
Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger. The bi-factor model has a general factor and a number of group factors. The purpose of this article is to introduce an exploratory form of bi-factor analysis. An advantage of using exploratory bi-factor analysis is that one need not provide a specific bi-factor model a priori. The result of an exploratory bi-factor analysis, however, can be used as an aid in defining a specific bi-factor model. Our exploratory bi-factor analysis is simply exploratory factor analysis using a bi-factor rotation criterion. This is a criterion designed to approximate perfect cluster structure in all but the first column of a rotated loading matrix. Examples are given to show how exploratory bi-factor analysis can be used with ideal and real data. The relation of exploratory bi-factor analysis to the Schmid–Leiman method is discussed.
Schmid-Leiman method, group factor, bi-factor rotation, Holzinger's bi-factor method, general factor, gradient projection algorithms, Applications of statistics to psychology
Schmid-Leiman method, group factor, bi-factor rotation, Holzinger's bi-factor method, general factor, gradient projection algorithms, Applications of statistics to psychology
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