Exploratory factor analysis: Difference between revisions

Revision as of 02:42, 26 April 2012

Revision as of 02:46, 26 April 2012

Line 23:

''Underfactoring'' occurs when too few factors are included in a model. This is considered to be a greater error than overfactoring. If not enough factors are included in a model, there is likely to be substantial error. Measured variables that load onto a factor not included in the model can falsely loaded on factors that are included, altering true factor loadings . This can result in rotated solutions in which two factors are combined into a single factor, obscuring the true factor structure.

There are a number of procedures in order to determine the best number of factors, including scree plot, parallel analysis, kaiser criterion, and model comparison:. The first three measures rely on eigenvalues. The ''eigenvalue'' of a factor represents the amount of variance of

the variables accounted for by that factor. The lower the eigenvalue, the less that factor contributes to the explanation of variances in the variables.<ref name =Norris/>)

===Scree plot===