This tutorial provides an introduction to conducting exploratory factor analyses (EFAs) using SPSS. The SPSS data, syntax and output is available for each analysis, along with screencasts on youtube.
Here are the recommended steps when conducting an exploratory factor analysis using SPSS:
Get to know the data:
Univariate distributions: Explore the distribution and central tendency for each of the variables statistically (e.g., use descriptive statistics to examine the M, SD, skewness, and kurtosis) and graphically (e.g., histograms)).
Bivariate distributions: Examine the bivariate correlations statistically (e.g., correlation matrix) and graphically (e.g., scatterplots)
Examine EFA assumptions:
Conduct EFA (repeat until a good model is identified - may require many different analyses involving different numbers of factors, different types of rotation, and different sets of items):
Via Pull-down menus: Analyze - Data Reduction - Factor Analysis - Put target variables into the Variables box (order doesn't matter, but the output will be neater to read if the variables are sequentially ordered from 1 to X)
Descriptives - check these options:
KMO and Bartlett's
Extraction - check these options:
Analyse - Correlation matrix
Display - Screen plot
Extract - Eigenvalues over 1 - or usually better is to specify the number of factors
Rotation - Varimax (uncorrelated factors) or Direct Oblimin (correlated factors)