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(New page: {{StatsPsy}} {{expert}} '''Statistical rotation''' may be viewed as a form of '''Higher-order factor analysis''' is a statistical method consisting of repeating steps factor analysis ...)
 
 
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'''Statistical rotation''' may be viewed as a form of '''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 hierachical structure of studied phenomena.
 
'''Statistical rotation''' may be viewed as a form of '''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 hierachical structure of studied phenomena.
   
To interpret the results, one proceeds either by [[matrix multiplication|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.
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To interpret the results, one proceeds either by [[matrix multiplication|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.
   
 
==See also==
 
==See also==

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Statistical rotation may be viewed as a form of Higher-order factor analysis is a statistical method consisting of repeating steps factor analysisoblique rotation – factor analysis of rotated factors... Its merit is to enable the researcher to see the hierachical 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.

See alsoEdit

ReferencesEdit

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