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 method. For ease of realizing, the few mathematical formulation provided 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 the right way to manage enter documents to slot the main popular varieties of structural equation versions with those courses. the fundamental principles and techniques for undertaking SEM are self sustaining of any specific software program. Highlights of the second one version contain: • evaluation of latent swap (growth) research versions at an introductory point • assurance of the preferred Mplus software • up to date examples of LISREL and EQS • A CD that comprises all the text’s LISREL, EQS, and Mplus examples. a primary path in Structural Equation Modeling is meant as an introductory booklet for college students and researchers in psychology, schooling, enterprise, drugs, and different utilized social, behavioral, and health and wellbeing sciences with restricted or no prior publicity to SEM. A prerequisite of simple facts via regression research is suggested. The booklet usually attracts parallels among SEM and regression, making this past wisdom worthwhile.
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Additional resources for A First Course in Structural Equation Modeling, 2nd edition
The corresponding expression of model parameters at the same position in the model reproduced covariance matrix. Therefore, fitting a structural equation model is conceptually equivalent to solving this system of equations obtained according to the consequences of the model, whereby this solution is sought in an optimal way that is discussed in the next section. The preceding discussion in this section also demonstrates that the model presented in Fig. , Chapter 6) that is reproduced by that model in terms of PARAMETER ESTIMATION 27 particular expressions—in general, nonlinear functions—of unknown model parameters.
Model Implications and Reproduced Covariance Matrix As mentioned earlier in this section, any considered model has certain implications for the variances and covariances (and means, if included in the analysis) of the involved observed variables. In order to see these implications, the four laws for variances and covariances can be used. For example, consider the first two manifest variables V1 and V2 presented in Equations 1 (see the section “Rules for Determining Model Parameters” and Fig. 6).
Although the two numerical examples in this section were deliberately simple, they nonetheless illustrate the nature of similar problems that can occur in the context of structural equation models. Recall from earlier sections that SEM can be thought of as an approach to solving, in an optimal way, a system of equations—those relating the elements of the sample covariance matrix S with their counterparts in the model reproduced covariance matrix S(g). It is possible then, depending on the model, that for some of its parameters the system may have infinitely many solutions.
A First Course in Structural Equation Modeling, 2nd edition by Tenko Raykov, George A. Marcoulides