Describes two strategies for assessing measures of practical fit and the goodness of fit from structural equation models. Focuses on linear structural relations (LISREL), chi-square fit index, practical fit indices, and hierarchical analyses. Includes empirical example of hierarchical model procedures used with practical and statistical indices to…

Explores the four-step procedure for testing structural equation models and outlines some problems with the approach advocated by L. Hayduk and D. Glaser (2000) and S. Mulaik and R. Milsap (2000). Questions the idea that there is a "correct" number of constructs for a given phenomenon. (SLD)

Describes the integration of programmatic theory and structural equation modeling to serve as the basic intellectual machinery for designing and evaluating behavioral interventions. Illustrates this integration with the example of a program to reduce sexual risk taking. (SLD)

Simulated data for factor analytic models is used in the evaluation of three methods for controlling Type I errors: (1) the standard approach that involves testing each parameter at the 0.05 level; (2) the Bonferroni approach; and (3) a simultaneous test procedure (STP). Advantages offered by the Bonferroni approach are discussed. (SLD)

Focuses on the four-step method (four nested models) of structural equation modeling advocated by S. Mulaik (1997, 1998), discussing the limitations of the approach and considering the tests and criteria to be used in moving among the four steps. (SLD)

Defends the four-step approach to structural equation modeling based on testing sequences of models and points out misunderstandings of opponents of the approach. The four-step approach allows the separation of respective constraints within a structural equation model. (SLD)

Neither the four-step model nor the one-step procedure can actually tell whether the researcher has the right number of factors in structural equation modeling. In fact, for reasons discussed, a simple formulaic approach to the correct specification of models does not yet exist. (SLD)

Replies to commentaries on the four-step approach to structural equation modeling, pointing out the strengths and weaknesses of each argument and ultimately concluding that the four-step model is subject to criticisms that can be addressed to factor analysis as well. (SLD)

Evaluated the bootstrap method under varying conditions of nonnormality, sample size, model specification, and number of bootstrap samples drawn from the resampling space. Results for the bootstrap suggest the resampling-based method may be conservative in its control over model rejections, thus having an impact on the statistical power associated…

The relationship between attention and general intelligence was investigated considering the different types of attention: alertness, sustained attention, focused attention, attentional switching, divided attention, attention according to the supervisory attentional system, attention as inhibition, spatial attention, attention as planning,…

This article illustrates the use of the SAS system for Monte Carlo simulation work in structural equation modeling (SEM). Data generation procedures for both multivariate normal and nonnormal conditions are discussed, and relevant SAS codes for implementing these procedures are presented. A hypothetical example is presented in which Monte Carlo…

The aim of this study was to present and initially test a model of escalation to verbal and physical aggression among Israeli youths. Stratified sampling was used to obtain data from 799 students in the 7th, 8th, and 9th grades of junior high schools in a northern Israeli city and its suburbs. A structural equation model (SEM) analysis confirmed…

To date, finite mixtures of structural equation models (SEMMs) have been developed and applied almost exclusively for the purpose of providing model-based cluster analyses. This type of analysis constitutes a direct application of the model wherein the estimated component distributions of the latent classes are thought to represent the…

A covariance structure modeling method to test equality in proportions explained variance in studied unobserved dimensions by means of latent predictors is outlined. The procedure is applicable with multiple-indicator, structural equation models where of interest is to compare the predictive power of sets of latent independent variables for given…

Quality of life (QOL) has become an important concept for health care. As QOL is a multidimensional concept that is best evaluated by a number of latent constructs, it is well recognized that latent variable models, such as exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are useful tools for analyzing QOL data. Recently,…