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Those new to structural equation modeling (SEM) often encounter problems with convergence, software errors, or poor fitting models. Some basic suggestions are offered to help reduce these problems and avoid some common error messages encountered when first testing models. The tips concern initial data management steps, specification of latent variables, avoidance of factor identification problems, special precautions when estimating means, addressing poor measurement model fit, using multistep testing, fitting full structural models, attentiveness to interpretation pitfalls, and troubleshooting software warnings and errors. The advice provided is intended to help save time, effort, and trials and tribulations often encountered by beginner modelers. [ABSTRACT FROM AUTHOR]

Proposes a novel structural modeling approach based on latent growth curve model specifications for use with dyadic data. The approach allows researchers to test more sophisticated causal models, incorporate latent variables, and estimate more complex error structures than is currently possible using hierarchical linear modeling or multilevel structural equation models. (SLD)

A Monte Carlo simulation examined estimation difficulties and parameter and standard error bias for mean and variance estimates of binary latent growth curve models using mean and variance adjusted diagonally weighted least squares (WLSMV) and robust maximum likelihood (MLR). Small and medium effects of slope means and variances for longitudinal designs with three, five, and seven time points and sample sizes of 100, 200, 500, and 1000 were examined. Results indicated that more time points, larger sample size, and more symmetric distributions were associated with fewer improper solutions, lower parameter and standard error bias, better Type I error rates, and better coverage. WLSMV and MLR performed acceptably with at least five time points and sample size of 500, but WLSMV performance depended on the model specification. Three time points and 100 cases appeared to be too few for accurate estimation of binary latent growth curve models for any method. [ABSTRACT FROM AUTHOR]

The current widespread availability of software packages with estimation features for testing structural equation models with binary indicators makes it possible to investigate many hypotheses about differences in proportions over time that are typically only tested with conventional categorical data analyses for matched pairs or repeated measures, such as McNemar’s chi-square. The connection between these conventional tests and simple longitudinal structural equation models is described. The equivalence of several conventional analyses and structural equation models reveals some foundational concepts underlying common longitudinal modeling strategies and brings to light a number of possible modeling extensions that will allow investigators to pursue more complex research questions involving multiple repeated proportion contrasts, mixed between-subjects × within-subjects interactions, and comparisons of estimated membership proportions using latent class factors with multiple indicators. Several models are illustrated, and the implications for using structural equation models for comparing binary repeated measures or matched pairs are discussed. [ABSTRACT FROM AUTHOR]