USING PRINCIPAL COMPONENT REGRESSION ANALYSIS TO EXPLORE PROFESSIONAL COMPETENCE OF MATHEMATICS TEACHERS
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Abstract
Professional competence of secondary mathematics teachers is measured in terms of cognitive abilities and affective-motivational characteristics. There are many items which attribute to cognitive abilities and affective-motivational characteristics of teachers. Thus, this study wanted to reduce these items into components using principal component analysis. From the extracted components, develop a model using principal component regression. This study employed multi-stage cluster sampling where secondary mathematics teachers are clustered according to the municipal level, division, and school. Simple random sampling was used to determine clusters. Results showed that the kaiser-meyer-olkin yield good result and bartlett’s test for sphericity are significant, which implied that it already suits to test for principal component analysis. Findings showed that there were 27 extracted components and from these components have component loadings less than 0.300; thus, they need to be eliminated. The components with component loadings greater than 0.300 are used in principal component regression. The model generated yielded two insignificant regressors.
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