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首页> 外文期刊>Educational and Psychological Measurement >Constrained maximum likelihood estimation for two-level mean and covariance structure models
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Constrained maximum likelihood estimation for two-level mean and covariance structure models

机译：两级均值和协方差结构模型的约束最大似然估计

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摘要

Maximum likelihood is commonly used for the estimation of model parameters in the analysis of two-level structural equation models. Constraints on model parameters could be encountered in some situations such as equal factor loadings for different factors. Linear constraints are the most common ones and they are relatively easy to handle in maximum likelihood analysis. Nonlinear constraints could be encountered in complicated applications. The authors develop an EM-type algorithm for estimating model parameters with both linear and nonlinear constraints. The empirical performance of the algorithm is demonstrated by a Monte Carlo study. Application of the algorithm for linear constraints is illustrated by setting up a two-level mean and covariance structure model for a real two-level data set and running an EQS program.
机译：在两级结构方程模型的分析中，最大似然通常用于估计模型参数。在某些情况下可能会遇到对模型参数的约束，例如不同因素的相等因素载荷。线性约束是最常见的约束，在最大似然分析中相对容易处理。在复杂的应用中可能会遇到非线性约束。作者开发了一种用于估计具有线性和非线性约束的模型参数的EM型算法。蒙特卡洛研究证明了该算法的经验性能。通过为实际的两级数据集建立两级均值和协方差结构模型并运行EQS程序来说明该算法在线性约束中的应用。

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