Publishing nutrition research: a review of multivariate techniques--part 3: data reduction methods

Abstract

This is the ninth in a series of monographs on research design and analysis, and the third in a set of these monographs devoted to multivariate methods. The purpose of this article is to provide an overview of data reduction methods, including principal components analysis, factor analysis, reduced rank regression, and cluster analysis. In the field of nutrition, data reduction methods can be used for three general purposes: for descriptive analysis in which large sets of variables are efficiently summarized, to create variables to be used in subsequent analysis and hypothesis testing, and in questionnaire development. The article describes the situations in which these data reduction methods can be most useful, briefly describes how the underlying statistical analyses are performed, and summarizes how the results of these data reduction methods should be interpreted.

Keywords: Cluster analysis; Eigenvalue; Factor analysis; Principal component analysis; Reduced rank regression.