Factor analysis refers to the technique of taking measured items, usually responses to a variety of material, and then examining whether all the items can be broken down into clusters or groups based on content and similar response patterns. There are a variety of techniques designed to do this. Two general techniques involve either exploratory factor analysis (EFA) or confirmatory factor analysis (CFA). The principal difference between EFA and CFA is whether or not items are placed on factors (or put into groups) prior to statistical analysis. This entry examines the difference between EFA and CFA, discusses orthogonal versus oblique analysis, and further discusses how oblique analysis permits relationships among factors and why this is important.
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