The Use of Canonical Commonality Analysis for Quantitative Theory Building
Kim Nimon, Thomas G. Reio
Abstract
Kim Nimon, Thomas G. Reio
Abstract
When conducting canonical correlation analysis, researchers generally rely on function and structure coefficients when interpreting noteworthy canonical functions. This article describes how human resource development (HRD) researchers can use canonical commonality analysis to interpret their canonical functions more completely and thereby inform theory. Using the correlation matrix, we conducted a secondary analysis of data from the Dimensions of Learning Organizational Questionnaire (DLOQ) to illustrate the utility of canonical commonality analysis. Researchers will see how canonical commonality analysis can bring to light theoretically relevant relationships in canonical functions that might be left undetected by only examining function and structure coefficients.
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When conducting canonical correlation analysis, researchers generally rely on function and structure coefficients when interpreting noteworthy canonical functions. This article describes how human resource development (HRD) researchers can use canonical commonality analysis to interpret their canonical functions more completely and thereby inform theory. Using the correlation matrix, we conducted a secondary analysis of data from the Dimensions of Learning Organizational Questionnaire (DLOQ) to illustrate the utility of canonical commonality analysis. Researchers will see how canonical commonality analysis can bring to light theoretically relevant relationships in canonical functions that might be left undetected by only examining function and structure coefficients.
Key concepts: Canonical correlation, Canonical analysis, Canonical correspondence analysis, Non canonical, Function (biology), Canonical form, Computer science, Mathematics