By Tenko Raykov, George A. Marcoulides
During this e-book, 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 figuring out, 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 direction in Structural Equation Modeling is a wonderful beginner’s consultant to studying easy methods to arrange enter records to slot the main regular forms of structural equation types with those courses. the elemental rules and techniques for undertaking SEM are autonomous of any specific software program. Highlights of the second one version contain: • evaluation of latent swap (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 the entire text’s LISREL, EQS, and Mplus examples. a primary direction in Structural Equation Modeling is meant as an introductory e-book for college students and researchers in psychology, schooling, company, medication, and different utilized social, behavioral, and well-being sciences with constrained or no earlier publicity to SEM. A prerequisite of easy facts via regression research is usually recommended. The ebook often attracts parallels among SEM and regression, making this previous wisdom useful.
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Extra info for A First Course in Structural Equation Modeling, 2nd edition
We note that a reason for the numerical procedure of fit function minimization not to converge could be that the proposed model may simply be misspecified. Misspecified models are inadequate for the analyzed data, that is, they contain unnecessary parameters or omit important ones (and/or such variables); in terms of path diagrams, misspecified models contain wrong paths or two-way arrows, and/or omit important one-way paths or covariances. Another reason for lack of convergence is lack of 34 1.
This fact is related to another specificity of SEM that is different from classical modeling approaches. , alternative hypotheses of difference or change), SEM is pragmatically concerned with finding a model that does not contradict the data. That is, in an empirical SEM session, one is typically 40 1. FUNDAMENTALS OF STRUCTURAL EQUATION MODELING interested in retaining a proposed model whose validity is the essence of a pertinent null hypothesis. In other words, statistically speaking, when using SEM one is usually ‘interested’ in not rejecting the null hypothesis.
LISREL and Mplus, offer the option of fixing the scales for both dependent and independent latent variable). The reason that Rule 6 is needed stems from the fact that an application of Rule 1 on independent latent variables can produce a few redundant and not uniquely estimable model parameters. For example, the pair consisting of a path emanating from a given latent independent variable and this variable’s variance, contains a redundant parameter. This means that one cannot distinguish between these two parameters given data on the observed variables; that is, based on all available observations one cannot come up with unique values for this path and latent variance, even if the entire population of interest were examined.