Barendse M T, Ligtvoet R, Timmerman M E, Oort F J
Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University Ghent, Belgium.
Department of Education, Research Institute of Child Development and Education, University of Amsterdam Amsterdam, Netherlands.
Front Psychol. 2016 Apr 21;7:528. doi: 10.3389/fpsyg.2016.00528. eCollection 2016.
Maximum likelihood factor analysis of discrete data within the structural equation modeling framework rests on the assumption that the observed discrete responses are manifestations of underlying continuous scores that are normally distributed. As maximizing the likelihood of multivariate response patterns is computationally very intensive, the sum of the log-likelihoods of the bivariate response patterns is maximized instead. Little is yet known about how to assess model fit when the analysis is based on such a pairwise maximum likelihood (PML) of two-way contingency tables. We propose new fit criteria for the PML method and conduct a simulation study to evaluate their performance in model selection. With large sample sizes (500 or more), PML performs as well the robust weighted least squares analysis of polychoric correlations.
观察到的离散反应是呈正态分布的潜在连续分数的表现形式。由于最大化多元反应模式的似然性在计算上非常密集，因此改为最大化二元反应模式对数似然性的总和。当分析基于双向列联表的这种成对最大似然（PML）时，对于如何评估模型拟合了解甚少。我们为PML方法提出了新的拟合标准，并进行了模拟研究以评估它们在模型选择中的性能。对于大样本量（500或更多），PML的表现与多相关系数的稳健加权最小二乘分析一样好。
