By Larry Hatcher, Norm O'Rourke
This easy-to-understand advisor makes SEM available to all clients. This moment version includes new fabric on sample-size estimation for course research and structural equation modeling. in one hassle-free quantity, scholars and researchers will locate the entire details they want to be able to grasp SAS fundamentals ahead of relocating directly to issue research, course research, and different complex statistical strategies.
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Extra resources for A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling (2nd Edition)
Once these variables are created, they can be used as criterion or predictor variables in subsequent analyses. To keep things simple, assume that you are simply interested in determining whether there is a significant correlation between GIVING and HELPING. 3, “Working with Variables and Observations in SAS Datasets,” particularly the section on creating new variables from existing variables. This review should make it easier to understand the data manipulation statements used here. Assume that earlier statements in the SAS program have already entered responses to the six questionnaire items.
In an analysis that results in oblique (correlated) components, the definition of a factor loading is different depending on whether it is in a factor pattern matrix or in a factor structure matrix. The situation is simpler, however, in an analysis that results in orthogonal components (as in the present chapter). In an orthogonal analysis, factor loadings are equivalent to bivariate correlations between the observed variables and the components. 1. 48, and so forth. Rotations Ideally, you would like to review the correlations between the variables and the components, and use this information to interpret the components.
The OUT=D2 option has also been added. Line ❶ of the preceding program asks that an output dataset be created and given the name D2. This name is arbitrary; any name consistent with SAS requirements would be acceptable. The new dataset named D2 will contain all variables contained in the previous dataset (D1), as well as new variables named FACTOR1 and FACTOR2. FACTOR1 will contain factor scores for the first retained component, and FACTOR2 will contain scores for the second. The number of new “FACTOR” variables created will be equal to the number of components retained by the NFACT statement.
A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling (2nd Edition) by Larry Hatcher, Norm O'Rourke