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Factor analysis is a complex technique used to explore large data sets. It helps us to understand large data sets and to work out whether a number of variables have something in common underlying them. Factor Analysis will point out which variables seem to be strongly linked together, and produces an associated set of variables which are known as a factor. A typical analysis will reveal several such factors which can be interpreted by looking at the variables which make them up.
Scenario: Using last weeks data set regarding exam scores and measured IQ, we want to investigate whether there is some fundamental property of individuals which affects their performance on a number of different exams.
Click on continue then OK.
One of the first tables of interest is the Total Variance Explained, as seen below:-
When we conducted the analysis we told SPSS to only extract eigenvalues over 1 (in the extraction command), as only eigenvalues over 1 are considered to be stable.
Our Factor Analysis has extracted two components, which together account for 90.001% of the variance.
Note: successive factors explain progressively less of the variance of the original variables, but because each factor is orthogonal (i.e. unrelated), each explains a unique portion of the total variance. We are usually happy with a factor solution that explains 50-75% of the variance.
Scroll down to the Rotated Component Matrix.
We can see from this table that the analysis has produced two components or factors with factor loadings above 0.7 as we asked for.
We now have to decide what these factors are.
Factor 1 (component 1) seems to be some kind of language ability factor or literacy factor because scores on both the English and French exams load onto this factor.
Factor 2 (component 2) seems to be a mathematical ability factor because both maths and statistics load onto this.
Apply» Faculties & Departments» Department of Psychology» Labs» SPSS statistical procedures» Factor Analysis
