Overview: CALIS Procedure: Structural equation modeling is an important statistical tool in social and behavioral sciences. Structural equations express relationships among a system of variables that can be either observed variables (manifest variables) or unobserved hypothetical variables (latent variables). For an introduction to latent variable models, see Loehlin (2004), Bollen (1989b), Everitt (1984), or Long (1983); and for manifest variables with measurement errors, see Fuller (1987). In structural models, as opposed to functional models, all variables are taken to be random rather than having fixed levels. For maximum likelihood (default) and generalized least squares estimation in PROC CALIS, the random variables are assumed to have an approximately multivariate normal distribution. Nonnormality, especially high kurtosis, can produce poor estimates and grossly incorrect standard errors and hypothesis tests, even in large samples. Consequently, the assumption of normality is much more important than in models with nonstochastic exogenous variables. You should remove outliers and consider transformations of nonnormal variables before using PROC CALIS with maximum likelihood (default) or generalized least squares estimation. If the number of observations is sufficiently large, Browne’s asymptotically distribution-free (ADF) estimation method can be used. If your data sets contain random missing data, the full information maximum likelihood (FIML) method can be used...

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