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 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 advisor to studying how one can organize enter records to slot the main familiar forms of structural equation types with those courses. the fundamental principles and techniques for accomplishing SEM are autonomous of any specific software program. Highlights of the second one variation comprise: • overview of latent switch (growth) research types at an introductory point • insurance of the preferred Mplus software • up-to-date examples of LISREL and EQS • A CD that includes the entire text’s LISREL, EQS, and Mplus examples. a primary direction 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 future health sciences with constrained or no earlier publicity to SEM. A prerequisite of simple statistics via regression research is suggested. The e-book usually attracts parallels among SEM and regression, making this past wisdom beneficial.
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Additional info for A First Course in Structural Equation Modeling, 2nd edition
Note that the right-hand side of the equation of this law simplifies markedly if some of the variables are uncorrelated, that is, one or more of the involved covariances is equal to 0. 3 Using Laws 1 and 2, and the fact that Cov(X,Y) = Cov(Y,X) (since the covariance does not depend on variable order), one obtains the next equation, which, due to its importance for the remainder of the book, is formulated as a separate law: 3 Law 2 reveals the rationale behind the rules for determining the parameters for any model once the definition equations are written down (see section “Rules for Determining Model Parameters” and Appendix to this chapter).
Substantive theories are often representable as models that describe and explain phenomena under investigation. As discussed previously, an essential requirement for all such models is that they be identified. Another requirement, one of no lesser importance, is that researchers consider for further study only those models that are meaningful from a substantive viewpoint and present plausible means of data description and explanation. SEM provides a number of inferential and descriptive indices that reflect the extent to which a model can be considered an acceptable means of data representation.
This coefficient measures the extent to which the multivariate distribution of all observed variables has tails that differ from the ones characteristic of the normal distribution, with the same component means, variances and covariances. If the distribution deviates only slightly from the normal, Mardia’s coefficient will be close to 0; then its normalized estimate, which can be considered a standard normal variable under normality, will probably be nonsignificant. 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.
A First Course in Structural Equation Modeling, 2nd edition by Tenko Raykov, George A. Marcoulides