SIMULTANEOUS FACTOR ANALYSIS IN SEVERAL POPULATIONS
SIMULTANEOUS FACTOR ANALYSIS IN SEVERAL POPULATIONS
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DOI：
10.1007/bf02291366
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发表时间：
1971-01-01
期刊：
影响因子：
3
通讯作者：
JORESKOG, KG
中科院分区：
文献类型：
--
作者：
JORESKOG, KG
本文研究的是不同群体之间因素结构的异同。本文提出了一个非常一般的模型，在该模型中，不同群体的因子分析模型中的任何参数（因子载荷、因子方差、因子协方差和唯一方差）都可以被赋以任意值或被约束为等于其他参数。给定这样的规格，通过最大似然法估计模型，得到拟合优度的大样本x2。通过计算不同规格下的几个解决方案，可以测试各种假设。该方法能够处理任何程度的不变性，从一个极端，没有什么是不变的，到另一个极端，一切都是不变的。测试的数量和公共因子的数量都不需要对所有组都相同，但为了有趣，假设每个组合中有一个共同的测试核心，这些测试核心是相同的或至少在内容上是可比的。
This paper is concerned with the study of similarities and differences in factor structures between different groups. A common situation occurs when a battery of tests has been administered to samples of examinees from several populations.A very general model is presented, in which any parameter in the factor analysis models (factor loadings, factor variances, factor covariances, and unique variances) for the different groups may be assigned an arbitrary value or constrained to be equal to some other parameter. Given such a specification, the model is estimated by the maximum likelihood method yielding a large sample x2 of goodness of fit. By computing several solutions under different specifications one can test various hypotheses.The method is capable of dealing with any degree of invariance, from the one extreme, where nothing is invariant, to the other extreme, where everything is invariant. Neither the number of tests nor the number of common factors need to be the same for all groups, but to be at all interesting, it is assumed that there is a common core of tests in each battery that is the same or at least content-wise comparable.