Basic Principles of Structural Equation Modeling: An by Ralph O. Mueller

By Ralph O. Mueller

During the final twenty years, structural equation modeling (SEM) has emerged as a strong multivariate facts research device in social technology study settings, specifically within the fields of sociology, psychology, and schooling. even though its roots might be traced again to the 1st 1/2 this century, while Spearman (1904) constructed issue research and Wright (1934) brought direction research, it used to be now not till the Seventies that the works via Karl Joreskog and his affiliates (e. g. , Joreskog, 1977; Joreskog and Van Thillo, 1973) started to make basic SEM concepts obtainable to the social and behavioral technological know-how study groups. this day, with the advance and extending avail­ skill of SEM desktop courses, SEM has develop into a well-established and revered information research strategy, incorporating a number of the conventional research innovations as specific circumstances. state of the art SEM software program applications reminiscent of LISREL (Joreskog and Sorbom, 1993a,b) and EQS (Bentler, 1993; Bentler and Wu, 1993) deal with quite a few usual least squares regression designs in addition to complicated structural equation types regarding variables with arbitrary distributions. regrettably, many scholars and researchers hesitate to take advantage of SEM equipment, possibly as a result of a little bit complicated underlying statistical repre­ sentation and thought. for my part, social technological know-how scholars and researchers can profit enormously from buying wisdom and abilities in SEM because the methods-applied appropriately-can supply a bridge among the theo­ retical and empirical facets of behavioral research.

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Extra resources for Basic Principles of Structural Equation Modeling: An Introduction to LISREL and EQS

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As in previous EQS examples, the coefficient of determination, R2, for each of the three structural equations can be computed from the standardized coefficients associated with the error terms that are given at the end of the EQS output. 107 for the V2 (DegreAsp), V3 (Selctvty), and V4 (Degree) equations, respectively. 12. 945 E4 sions, but structural relationships were specified a priori. One major advantage of path analysis is that, in addition to direct structural effects (DE), indirect effects (J E) through intervening variables can be estimated.

Linear Regression and Classical Path Analysis 34 The computational definitions of a direct and indirect effect of an independent on a dependent variable in a general structural equation model are based on the fact that covariances between two variables can be completely decomposed and written as functions of the model-implied parameters. 27) where q is a subscript denoting all variables (including error terms) with direct paths to variable Y and Prq denotes the path coefficient from variable q to variable Y The law states that the covariance between two variables, X and Y, can be decomposed into the sum of products of structural coefficients of variables, q, with direct paths to Y and the co variances of these variables with X.

3. Multiply each structural coefficient identified in Step 2 by the covariance of Xl and the associated variable identified in Step 1; now sum the results. 2 that (3 does not covary with Xl ((J~3X, = 0). 28) does not involve the term (J~3X I. 4. 28) contains two covariances, (Jy,X, and (Jy,X" which in turn can be decomposed by repeating Steps 1 and 3 above. 30) 5. 4. 283).

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