By Rex B. Kline PhD
New to This Edition
*Extensively revised to hide very important new subject matters: Pearl's graphing concept and the SCM, causal inference frameworks, conditional procedure modeling, course types for longitudinal info, merchandise reaction idea, and more.
*Chapters on top practices in all levels of SEM, size invariance in confirmatory issue research, and value checking out matters and bootstrapping.
*Expanded assurance of psychometrics.
*Additional desktop instruments: on-line records for all unique examples, formerly supplied in EQS, LISREL, and Mplus, are actually additionally given in Amos, Stata, and R (lavaan).
*Reorganized to hide the specification, id, and research of saw variable versions individually from latent variable models.
*Exercises with solutions, plus end-of-chapter annotated lists of additional reading.
*Real examples of not easy info, demonstrating how one can deal with common difficulties in analyses.
*Topic bins on really good matters, similar to explanations of nonpositive sure correlations.
*Boxed ideas to remember.
*Website selling a learn-by-doing process, together with syntax and information records for 6 everyday SEM computing device tools.
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Additional info for Principles and Practice of Structural Equation Modeling
Bollen (1989) put it like this (emphasis in original): If a model is consistent with reality, then the data should be consistent with the model. But if the data are consistent with the model, this does not imply that the model corresponds to reality. 68) Retaining a model in SEM makes causal assumptions more tentatively plausible, but any such conclusions must withstand replication and the criticism of other researchers who suggest alternative models for the same data (Bollen & Pearl, 2013). A related myth is that SEM and regression techniques are equivalent.
2), the value of the standardized regression coefficient (rXY) is unaffected by the scale on either X or Y. 686 is also the standardized coefficient when regressing zX on zY, and (2) the standardized prediction equation in this case is zˆ X = rXY zY. There is a special relation between rXY and the unstandardized predicted scores. If Y is regressed on X, for example, then 1. rXY = rYYˆ ; that is, the bivariate correlation between X and Y equals the bivariate correlation between Y and Yˆ ; 2. the observed variance in Y can be represented as the exact sum of the variances of the predicted scores and the residuals, or sY2 = sY2ˆ + sY2 −Yˆ ; and 2 3.
Another latent variable category in SEM corresponds to residual or error terms, which can be associated with either observed variables or factors specified as outcomes (dependent variables). In the case of indicators, a residual term represents variance not explained by the factor that the corresponding indicator is supposed to measure. Part of this unexplained variance is due to random measurement error or score unreliability. The explicit representation of measurement error is a special characteristic of SEM.
Principles and Practice of Structural Equation Modeling by Rex B. Kline PhD