Factor Analysis

Factor analysis is a statistical method used in social science research to understand the underlying structure of a relatively large set of variables. It is often used when the researcher believes that multiple observed variables have a common underlying basis.

Factor analysis groups these observed variables into a smaller set of factors (also known as latent variables or constructs) based on the correlations among the variables. Each factor captures a certain amount of the overall variance observed in the variables, and the factors are often interpreted as underlying concepts or themes that explain the connections among the variables.

For example, in a psychological study, a researcher may use factor analysis to identify underlying mental constructs such as intelligence, personality traits, or attitudes from a large number of test or survey items.

The key goal of factor analysis is to reduce the dimensionality of the data and to detect structure in the relationships between variables, that is to classify variables.

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因子分析是社会科学研究中用于理解相对大量变量的底层结构的统计方法。当研究者认为多个观察变量具有共同的底层基础时，常常会使用因子分析。

因子分析基于变量之间的相关性，将这些观察变量分组为较小的一组因子（也被称为潜在变量或结构）。每个因子捕获了在变量中观察到的总体方差的一部分，因子通常被解释为解释变量之间连接的底层概念或主题。

例如，在心理学研究中，研究者可能使用因子分析从大量的测试或调查项目中识别底层的心理结构，如智力、性格特质或态度。

因子分析的主要目标是减少数据的维度，并检测变量之间关系的结构，也就是对变量进行分类。