職業適性検査の因子分析 : 分散分析的手法による因子分析の適用
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概要
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Recently, we have been able to do the factor analysis of the correlation matrix with great many variables quite easily with the aid of the electronic computer. However, the uniqueness of the meanings of factors extracted by the different researchers using the same variables, which has been for long argued, still remains unsolved. This problem is attributable to the fact that the informations of the subjects such as sex or developmental stage are neglected. In view of this point, this study aims to apply the L. J. Cronbach's generalizability theory to the multivariate variables, so that the correlation matrix can be divided into the sum of several component matrices, which is considered to depend largely on the variance analysis technique. Through this technique, the correlation matrix R is diveded as the fullowing formula shows. (1) R=R_A+R_<Ae> (in case of one classification category) (2) R=R_A+R_B+R_<AB>+R_<ABe> (in case of two classification categories) (3)R=R_A+R_B+R_C+R_<BC>+R_<CA>+R_<ABC>+R_<ABCe> (in case of three categories) (A, B, C denotes the classification categories such as sex. AB, BC, CA denotes the interaction effect. R_<Ae>, R_<ABe>, R_<ABCe> denotes the residual matrix closely related to the indivisula differences.) In ordinary analysis, the correlation matrix R is derectly analyzed, but in this analysis R_A, R_B, ……R_<ABCe> are separately analyzed by the principal axis method. Factors extracted from R_A, R_B, ……, R_<ABCe> are closely related to the meaning of the classification category and the factors extracted from the residual matrix R_<ABCe> are free of the meaning of the classification category, This technique was applied to the data of the General Vocational Aptitude Test (GATB) consisting of II scales, subjects of which are 950 Japanese students who are classified with respect to sex, developmental (high school and middle school) and regional (rural and urban) differences. Comparing the factors extracted from nine component matrices shown in formula (3) with the factors extracted from the correlation matrix R, the principal factor loadings of the residual matrix R_<ABCe> decreased and no small factor loadings of the developmental difference matrix were found. This fact means that the factors related to the developmental difference are excluded from the principal factor loadings of the correlation matrix R. Applying this techniqu widely, it si expected that the unique factor solutions independent of the difference of the subjects and the experimental conditions will be acquired in the near future.
- 1969-03-30
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