J Acad Nutr Diet. 2015 Jul;115(7):1072-82. doi: 10.1016/j.jand.2015.03.011. Epub 2015 Apr 30.
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.
这是一系列关于研究设计和分析的专著中的第九本，也是这一系列专门研究多元方法的专著中的第三本。本文的目的是提供数据降维方法的概述，包括主成分分析、因子分析、降秩回归和聚类分析。在营养领域，数据降维方法可以用于三个一般目的：用于高效总结大量变量的描述性分析，创建用于后续分析和假设检验的变量，以及在问卷开发中。本文描述了这些数据降维方法最有用的情况，简要描述了基础统计分析是如何进行的，并总结了如何解释这些数据降维方法的结果。
