最大似然联合对应分析

IF 2 3区心理学 Q2 PSYCHOLOGY, MATHEMATICAL

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Pub Date : 2005-12-01 DOI:10.1027/1614-1881.1.1.18

J. Vermunt, Carolyn J. Anderson

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引用次数: 13

摘要

摘要联合对应分析(JCA)中的参数估计通常采用加权最小二乘方法，以Burt矩阵作为数据矩阵。在本文中，我们展示了如何用极大似然的方法来估计JCA模型。为此，通过推广Choulakian (1988a, 1988b)提出的三向表对应分析模型，将JCA定义为全k向分布的模型。将JCA置于更正式的统计框架中的优点是，可以应用标准卡方检验来评估不受限制和受限制模型的拟合优度。

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Joint Correspondence Analysis (JCA) by Maximum Likelihood

Abstract. Parameter estimation in joint correspondence analysis (JCA) is typically performed by weighted least squares using the Burt matrix as the data matrix. In this paper, we show how to estimate the JCA model by means of maximum likelihood. For that purpose, JCA is defined as a model for the full K-way distribution by generalizing the correspondence analysis model for three-way tables proposed by Choulakian (1988a, 1988b). The advantage of placing JCA in a more formal statistical framework is that standard chi-squared tests can be applied to assess the goodness-of-fit of unrestricted and restricted models.

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来源期刊

Methodology: European Journal of Research Methods for The Behavioral and Social Sciences Multiple-

CiteScore

2.70

自引率

6.50%

发文量

审稿时长

36 weeks