By Tenko Raykov, George A. Marcoulides

During this ebook, authors Tenko Raykov and George A. Marcoulides introduce scholars to the fundamentals of structural equation modeling (SEM) via a conceptual, nonmathematical procedure. For ease of knowing, the few mathematical formulation offered are utilized in a conceptual or illustrative nature, instead of a computational one. that includes examples from EQS, LISREL, and Mplus, a primary path in Structural Equation Modeling is a wonderful beginner’s advisor to studying how one can arrange enter documents to slot the main universal kinds of structural equation versions with those courses. the fundamental rules and techniques for undertaking SEM are self reliant of any specific software program. Highlights of the second one version comprise: • evaluation of latent swap (growth) research types at an introductory point • assurance of the preferred Mplus application • up to date examples of LISREL and EQS • A CD that includes all the text’s LISREL, EQS, and Mplus examples. a primary path in Structural Equation Modeling is meant as an introductory publication for college kids and researchers in psychology, schooling, company, drugs, and different utilized social, behavioral, and health and wellbeing sciences with constrained or no past publicity to SEM. A prerequisite of uncomplicated data via regression research is usually recommended. The booklet often attracts parallels among SEM and regression, making this previous wisdom worthwhile.

**Read Online or Download A First Course in Structural Equation Modeling, 2nd edition PDF**

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**Extra info for A First Course in Structural Equation Modeling, 2nd edition**

**Example text**

Although it may happen that multivariate normality holds when all observed variables are individually normally distributed, it is desirable to also examine bivariate normality that is generally not a consequence of univariate normality. In fact, if the observations are from a multivariate normal distribution, each bivariate distribution should also be normal, like each univariate distribution. , Tabachnik & Fidell, 2001). , Johnson & Wichern, 2002; Khattree & Naik, 1999; Marcoulides & Hershberger, 1997).

In fact, unless its metric is defined, the scale of the latent variable will remain indeterminate. Subsequently, this will lead to model-estimation problems and unidentified parameters and models (discussed later in this chapter). For any independent latent variable included in a given model, the metric can be fixed in one of two ways that are equivalent for this purpose. Either its variance is set equal to a constant, usually 1, or a path going out of the latent variable is set to a constant (typically 1).

Conversely, a researcher may elect to free some initially fixed parameters, rendering them free parameters, after making sure of course that the model remains identified (see below). The third type of parameters are called constrained parameters, also sometimes referred to as restricted or restrained parameters. Constrained parameters are those that are postulated to be equal to one another—but their value is not specified in advance as is that of fixed parameters—or involved in a more complex relationship among themselves.