Two Kinds of Factor Analysis For Ordered Categorical Variables.
Two Kinds of Factor Analysis For Ordered Categorical Variables.
复制标题
有序分类变量的两种因子分析。
DOI：
10.1207/s15327906mbr1804_5
复制
发表时间：
1983
影响因子：
3.8
通讯作者：
A. Mooijaart
中科院分区：
文献类型：
--
作者：
A. Mooijaart
在有序分类变量的因子分析中，假设变量为潜在反应变量。这些潜在反应变量可以是离散的，也可以是连续的。关于潜在反应变量的不同假设导致了不同类型的因素模型。一个方便的假设是假设潜在响应变量是连续的和正态分布的。本文讨论了两种因子分析之间的关系。从数学上看，仅含整数值的数据(类别号)的因子分析对显性变量的偏度和因子负载量的大小非常敏感。Olsson(1979b)用“完美”数据进行的模拟研究也证明了这一点。此外，在假设潜在响应变量服从正态分布的前提下，本文提出了一种简单的分类变量因子模型的估计方法。
In factor analysis of variables with ordered categories, latent response variables are assumed. These latent response variables may be either discrete or continuous. Different assumptions regarding the latent response variables lead to different kinds of factor models. A convenient assumption is to postulate that the latent response variables are continuous and normally distributed. In this paper the relationship between two kinds of factor analysis is discussed. It is shown, mathematically, that factor analysis of data with integer values only (the category numbers) is very sensitive to the skewness of the manifest variables and the size of the factor loadings. This was also shown by Olsson (1979b) by a simulation study with "perfect" data. Further, in the paper we propose a simple estimation procedure for the factor model of categorical variables, in which it is assumed that the latent response variables are normally distributed.