Structural Equation Modeling: A Second Course (2nd ed.) by Gregory R. Hancock, Ralph O. Mueller

By Gregory R. Hancock, Ralph O. Mueller

Backed by way of the yank academic learn Association's exact curiosity workforce for academic Statisticians This quantity is the second one version of Hancock and Mueller's highly-successful 2006 quantity, with the entire unique chapters up to date in addition to 4 new chapters. the second one version, just like the first, is meant to function a didactically-oriented source for graduate scholars and study pros, protecting a extensive variety of complex subject matters usually no longer mentioned in introductory classes on structural equation modeling (Sem). Such subject matters are very important in furthering the knowledge of foundations and assumptions underlying Sem in addition to in exploring Sem, as a possible device to handle new sorts of study questions that will no longer have arisen in the course of a primary direction. Chapters specialise in the transparent rationalization and alertness of subject matters, instead of on analytical derivations, and include fabrics from renowned Sem software program.

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1987). , 1998) to examine the parameter space. Heuristic search algorithms are specifically designed to determine the best possible solution but do not guarantee that the optimal solution is found—though their performance using empirical testing or worst cases analysis indicates that in many situations they seem to be the only way forward to produce concrete results (Salhi, 1998). To date, heuristic procedures have been successfully applied to tackle difficult problems in business, economics, and industrial engineering.

6. When tested against a set of data, identical fit statistics would be obtained for each presented model. Whenever a model is modified to accommodate a sizable but unmodeled correlation between two variables, the potential for model equivalence increases due to the many ways in which the source of the correlation may be represented. A number of possibilities might occur. For example, another factor may be specified (Model 1, Model 2, and Model 4), or the correlation between the two variables may be left as a residual covariance (Model 3).

If theory testing is one’s objective, the most desirable identification status of a model is over-identification, where the number of available data elements 28  S. L. HERSHBERGER and G. A. MARCOULIDES is more than what we need to obtain a unique solution. In an over-identified model, the degrees of freedom are positive so that we can explicitly test model fit. An over-identified model implies that, for at least one parameter, there is more than one equation the estimate of a parameter must satisfy; only under these circumstances—the presence of multiple solutions—are models provided with the opportunity to be rejected by the data.

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