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A program for the analysis of factorial invariance in latent variable models is LISREL. Bollen (2002) presented an analysis of his data from a factor invariance model in latent variable modeling by LISREL. The program is freely available from http://www.lislab.org.
A database for CFA and LISREL calculations. Systematic testing is essential for empirically supported theories to remain valid (SEM). Just like Bollen’s study, this study includes a procedure for comparing a newly developed SEM model with a previously established model based on an empirical model (sample data from a previous study). A similar procedure was used for testing of a newly developed SEM model by Freudenberg & Cheng (1996). Additionally, the program allows configuration and standardization of model parameters with respect to model complexity; thus, it creates consistency of results. The configuration and standardization process contains various estimation procedures: configural baseline adjustment (Boruchowitz 1996), multigroup comparison of factor loadings (Bryk & Raudenbush 1992), and covariance structure comparisons (Kokkinen 1998). The program also contains options for testing measurement invariance. It is specifically designed for experimental data; however, it can be used for any data type for which LISREL can be used, as it can be viewed as a multivariate estimation program.
The Bayesian analysis using Markov chain Monte Carlo simulation is performed by a software named LISREL. A Bayesian analysis provides theoretical and computational advantages as compared to the classical statistical methods. Although LISREL is very popular, it is significantly time-consuming, and some users are afraid to perform the Bayesian analysis in their data processing. Therefore, there is a need for a user-friendly software, which provides the Bayesian analysis with high efficiency. Because CFA allows us to compare the data fit of various models, it would be highly beneficial for computer users to use the CFA method as an alternative to Bayesian analysis.
The final short version consisted of 18 items with three items for each subscale (table 1 ). Item reduction was possible without loss of model fit; in fact, its overall fit was better than the full version. Due to a decrease in the number of estimated parameters, the Normal Theory Weighted Least Square 2 significantly decreased to 530.427. RMSEA still indicated reasonable fit. The value of IFI improved to 0.
The aim of this study is to compare the generalizability estimates from different structural equation modelling (SEM) software packages on data simulated with different degrees of correlation among latent variables. Software packages included in the comparison are Mplus, LISREL, EQS, SEM, and RMAR (Gordis 2007). The data represent a simulated multi-population dataset that allow assessment of the generalizability of statistical models estimated in that population to other populations. A simulation study was conducted to examine how generalizability estimates for five typical SEM software packages are sensitive to the assumed inter- and intra-class correlation between latent variables (i.e., different levels of correlation among latent variables). The main results show that all five software packages can be used to estimate the parameters of a simulated data set. Moreover, the generalizability of the estimated parameters to other simulated data sets is very stable and the model fit and parameter estimates are not sensitive to the degree of assumed correlation among latent variables in the simulated data.
The purpose of the paper is to examine the ability of the structural equation modeling (SEM) software packages available. This was motivated by an increasing number of studies indicating that what the researcher believes is the conceptual model may differ from the actual fit of the model to the data. Unlike the previous literature, the present paper does not focus on investigating the predictive power of the software. Instead, the focus is on an assessment of the ability of the software packages to provide adequate descriptions of the data (model fit), and the ability to obtain adequate coefficients on the primary factors of interest. The paper compares six SEM software packages: 1) Mplus; 2) LISREL; 3) EQS; 4) SEM; 5) RMAR (Gordis 2007); 6) Mx.
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