Download A Step-by-Step Approach to Using SAS for Factor Analysis and by Larry Hatcher, Norm O'Rourke PDF

By Larry Hatcher, Norm O'Rourke

This easy-to-understand advisor makes SEM available to all clients. This moment variation comprises new fabric on sample-size estimation for direction research and structural equation modeling. in one common quantity, scholars and researchers will locate the entire info they want as a way to grasp SAS fundamentals sooner than relocating directly to issue research, course research, and different complicated statistical strategies.

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

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Chapter 1: Principal Component Analysis 29 The reason for this apparent contradiction is simple: FACTOR1 and FACTOR2 are true principal components, and true principal components (created in an orthogonal solution) are always created with optimally weighted equations so that they will be mutually uncorrelated. In contrast, GIVING and HELPING are not true principal components that consist of true factor scores; they are merely variables based on the results of a principal component analysis. Optimal weights (that would ensure orthogonality) were not used in the creation of GIVING and HELPING.

Cary, North Carolina, USA. ALL RIGHTS RESERVED. com/bookstore. 22 A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling, Second Edition 5. Read across the row for the first variable. 40) have been flagged with an asterisk (“*”). 40 option in the preceding program. If a given variable has a meaningful loading on more than one component, cross out that variable and ignore it in your interpretation. In many situations, researchers drop variables that load on more than one component because the variables are not pure measures of any one construct.

ALL RIGHTS RESERVED. com/bookstore. 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.

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