By Larry Hatcher, Norm O'Rourke

ISBN-10: 1612903878

ISBN-13: 9781612903873

This easy-to-understand consultant makes SEM obtainable to all clients. This moment version includes new fabric on sample-size estimation for course research and structural equation modeling. in one simple quantity, scholars and researchers will locate all of the details they want on the way to grasp SAS fundamentals earlier than relocating directly to issue research, direction research, and different complicated statistical methods.

**Read Online or Download A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling (2nd Edition) PDF**

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**Extra resources for A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling (2nd Edition)**

**Sample text**

When many variables are analyzed, when communalities are small). Also, the reflexive application of this criterion can lead to retaining a certain number of components when the actual difference in the eigenvalues of successive components is trivial. 99, then component 2 will be retained but component 3 will not. This may mistakenly lead you to believe that the third component was meaningless when, in fact, it accounted for almost the same amount of variance as the second component. In short, the eigenvalue-one criterion can be helpful when used judiciously, yet the reflexive application of this approach can lead to serious errors of interpretation.

Chapter 1: Principal Component Analysis 17 The Eigenvalue-One Criterion In principal component analysis, one of the most commonly used criterion for solving the number-ofcomponents problem is the eigenvalue-one criterion, also known as the Kaiser-Guttman criterion (Kaiser 1960). 00. The rationale for this criterion is straightforward: each observed variable contributes one unit of variance to the total variance in the dataset. 00 accounts for a greater amount of variance than had been contributed by one variable.

Computing Factor Scores Factor scores are requested by including the NFACT and OUT options in the PROC FACTOR statement. Here is the general form for a SAS program that uses the NFACT and OUT option to compute factor scores: proc factor var run; data=dataset-name simple method=prin priors=one nfact=number-of-components-to-retain rotate=varimax round flag=desired-size-of-"significant"-factor-loadings out=name-of-new-SAS-dataset ; variables-to-be-analyzed ; O’Rourke, Norm, and Larry Hatcher. A Step-by-Step Approach to Using SAS® for Factor Analysis and Structural Equation Modeling, Second Edition.

### A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling (2nd Edition) by Larry Hatcher, Norm O'Rourke

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Categories: Statistics