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 technique. For ease of figuring out, the few mathematical formulation awarded 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 consultant to studying how you can manage enter documents to slot the main familiar varieties of structural equation types with those courses. the elemental rules and strategies for engaging in SEM are self sustaining of any specific software program. Highlights of the second one variation contain: • overview of latent swap (growth) research versions at an introductory point • assurance of the preferred Mplus application • up-to-date examples of LISREL and EQS • A CD that includes the entire text’s LISREL, EQS, and Mplus examples. a primary path in Structural Equation Modeling is meant as an introductory publication for college students and researchers in psychology, schooling, company, drugs, and different utilized social, behavioral, and overall healthiness sciences with constrained or no past publicity to SEM. A prerequisite of easy data via regression research is suggested. The publication often attracts parallels among SEM and regression, making this earlier wisdom invaluable.

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

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**Additional resources for A First Course in Structural Equation Modeling, 2nd edition**

**Sample text**

6). In order to clarify this feature of the estimation process, let us look again at the path diagram in Fig. 6 and the associated model definition Equations 1 in the previous section. As indicated in earlier discussions the model represented by this path diagram, or system of equations, makes certain assumptions about the relationships between the involved variables. Hence, the model has specific implications for their variances and covariances. These implications can be worked out using a few simple relations that govern the variances and covariances of linear combinations of variables.

The unweighted least squares (ULS) method uses as a fit function, denoted FULS, the simple sum of squared differences between the corresponding elements of S and the model reproduced covariance matrix S(g). Accordingly, the estimates for the model parameters are those values for which FULS attains its smallest value. The ULS estimation approach can typically be used when the same or similar scales of measurement underlie the analyzed variables. The other three estimation methods are based on the same sum of squares as the ULS approach, but after specific weights have been used to multiply PARAMETER ESTIMATION 29 each of the squared element-wise differences between S and S(g), resulting in corresponding fit functions.

Whenever its value is 0, and only then, the two matrices involved are identical. It turns out that depending on how the matrix distance is defined, several fit functions result. These fit functions, along with their corresponding methods of parameter estimation, are discussed next. Methods of Parameter Estimation There are four main estimation methods and types of fit functions in SEM: unweighted least squares, maximum likelihood, generalized least squares, and asymptotically distribution free (often called weighted least squares).