65 citations0/0 files storedJournal of African Business

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Psychometric Methodologies and Testing Statistical Methods and Applications Business Strategies and Management Research

Abstract

Structural equation modeling (SEM) is a popular statistical technique that has become an essential tool for business academics and practitioners. The technique is particularly well suited for evaluating the relationships among any number of observed and latent variables. Thus, typical applications for SEMs are model and theory testing, and scale development. The purpose of this article is to introduce the technique and to explain its use in practical terms that do not require a strong statistical background. Two previous Journal of African Business articles are used to demonstrate SEM applications, and the article concludes with a brief introduction to SEM software packages.

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References

1981Evaluating Structural Equation Models with Unobservable Variables and Measurement Error 1999Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives 2003Common method biases in behavioral research: A critical review of the literature and recommended remedies.1951Coefficient Alpha and the Internal Structure of Tests 1992Alternative Ways of Assessing Model Fit 1989Structural Equations with Latent Variables 1991Structural Equations with Latent Variables.1979A Paradigm for Developing Better Measures of Marketing Constructs

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