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)

SOLAS is a general-purpose program for univariate statistical analysis with the ability to perform multiple imputation (MI) for dealing with missing data. Several completed data sets can be obtained and analyzed separately, and results can be combined so that extra variability due to missing data is taken into account. (SLD)

Assessed the robustness of an estimation method for multilevel and path analysis with hierarchical data proposed by B. Muthen (1989) with unequal groups and small sample sizes and in the presence of a low or high intraclass correlation. Simulation results show the effects of varying these conditions on the within-group and between-groups part of…

Examines some positions in various arguments related to the proper number of factors and proper number of steps when using structural equation models. Defines the issue in estimating structural equation models as a problem of specifying a model appropriately based on theoretical concerns and then diagnosing ills in the model as well as possible.…

Used Monte Carlo simulation to examine the performance of four missing data methods in structural equation models: (1)full information maximum likelihood (FIML); (2) listwise deletion; (3) pairwise deletion; and (4) similar response pattern imputation. Results show that FIML estimation is superior across all conditions of the design. (SLD)

Improper structures arising from the estimation of parameters in structural equation models (SEMs) are commonly an indication that the model is incorrectly specified. The use of boundary solutions cannot in general be recommended. Partly on the basis of theory given by Van Driel, and partly by example, suggestions are made for using the data as…

Sample covariance matrices constructed with pairwise deletion for randomly missing data were used in a simulation with three sample sizes and five levels of missing data (up to 50%). Parameter estimates were unbiased, parameter variability was largely explicable, and no sample covariance matrices were nonpositive definite except for 50% missing…

Considers models incorporating principal components from the perspectives of structural-equation modeling. These models include the following: (1) the principal-component analysis of patterned matrices; (2) multiple analysis of variance based on principal components; and (3) multigroup principal-components analysis. Discusses fitting these models…

Presents a new approach that extends conventional random coefficient growth models to incorporate a categorical latent trajectory variable representing latent classes or mixtures. Provides a didactic example of this new methodology using adolescent alcohol use data and discusses the method as a tool for mapping hypotheses of development onto…

Proposes the use of nonnested tests for the two stage least squares (2SLS) estimator of latent variable models to discriminate between scales. Compares the finite sample performance of these tests to structural equation modeling information-based criteria. Presents practical recommendations based on the Monte Carlo analysis. (SLD)

Studied the maximum likelihood estimation of unknown parameters in a general LISREL-type model with mixed polytomous and continuous data through Monte Carlo simulation. Proposes a model selection procedure for obtaining good models for the underlying substantive theory and discusses the effectiveness of the proposed model. (SLD)

This article reviews several basic statistical tools needed for modeling data with sampling weights that are implemented in Mplus Version 3. These tools are illustrated in simulation studies for several latent variable models including factor analysis with continuous and categorical indicators, latent class analysis, and growth models. The…

A widely and readily applicable covariance structure modeling approach is outlined that allows point and interval estimation of scale reliability with fixed components. The procedure employs only linear constraints introduced in a congeneric model, which after reparameterization permit expression of composite reliability as a function of…

In applications of structural equation modeling, it is often desirable to obtain measures of uncertainty for special functions of model parameters. This article provides a didactic discussion of how a method widely used in applied statistics can be employed for approximate standard error and confidence interval evaluation of such functions. The…

Problems found in the application of structural equation modeling to the theory of reasoned action are explored, and an alternative model specification is proposed that improves the fit of the data while leaving intact the structural part of the model being tested. Problems and the proposed alternative are illustrated. (SLD)