Higher-order factor analysis is a statistical method consisting of repeating steps factor analysis – oblique rotation – factor analysis of rotated factors. Its merit is to enable the researcher to see the hierarchical structure of studied phenomena. To interpret the results, one proceeds either by post-multiplying the primary factor pattern matrix by the higher-order factor pattern matrices (Gorsuch, 1983) and perhaps applying a Varimax rotation to the result (Thompson, 1990) or by using a Schmid-Leiman solution (SLS, Schmid & Leiman, 1957, also known as Schmid-Leiman transformation) which attributes the variation from the primary factors to the second-order factors.
Famous quotes containing the words factor and/or analysis:
“Children of the middle years do not do their learning unaffected by attendant feelings of interest, boredom, success, failure, chagrin, joy, humiliation, pleasure, distress and delight. They are whole children responding in a total way, and what they feel is a constant factor that can be constructive or destructive in any learning situation.”
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