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研究方法。第四部分:理解典型相关分析。

Research methodology. Part IV: Understanding canonical correlation analysis.

作者信息

Beard M T, Edwards K A, Curry E L, Marshall D D, Johnson M N

出版信息

ABNF J. 1996 Jan-Feb;7(1):11-8.

PMID:8715316
Abstract

Canonical correlation is presented as a technique to determine how sets of dependent variables are related with sets of independent variables. Canonical correlation reveals the strength of the relationship between the clusters using case data as illustration, three pairs of clusters (factors or profiles) emerged. Interpretation of the clusters are presented. As indicated in the case presentation, Canonical Correlation (CA) is the fourth in a series of methodologies selected for illustration as precursors to advanced statistics and modeling. In this paper, background will be given, a schematic example presented, sample size and CA, SPSS procedure to perform CA, and interpretation of CA and possible uses of CA in nursing research.

摘要

典型相关分析是一种用于确定因变量集与自变量集之间关系的技术。典型相关分析通过案例数据揭示了聚类之间关系的强度,以此为例,出现了三对聚类(因素或概况)。文中给出了对这些聚类的解释。如案例展示所示,典型相关分析(CA)是作为高级统计和建模的先导而被选作示例的一系列方法中的第四个。本文将介绍其背景,给出一个示意性示例,说明样本量与典型相关分析,介绍在SPSS中执行典型相关分析的步骤,以及对典型相关分析的解释和其在护理研究中的可能用途。

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