Discusses linear structural relations, Two Stage Least Squares, and path analysis as statistical procedures that sometimes permit causal inferences from correlational findings. Even though two variables cannot be interpreted causally due to a possible but unknown third variable, these methods are appropriate for handling models with correlated…

Defines structural equation modeling as comprehensive, flexible approach to research design and data analysis. Provides conceptual overview of clinical research hypotheses that invite evaluation as structural equation models. Particular attention is devoted to hypotheses that are not adequately evaluated using traditional statistical models.…

Used structural equation modeling for comparative treatment outcome research conducted with heterogeneous clinical subpopulations within large multimodality treatment settings. Evaluated effect of early period of treatment on daily lives of 486 clients in 2 drug abuse treatment modalities (methadone maintenance and outpatient counseling).…

The effect that collapsing ordered polytomous variable scales has on structural equation measurement model parameter estimates was examined. Four parameter estimation procedures were investigated in a Monte Carlo study. Collapsing categories in ordered polytomous variables had little effect when latent projection procedures were used. (SLD)

Demonstrates how perspectives from covariance structural modeling and generalizability theory can be combined for a comprehensive assessment of latent constructs. This approach to examining variance components is illustrated by one- and two- facet designs, and can be extended to more complex designs. (MAK)

A Monte Carlo simulation study investigated the effects on 10 structural equation modeling fit indexes of sample size, estimation method, and model specification. Some fit indexes did not appear to be comparable, and it was apparent that estimation method strongly influenced almost all fit indexes examined, especially for misspecified models. (SLD)

Investigated the assumption that determining an adequate sample size in structural equation modeling can be aided by considering the number of parameters to be estimated. Findings from maximum likelihood confirmatory factor analysis support previous research on the effect of sample size, measured variable reliability, and the number of measured…

The sensitivity analysis of structural equation models when minor perturbation is introduced is investigated. An influence measure based on the general case weight perturbation is derived for the generalized least squares estimation, and an influence measure is developed for the special case deletion perturbation scheme. (Author/SLD)

This study with 203 college students used structural equation analysis and found that the relationship between students' academic stress and course grades was influenced by problem-focused coping and motivation, but not by emotion-focused coping. Greater academic stress covaried with lower course grades. (DB)

Conducted a meta-analysis focusing on the explanation of empirical Type I error rates for six principal classes of estimators. Generally, chi-square tests for overall model fit were found to be sensitive to nonnormality and the size of the model for all estimators, with the possible exception of elliptical estimators with respect to model size and…

Demonstrates empirically a structural equation modeling technique for group comparison of reliability and validity. Data, which are from a study of 495 mothers' attitudes toward pregnancy, have a one-factor measurement model and three sets of subpopulation comparisons. (SLD)

The purpose of this study was to determine the causal ordering (predominance) between attitude toward mathematics and achievement in mathematics in secondary school (grades 7-12). Structural equation models were employed to analyze data from the Longitudinal Study of American Youth. Results showed that achievement demonstrated causal predominance…

Multicollinearity is a well-known general problem, but it also seriously threatens valid interpretations in structural equation models. Illustrating this problem, J. Pietsch, R. Walker, and E. Chapman (2003) found paths leading to achievement were apparently much larger for self-efficacy (.55) than self-concept (-.05), suggesting--erroneously, as…

Multilevel structural equation modeling (MSEM) has been proposed as an extension to structural equation modeling for analyzing data with nested structure. We have begun to see a few applications in cross-cultural research in which MSEM fits well as the statistical model. However, given that cross-cultural studies can only afford collecting data…

The Bollen-Stine bootstrap can be used to correct for standard error and fit statistic bias that occurs in structural equation modeling (SEM) applications due to nonnormal data. The purpose of this article is to demonstrate the use of a custom SAS macro program that can be used to implement the Bollen-Stine bootstrap with existing SEM software.…