The instructions given here and in all the data analysis chapters (14 to 22) will work with the SAS Learning Edition as well as with the SAS Enterprise Guide. For a point-and-click approach for performing principal components analysis and factor analysis, use the Analyze task within the SAS Learning Edition. The Multivariate>Factor Analysis tasks performs…
Computer programs are available to implement both of the approaches: principal components analysis and common factor analysis. We discuss the use of SPSS and SAS in detail in the subsequent sections. Here, we briefly describe the use of MINITAB. In MINITAB, factor analysis can be assessed using Multivariate-Factor analysis. Principal components or maximum likelihood can…
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The final step in factor analysis involves the determination of model fit. A basic assumption underlying factor analysis is that the observed correlation between variables can be attributed to common factors. Hence, the correlations between the variables can be deduced or reproduced from the estimated correlations between the variables and the factors, The differences between…
Interpretation is facilitated by identifying the variables that have large loadings on the same factor. That factor can then be interpreted in terms of the variables that load high on it. Another useful aid in interpretation is to plot the variables using the factor loadings as coordinates. Variables at the end of an axis are…
An important output from factor analysis is the factor matrix, also called thefactor pattern matrix. The factor matrix contains the coefficients used to express the standardized variables in terms of the factors. These coefficients, the factor loadings, represent the correlations between the factors and the variables. A coefficient with a large absolute value indicates that…
Once it has been determined that factor analysis is suitable for analyzing the data, an appropriate method must be selected. The approach used to derive the weights or factor score coefficients differentiates the various methods of factor analysis. The two basic approaches are principal components analysis and common factor analysis. In principal components analysis, the…
The analytical process is based on a matrix of correlations between the variables. Valuable insights can be gained from an examination of this matrix. For the factor analysis to be appropriate, the variables must be correlated. In practice, this is usually the case. If the correlations between all the variables are small, factor analysis may…
The steps involved in conducting factor analysis are illustrated in Figure 19.3. The first step is 10 define the factor analysis problem and identify the variables 10 be factor analyzed. Then a correlation matrix of these variables is constructed and a method of factor analysis selected. The researcher decides on the number of factors to…
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