Structural equation modeling is an ideal data analytical tool for testing complex relationships among many analytical variables. It can simultaneously test multiple mediating and moderating relationships, estimate latent variables on the basis of related measures, and address practical issues such as nonnormality and missing data. To test the…

A latent variable analysis procedure for evaluation of reliability coefficients for 2-level models is outlined. The method provides point and interval estimates of group means' reliability, overall reliability of means, and conditional reliability. In addition, the approach can be used to test simple hypotheses about these parameters. The…

The authors modeled the relationships between students' perceptions of interactive technology in terms of whether it helps them pay more attention and be better prepared in a Consumer Behavior course and their attitude toward and satisfaction with it. The results suggest that students who feel they pay more attention due to the use of Interactive…

A readily implemented procedure is discussed for interval estimation of indexes of interrelationship between items from multiple-component measuring instruments as well as between items and total composite scores. The method is applicable with categorical (ordinal) observed variables, and can be widely used in the process of scale construction,…

Presents a didactic collection of covariance and mean structure hypotheses that can be tested using a widely applicable and easy to use structural equation modeling approach. The method is useful when the goal is to examine the observed multivariable structure or test hypotheses regarding interrelationships in measures and when large samples are…

Proposes an extension of the propensity score adjustment method to the analysis of group differences on latent variable models. Uses multiple indicators-multiple causes (MIMIC) structural equation modeling to test hypotheses about treatment group differences. Discusses the role of factorial invariance as it relates to this approach. (SLD)

A type of data layout that may be considered as an extension of the two-way random effects analysis of variance is characterized and modeled based on group invariance. The data layout seems to be suitable for several scenarios in psychometrics, including the one in which multiple measurements are taken on each of a set of variables, and the…

Discusses two criticisms raised by L. Hayduk and D. Glaser of the most commonly used point estimate of the Root Mean Square Error (RMSEA) and points out misconceptions in their discussion. Although there are apparent flaws in their arguments, the RMSEA is open to question for several other reasons. (SLD)

Demonstrates the use of the well-known Bayes factor in the Bayesian literature for hypothesis testing and model comparison in general two-level structural equation models. Shows that the proposed method is flexible and can be applied to situations with a wide variety of nonnested models. (SLD)

This article offers different examples of how to fit latent growth curve (LGC) models to longitudinal data using a variety of different software programs (i.e., LISREL, Mx, Mplus, AMOS, SAS). The article shows how the same model can be fitted using both structural equation modeling and multilevel software, with nearly identical results, even in…

This field study examined how early socialization experiences affect new employee mastery of socialization content and socialization outcomes. New employees reported the realism of their preentry knowledge and the helpfulness of socialization agents. A follow-up survey assessed mastery of socialization content along with role clarity, job…

Goodness-of-fit (GOF) indexes provide "rules of thumb"?recommended cutoff values for assessing fit in structural equation modeling. Hu and Bentler (1999) proposed a more rigorous approach to evaluating decision rules based on GOF indexes and, on this basis, proposed new and more stringent cutoff values for many indexes. This article discusses…

Structural equation modeling (SEM) refers to a family of statistical techniques that explores the relationships among a set of variables. Structural equation modeling provides an extremely versatile method to model very specific hypotheses involving systems of variables, both measured and unmeasured. Researchers can use SEM to study patterns of…