A general model for two-level multivariate data, with responses possibly missing at random, is described. The model combines regressions on fixed explanatory variables with structured residual covariance matrices. The likelihood function is reduced to a form enabling computational methods for estimating the model to be devised. (Author)

Methods by which structural models correct for the effects of attenuation due to measurement error are reviewed, and implications of such disattenuation for interpreting the results of structural equation models are considered. Recommendations are made for improving the practice of disattenuation, and caution is urged in drawing inferences based…

An algorithm for obtaining initial values for the minimization process in covariance structure analysis is developed that is more generally applicable for computing parameters connected to latent variables than the currently existing ones. The algorithm is formulated in terms of the RAM model but can be extended. (SLD)

In experiments with paired comparisons, judges are occasionally allowed to express indifference between alternatives. For the analysis of such data, models for paired comparisons with ties are needed. Several models with tie parameters are reviewed. All are extensions of the basic models of L. L. Thurstone (1927) and R. A. Bradley and M. E. Terry…

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)

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)

A Monte Carlo simulation study was conducted to investigate the effects of sample size, estimation method, and model specification on structural equation modeling (SEM) fit indices. Based on a balanced 3x2x5 design, a total of 6,000 samples were generated from a prespecified population covariance matrix, and eight popular SEM fit indices were…

A reparameterization is formulated that yields estimates of scale-invariant parameters in recursive path models with latent variables, and (asymptotically) correct standard errors, without the use of constrained optimization. The method is based on the logical structure of the reticular action model. (Author)

In this Monte Carlo study, the effects of four factors on structural equation modeling (SEM) fit indices and parameter estimates were investigated. The 14,400 samples generated were fitted to 3 SEM models with different degrees of model misspecification. Effects of data nonnormality, estimation method, and sample size are noted. (SLD)

Characteristic curve approaches for linking parameters from the generalized partial credit model were examined for cases in which common (anchor) items are calibrated separately in two groups. Three of these approaches are simple extensions of the test characteristic curve (TCC), item characteristic curve (ICC), and operating characteristic curve…

Presents a method for estimating the random coefficients model using covariance structure modeling and allowing one to estimate both fixed and random effects. The method is applied to real and simulated data, including marriage data from J. Belsky and M. Rovine (1990). (SLD)

A Monte Carlo study was conducted to assess the effects of some potential confounding factors on structural equation modeling (SEM) fit indices and parameter estimates for both true and misspecified models. The factors investigated were data nonnormality, SEM estimation method, and sample size. Based on the fully crossed and balanced 3x3x4x2…

Basic philosophical and statistical issues in structural equation modeling (SEM) are reviewed, including model conceptualization, identification, and parameter estimation and data-model-fit assessment and model modification. These issues should be addressed before the researcher uses any of the new generation of SEM software. (SLD)

A computationally efficient three-stage estimator of thresholds and covariance structure parameters is prepared for analysis of structural equation models with polytomous variables. The method is based on partition maximum likelihood and generalized least squares estimation. An analysis of questionnaire responses of 739 young adults illustrates…

A two-stage approach based on the rationale of maximum likelihood and generalized least-squares methods is developed to analyze the general structural equation model for continuous and polytomous variables. Some illustrative examples and a small simulation study (50 replications) are reported. (SLD)