By Larry Hatcher, Norm O'Rourke
This easy-to-understand advisor makes SEM available to all clients. This moment version comprises new fabric on sample-size estimation for course research and structural equation modeling. in one straight forward quantity, scholars and researchers will locate the entire details they wish which will grasp SAS fundamentals earlier than relocating directly to issue research, course research, and different complicated statistical methods.
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Extra resources for A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling (2nd Edition)
The correlations between FACTOR1 and FACTOR2 and the original observed variables appear at the bottom half of page 5. ” This makes sense, as the elements of a factor pattern (in an orthogonal solution) are simply correlations between the observed variables and the components themselves. 1. Of particular interest is the correlation between FACTOR1 and FACTOR2, as computed by PROC CORR. 2, where the row for FACTOR2 intersects with the column for FACTOR1. Notice that the observed correlation between these two components is zero.
Gave money to a panhandler. None of these items are reversed. With each item, a response of “7” indicates a high level of financial giving. In the following, however, item 4 is a reversed item; a response of “7” indicating a low level of giving: 1234567 1234567 1234567 4. Refused to give money to a religious charity. 5. Gave money to a charity not affiliated with a religion. 6. Gave money to a panhandler. , if items 5 and 6 had positive loadings, then item 4 would have a negative loading). This would complicate the creation of a component-based scale: with items 5 and 6, higher scores indicate greater giving whereas with item 4, lower scores indicate greater giving.
The OUT option may be used to create component scores only if the analysis has been performed on a raw data as opposed to a correlation or covariance matrix. The use of the NFACT statement is also required. Having created the new variables named FACTOR1 and FACTOR2, you may be interested to see how they relate to the study’s original observed variables. This can be done by appending PROC CORR statements to the SAS program, following the last of the PROC FACTOR statements. 40 ❶ out=D2 ; var V1 V2 V3 V4 V5 V6; run; ❷ proc corr data=D2; var FACTOR1 FACTOR2; with V1 V2 V3 V4 V5 V6 FACTOR1 FACTOR2; run; Notice that the PROC CORR statement on line ❷ specifies DATA=D2.
A Step-by-Step Approach to Using SAS for Factor Analysis and Structural Equation Modeling (2nd Edition) by Larry Hatcher, Norm O'Rourke