By Rex B. Kline PhD
New to This Edition
*Extensively revised to hide very important new issues: Pearl's graphing conception and the SCM, causal inference frameworks, conditional procedure modeling, direction versions for longitudinal info, merchandise reaction conception, and more.
*Chapters on most sensible practices in all levels of SEM, size invariance in confirmatory issue research, and importance checking out concerns and bootstrapping.
*Expanded assurance of psychometrics.
*Additional laptop instruments: on-line records for all unique examples, formerly supplied in EQS, LISREL, and Mplus, at the moment are additionally given in Amos, Stata, and R (lavaan).
*Reorganized to hide the specification, id, and research of saw variable types individually from latent variable models.
*Exercises with solutions, plus end-of-chapter annotated lists of extra reading.
*Real examples of tricky info, demonstrating the best way to deal with ordinary difficulties in analyses.
*Topic bins on really good matters, akin to explanations of nonpositive certain correlations.
*Boxed ideas to remember.
*Website selling a learn-by-doing procedure, together with syntax and information records for 6 primary SEM laptop tools.
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Additional info for Principles and Practice of Structural Equation Modeling
An observed variable used as an indirect measure of a construct is an indicator, and the statistical realization of a construct based on analyzing scores from its indicators is a factor. The explicit distinction between indicators and factors in SEM allows one to test a wide variety of hypotheses about measurement. Suppose that a researcher believes that variables X1–X3 tap a common domain that is distinct from the one measured by X4 –X5. In SEM, it is relatively easy to specify a model where X1–X3 are indicators of one factor and X4 –X5 are indicators of a different factor.
2003) described the SCM as the future of SEM and also why I introduce readers to it in this book. The SCM is well known in disciplines such as epidemiology, but it is less familiar in areas such as psychology and education. This is unfortunate because the SCM has features described next that make it worthwhile to learn about it. The SCM is graphical in nature; specifically, causal hypotheses are represented in directed graphs where either observed or latent variables can be depicted. Unlike graphs (model diagrams) in SEM, which are basically static entities that require data in order to 20 CONCEPTS AND TOOLS be analyzed, there are special computer tools in the SCM for analyzing directed graphs without data.
0 because there can be no error variance with so many predictors, and this is true even for random numbers. There are many corrections that downward adjust R2 as a function of N and k. 14) where Rˆ 2 is the shrinkage-corrected estimate of r2. In small samples it can happen that Rˆ 2 < 0; if so, then Rˆ 2 is interpreted as though its value were zero. As the sample size increases for a constant number of predictors, values of Rˆ 2 and R2 are increasingly similar, and in very large samples they are essentially equal; that is, it is unnecessary to correct for positive bias in very large samples.