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 process. 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 superb beginner’s consultant to studying easy methods to organize enter documents to slot the main conventional different types of structural equation types with those courses. the fundamental rules and strategies for engaging in SEM are autonomous of any specific software program. Highlights of the second one version comprise: • overview of latent switch (growth) research versions at an introductory point • assurance of the preferred Mplus software • 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 booklet for college students and researchers in psychology, schooling, company, drugs, and different utilized social, behavioral, and healthiness sciences with constrained or no prior publicity to SEM. A prerequisite of uncomplicated facts via regression research is usually recommended. The publication usually attracts parallels among SEM and regression, making this previous wisdom precious.
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Extra info for A First Course in Structural Equation Modeling, 2nd edition
01) corresponds to l12 + q1 in the reproduced matrix S(g). 72 in the sixth row and fourth column of S, is l4l6 in the S(g) matrix; conversely, the counterpart of the element in the 3rd row and 1st column in S(g), viz. 43 in S, and so on. Now imagine setting the pairs of counterpart elements in S and S(g) equal to one another, from the top-left corner of S to its bottom-right corner; that is, for the model displayed in Fig. 16 = l92 + q9 is set. As a result of this equality setting, for the model in Fig.
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. Clearly, such a situation is not desirable given the fact that SEM models attempt to estimate what the parameter values would be in the population.
RULES FOR DETERMINING MODEL PARAMETERS 21 In that case, imposing this restriction decreases by two the number of unknown parameters to 19, because the three factor loadings involved in the constraint are not represented by three separate parameters anymore but only by a single one. Free, Fixed, and Constrained Parameters There are three types of model parameters that are important in conducting SEM analyses—free, fixed, and constrained. All parameters that are determined based on the above six rules are commonly referred to as free parameters (unless a researcher imposes additional constraints on some of them; see below), and must be estimated when fitting the model to data.
A First Course in Structural Equation Modeling, 2nd edition by Tenko Raykov, George A. Marcoulides