Higher-order factor analysis
From HandWiki - Reading time: 1 min
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.
References
- B.T. Gray (1997) Higher-Order Factor Analysis (Conference paper)
- Hans-Georg Wolff, Katja Preising (2005)Exploring item and higher order factor structure with the schmid-leiman solution : Syntax codes for SPSS and SAS Behavior research methods, instruments & computers, 37 (1), 48-58
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