This article presents relevant research on Bayesian methods and their major applications to modeling in an effort to lay out differences between the frequentist and Bayesian paradigms and to look at the practical implications of these differences. Before research is reviewed, basic tenets and methods of the Bayesian approach to modeling are…

Discusses the utility of multilevel confirmatory factor analysis and hierarchical linear modeling methods in testing measurement models in which the underlying attribute may vary as a function of levels of observation. A real dataset is used to illustrate the two approaches and their comparability. (SLD)

Outlines a method for comparing completely standardized solutions in multiple groups. The method is based on a correlation structure analysis of equal-size samples and uses the correlation distribution theory implemented in the structural equation modeling program RAMONA. (SLD)

An alternative baseline model for comparative fit assessment of structural equation models is described, evaluated, and compared to the standard "null" baseline model. The new "equal correlation" model constrains all variables to have equal, rather than zero, correlations, but all variances are free. Advantages and limitations…

Discusses concerns with the model proposed by E. Rigdon for computing incremental fit indices in which all measured variables are equally correlated (as opposed to the traditional null model). Proposes retaining the traditional null model with emphasis on the comparative fit of alternative models within a nested sequence that could include the new…

Continuing a discussion of the topic of fit assessment in structural equation modeling, this article accepts the compromise proposed by H. Marsh (1998) and offers tentative heuristic models for interpreting fit indices that involve the new baseline model proposed by E. Rigdon (1998). (SLD)

Explores the use of the Friedman method of ranks (H. Friedman, 1937) as an inferential procedure for evaluating competing models in structural-equation modeling. Describes the attractive features of this approach, but raises important issues regarding the lack of independence of observations and the power of the test. (SLD)

We consider a general type of model for analyzing ordinal variables with covariate effects and 2 approaches for analyzing data for such models, the item response theory (IRT) approach and the PRELIS-LISREL (PLA) approach. We compare these 2 approaches on the basis of 2 examples, 1 involving only covariate effects directly on the ordinal variables…

In addition to assessing the rate of change in outcome measures, it may be useful to test the significance of outcome changes during specific time periods within an entire observation period under study. While discussing the delta method and bootstrapping, this study demonstrates how to use these 2 methods to estimate the standard errors of the…

Presents an application of latent growth curve methodology to the analysis of longitudinal developmental change in alcohol consumption of 586 young adults, illustrating three approaches to the analysis of missing data: (1) multiple-sample structural equation modeling procedures; (2) raw maximum likelihood analyses; and (3) multiple modeling and…

Monte Carlo methods were used to study the accuracy and utility of estimators of overall error and error due to approximation in structural equation modeling. Effects of sample size, indicator reliabilities, and degree of misspecification were examined. The rescaled noncentrality parameter also was examined. Choosing among competing models is…

Used simulation to compare structural equation modeling methods for estimating and testing hypotheses about an interaction between continuous variables. Findings indicate that the two-stage least squares procedure exhibited more bias and lower power than the other methods. The Jaccard-Wan procedure (J. Jaccard and C. Wan, 1995) and maximum…