# Factor analysis using SPSS

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## An overview of our factor analysis workshop

Factor analysis is a statistical technique commonly used to establish evidence of survey instrument validity. Exploratory factor analysis explores the relationships among variables to discover if those variables can be grouped into a smaller set of underlying factors. Often researchers and applied practitioners are faced with the difficult task of summarizing numerous variables from a survey and want to reduce the data into a smaller set of factors. The workshop will review the basic statistical principles of factor analysis and will use a case study example from a college senior survey to analyze and interpret Exploratory Factor analysis using SPSS.

## Expected outcomes of our factor analysis workshop

By the end of the workshop, participants should understand exploratory factor analysis well enough to begin using it in their research. They will understand when to use exploratory factor analysis, and how to run and interpret output from exploratory factor analysis.

## Who should attend?

The target audience is researchers who are familiar with the basic concepts of correlation and the process of establishing the validity of psychometric instruments, but are not familiar with factor analysis, and wish to begin using it in their research (or those researchers looking for a quick refresher). This is an applied course, so no advanced math skills are required; however, you should understand how to interpret correlation coefficients and the basic principles of validity. Software demonstrations will use SPSS, but output will be described in such a manner that participants can understand and interpret factor analyses using other statistical software.

## Agenda

1. What are the basic purposes and applications for Factor Analysis and Exploratory Factor Analysis.
2. What are the basic assumptions of Exploratory Factor Analysis and how do those assumptions impact your analyses.
3. Comparison of principal components and principal axis extraction procedures for determining the initial factor structure.
4. Interpreting the initial factor matrix; what are eigenvalues, how do you determine the number of meaningful factors; problems with interpreting the initial factor matrix.
5. Description of procedures (varimax, quartimax, and oblique) for rotating the initial factor matrix and calculating the rotated factor matrix.
6. Interpreting the rotated factor matrix; guidelines for interpreting the rotated matrix, and naming the factors, differences between initial communalities, eigenvalues .
7. Exploring and understanding the analyses; what are the similarities and differences between initial factor matrices, rotated factor matrices, initial communalities, and eigenvalues .
8. Comparison of varimax and oblique solutions for rotation of the initial factor matrix; what are pattern and structure matrices; how are pattern and structure matrices interpreted and how do these matrices compare to the rotated factor matrices calculated when using varimax.