Used simulation to study two different approaches to latent variable interaction modeling with continuous observed variables: (1) a LISREL 8.30 program and (2) data analysis through PRELIS2 and SIMPLIS programs. Results show that parameter estimation was similar but standard errors were different. Discusses differences in ease of implementation.…

Established a new criterion for the identification of recursive linear models in which some errors are correlated. Shows that identification is assured as long as error correlation does not exist between a cause and its direct effect; no restrictions are imposed on errors associated with indirect causes. (SLD)

Proposes true score models that can account for correlated errors and their effect on coefficient alpha. These models allow random measurement errors on earlier items to affect directly or indirectly the scores on later items. Conditions under which coefficient alpha may yield spuriously high estimates or reliability are discussed. (SLD)

Explains why, when one is using a bootstrapping approach for generating empirical standard errors for parameters of interest, the researchers must choose to fix an indicator path rather than the latent variable variance for the empirical standard errors to be generated properly. (SLD)

Shows that the conventional noncentrality parameter estimator of covariance structure models, currently implemented in popular structural modeling programs, possesses asymptotically potentially large bias, variance, and mean squared error (MSE). Presents a formal expression for its large-sample bias and quantifies large-sample bias and MSE. (SLD)

Investigated the impact of categorization on confirmatory factor analysis parameter estimates, standard errors, and five ad hoc fit indexes through simulation studies. Results replicate some previous studies but also suggest that tests of parameter estimates will be underestimated and the amount of underestimation will increase as saturation…

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…

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…

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)

In addition to assessing the rate of change in outcome measures, it may be useful to test the significance of outcome changes during specific time periods within an entire observation period under study. While discussing the delta method and bootstrapping, this study demonstrates how to use these 2 methods to estimate the standard errors of the…

Studied whether moving average or autoregressive moving average models fit two longitudinal data sets previously thought to possess quasi-simplex structures better than the quasi-simplex, one-factor, or autoregressive models. Results of a Monte Carlo study show the importance of evaluating the fit, propriety, and parsimony of models before one…

We consider the question of how variation in the number and reliability of indicators affects the power to reject the hypothesis that the regression coefficients are zero in latent linear regression analysis. We show that power remains constant as long as the coefficient of determination remains unchanged. Any increase in the number of indicators…

This article describes a technique to analyze randomized response data using available structural equation modeling (SEM) software. The randomized response technique was developed to obtain estimates that are more valid when studying sensitive topics. The basic feature of all randomized response methods is that the data are deliberately…

Two methods, direct maximum likelihood (ML) and the expectation maximization (EM) algorithm, can be used to obtain ML parameter estimates for structural equation models with missing data (MD). Although the 2 methods frequently produce identical parameter estimates, it may be easier to satisfy missing at random assumptions using EM. However, no…

The primary purpose of this study was to examine relative performance of 2 power estimation methods in structural equation modeling. Sample size, alpha level, type of manifest variable, type of specification errors, and size of correlation between constructs were manipulated. Type 1 error rate of the model chi-square test, empirical critical…