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首页> 外文期刊>Psychometrika >Simplified Estimation and Testing in Unbalanced Repeated Measures Designs
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Simplified Estimation and Testing in Unbalanced Repeated Measures Designs

机译：在不平衡重复措施设计中简化的估计和测试

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摘要

In this paper we propose a simple estimator for unbalanced repeated measures design models where each unit is observed at least once in each cell of the experimental design. The estimator does not require a model of the error covariance structure. Thus, circularity of the error covariance matrix and estimation of correlation parameters and variances are not necessary. Together with a weak assumption about the reason for the varying number of observations, the proposed estimator and its variance estimator are unbiased. As an alternative to confidence intervals based on the normality assumption, a bias-corrected and accelerated bootstrap technique is considered. We also propose the naive percentile bootstrap for Wald-type tests where the standard Wald test may break down when the number of observations is small relative to the number of parameters to be estimated. In a simulation study we illustrate the properties of the estimator and the bootstrap techniques to calculate confidence intervals and conduct hypothesis tests in small and large samples under normality and non-normality of the errors. The results imply that the simple estimator is only slightly less efficient than an estimator that correctly assumes a block structure of the error correlation matrix, a special case of which is an equi-correlation matrix. Application of the estimator and the bootstrap technique is illustrated using data from a task switch experiment based on an experimental within design with 32 cells and 33 participants.
机译：在本文中，我们提出了一种简单的估计，用于不平衡重复测量设计模型，其中每个单元在实验设计的每个单元中观察到。估算器不需要错误协方差结构的模型。因此，不需要误差协方差矩阵的循环度和相关参数和差异的估计。与对不同观察数的原因的弱假设一起，所提出的估计器及其方差估计器是无偏见的。作为基于正常假设的置信区间的替代方案，考虑了偏置和加速的自动启动技术。我们还提出了WALD型测试的Naive百分位引导，当观察的数量相对于估计的参数数量很小时，标准WALD测试可能会破裂。在仿真研究中，我们说明了估计器的特性和自举技术，以计算置信区间，并在误差的正常性和非正常性下进行小型和大样本的假设试验。结果意味着简单的估计器仅略微效率低于正确假定误差相关矩阵的块结构的估计器，这是一个特殊情况是Equi-Collelix。使用基于设计内的实验与32个细胞和33名参与者的实验，使用来自任务开关实验的数据来说明估计器和引导技术的应用。

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