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 analysis are reviewed. As background to the two-step approach, the distinction between exploratory and confirmatory analysis, the distinction between complementary approaches for theory testing versus predictive application, and some developments in estimation methods also are discussed.

/pdf/structural-equation-modeling-in-practice-a-review-and-1ue2omj492.pdf

Structural equation modeling in practice: a review and recommended two-step approach

SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality.

Structural Equations with Latent Variables.

Despite the widespread use of exploratory factor analysis in psychological research, researchers often make questionable decisions when conducting these analyses. This article reviews the major design and analytical decisions that must be made when conducting a factor analysis and notes that each of these decisions has important consequences for the obtained results. Recommendations that have been made in the methodological literature are discussed. Analyses of 3 existing empirical data sets are used to illustrate how questionable decisions in conducting factor analyses can yield problematic results. The article presents a survey of 2 prominent journals that suggests that researchers routinely conduct analyses using such questionable methods. The implications of these practices for psychological research are discussed, and the reasons for current practices are reviewed.

/pdf/evaluating-the-use-of-exploratory-factor-analysis-in-4fpz0boat8.pdf

Evaluating the use of exploratory factor analysis in psychological research.

Maximum likelihood factor analysis provides an effective method for estimation of factor matrices and a useful test statistic in the likelihood ratio for rejection of overly simple factor models. A reliability coefficient is proposed to indicate quality of representation of interrelations among attributes in a battery by a maximum likelihood factor analysis. Usually, for a large sample of individuals or objects, the likelihood ratio statistic could indicate that an otherwise acceptable factor model does not exactly represent the interrelations among the attributes for a population. The reliability coefficient could indicate a very close representation in this case and be a better indication as to whether to accept or reject the factor solution.

/pdf/a-reliability-coefficient-for-maximum-likelihood-factor-4cj3aorqbj.pdf

A reliability coefficient for maximum likelihood factor analysis

This thoroughly revised third edition of Harry H. Harman's authoritative text incorporates the many new advances made in computer science and technology over the last ten years. The author gives full coverage to both theoretical and applied aspects of factor analysis from its foundations through the most advanced techniques. This highly readable text will be welcomed by researchers and students working in psychology, statistics, economics, and related disciplines.

Modern Factor Analysis

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Modern Factor Analysis