By Rex B. Kline PhD
New to This Edition
*Extensively revised to hide vital new issues: Pearl's graphing conception and the SCM, causal inference frameworks, conditional technique modeling, direction types for longitudinal facts, merchandise reaction idea, and more.
*Chapters on top practices in all phases of SEM, size invariance in confirmatory issue research, and value checking out concerns and bootstrapping.
*Expanded assurance of psychometrics.
*Additional laptop instruments: on-line records for all distinct examples, formerly supplied in EQS, LISREL, and Mplus, are actually additionally given in Amos, Stata, and R (lavaan).
*Reorganized to hide the specification, identity, and research of saw variable types individually from latent variable models.
Pedagogical Features
*Exercises with solutions, plus end-of-chapter annotated lists of extra reading.
*Real examples of problematical info, demonstrating find out how to deal with normal difficulties in analyses.
*Topic containers on really good matters, corresponding to motives of nonpositive sure correlations.
*Boxed ideas to remember.
*Website selling a learn-by-doing strategy, together with syntax and information documents for 6 universal SEM computing device tools.
Read Online or Download Principles and Practice of Structural Equation Modeling (Methodology in the Social Sciences) PDF
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Extra resources for Principles and Practice of Structural Equation Modeling (Methodology in the Social Sciences)
Sample text
The type of null hypothesis tested most often in the behavioral sciences is a nil hypothesis, which says that the value of a population parameter or the difference between two parameters is zero. , H0: µ1 = µ2), which predicts that two population means are exactly equal. However, it is unlikely that the value of any population parameter (or difference between two parameters) is exactly zero, especially if zero implies the complete absence of an effect or association. 00 but this is rarely done in practice.
40 vs. 06). Omitting a predictor correlated with others in the equation does not always result in overestimation of the predictive power of an included predictor. 2. Examples of the Omitted Variable Problem Predictor(s) Both X1 and X2 Case 1. 2. 3. 60 Note. Numerical values for X1 and X2 are standardized regression coefficients. 60. 6The same principles hold if X1 is the omitted predictor and X2 is the included predictor. 26 CONCEPTS AND TOOLS or even for rY1 and b1 to have different signs. Both cases indicate suppression.
The power of a study should be estimated when the study is planned but before the data are collected (Wilkinson & the Task Force on Statistical Inference, 1999). 85. 50, then the odds of rejecting a false null hypothesis are no greater than guessing the outcome of a coin toss. In fact, tossing a coin instead of conducting the study would be just as likely to give the correct decision and would save time and money, too (Schmidt & Hunter, 1997). How to estimate power in SEM is described in a later chapter, but the typical power of certain kinds of statistical tests in SEM are often relatively low even in large samples.
