By Tenko Raykov, George A. Marcoulides
During this publication, 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 realizing, 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 tips to manage enter documents to slot the main generic different types of structural equation versions with those courses. the elemental rules and strategies for carrying out SEM are self sufficient of any specific software program. Highlights of the second one version contain: • overview of latent switch (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 direction in Structural Equation Modeling is meant as an introductory ebook for college students and researchers in psychology, schooling, enterprise, medication, and different utilized social, behavioral, and health and wellbeing sciences with constrained or no earlier publicity to SEM. A prerequisite of simple facts via regression research is suggested. The booklet often attracts parallels among SEM and regression, making this past wisdom beneficial.
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Extra info for A First Course in Structural Equation Modeling, 2nd edition
Which one of these models is better and which one is to be ruled out, can only be decided on the basis of a sound body of substantive knowledge about the studied phenomenon. This is partly the reason why substantive considerations are so important in model-fit evaluation. In addition, one can also evaluate the validity of a proposed model by conducting replication studies. The value of a given model is greatly enhanced if it can be replicated in new samples from the same studied population. Parameter Estimate Signs, Magnitude, and Standard Errors It is worth reiterating at this point that one cannot in general meaningfully interpret a model solution provided by SEM software if the underlying numerical minimization routine has not converged, that is has not ended after a finite number of iterations.
See Appendix to this chapter). For ease of computation, most programs make use of matrix algebra, with the software in effect determining each of the parametric expressions involved in these p(p+1)/2 equations. This occurs quite automatically once a researcher has communicated to the program the model with its parameters (and a few other related details discussed in the next chapter). How Good Is a Proposed Model? The previous section illustrated how a given structural equation model leads to a reproduced covariance matrix S(g) that is fit to the observed sample covariance matrix S through appropriate choice of values for the model parameters.
3) To obtain Equation 3, the following two facts regarding the model in Fig. 6 are also used. First, the covariance of the residuals E1 and E2, and the covariance of each of them with the factor F1, are equal to 0 according to our earlier assumptions when defining the model (note that in Fig. , Var(F1) = 1). 25 PARAMETER ESTIMATION Similarly, using Law 2, the covariance between the observed variables V1 and V4 say (each loading on a different factor) is determined as follows: Cov(V1,V4) = Cov(llF1 + E1, l4F2 + E4) = l1l4 Cov(F1,F2) + l1 Cov(F1,E4) + l4 Cov(E1,F2) + Cov(E1,E4) = l1l4f21, (4) where f21 (Greek letter phi) denotes the covariance between the factors F1 and F2.
A First Course in Structural Equation Modeling, 2nd edition by Tenko Raykov, George A. Marcoulides