Criteria for evaluating structural equation models with latent variables are defined, critiqued, and illustrated. An overall program for model evaluation is proposed based upon an interpretation of converging and diverging evidence. Model assessment is considered to be a complex process mixing statistical criteria with philosophical, historical, and theoretical elements. Inevitably the process entails some attempt at a reconcilation between so-called objective and subjective norms.

On the evaluation of structural equation models

This article describes the development and validation process for an instrument to assess goal orientation (an individual disposition toward developing or validating one's ability in achievement settings). In contrast to previous goal orientation instruments, three goal orientation dimensions are identified (learning, avoid, and prove), and the instrument is domain specific to work settings. The results of exploratory factor analysis, reliability analysis (internal consistency and test-retest), confirmatory factor analysis, and nomological network analysis all support the conclusion that the instrument operationalizes the theorized three-dimensional construct.

Development and validation of a work domain goal orientation instrument.

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"How Big Is Big Enough?": Sample Size and Goodness of Fit in Structural Equation Models with Latent Variables.

The author proposes an alternative estimation technique for quadratic and interaction latent variables in structural equation models using LISREL, EQS, and CALIS. The technique specifies these vari...

A Parsimonious Estimating Technique for Interaction and Quadratic Latent Variables

Because structural equation modeling (SEM) has become a very popular data-analytic technique, it is important for clinical scientists to have a balanced perception of its strengths and limitations. We review several strengths of SEM, with a particular focus on recent innovations (e.g., latent growth modeling, multilevel SEM models, and approaches for dealing with missing data and with violations of normality assumptions) that underscore how SEM has become a broad data-analytic framework with flexible and unique capabilities. We also consider several limitations of SEM and some misconceptions that it tends to elicit. Major themes emphasized are the problem of omitted variables, the importance of lower-order model components, potential limitations of models judged to be well fitting, the inaccuracy of some commonly used rules of thumb, and the importance of study design. Throughout, we offer recommendations for the conduct of SEM analyses and the reporting of results.

Michael W. Browne

Papers

Topics in Applied Multivariate Analysis: COVARIANCE STRUCTURES