Instructor: Quinnehtukqut McLamore
Exploratory factor analysis (EFA) groups a set of variables into clusters that share variances. It is useful in identifying dimensions of an index/scale, understanding the structure of a large set of variables, and reducing the variables to a more manageable size while retaining as much of the original information as possible. However, there are different situations that call for different types of procedures to be used in different EFAs. What happens if the different indices or scales are correlated? What if they *aren’t* correlated? What do you do if a variable loads onto more than one factor? And what’s the difference between an EFA and other kinds of analyses? Find out in this two-hour workshop using SPSS. Prior experience with SPSS is recommended, but not necessary.
By the end of this workshop, participants will know:
- What EFA is and is Not (the difference between EFA and CFA or PCA)
- How to run factor analysis in SPSS using both GUI and Syntax
- How to use different orthogonal and non-orthogonal factor rotations
- How to interpret SPSS output for Exploratory Factor Analyses