van Ginkel Joost R, Kroonenberg Pieter M
Leiden University.
Multivariate Behav Res. 2014 Jan 1;49(1):78-91. doi: 10.1080/00273171.2013.855890.
As a procedure for handling missing data, Multiple imputation consists of estimating the missing data multiple times to create several complete versions of an incomplete data set. All these data sets are analyzed by the same statistical procedure, and the results are pooled for interpretation. So far, no explicit rules for pooling -tests of (repeated-measures) analysis of variance have been defined. In this paper we outline the appropriate procedure for the results of analysis of variance for multiply imputed data sets. It involves both reformulation of the ANOVA model as a regression model using effect coding of the predictors and applying already existing combination rules for regression models. The proposed procedure is illustrated using three example data sets. The pooled results of these three examples provide plausible - and -values.
作为一种处理缺失数据的方法，多重填补包括多次估计缺失数据，以创建不完整数据集的几个完整版本。所有这些数据集都通过相同的统计程序进行分析，并汇总结果以供解释。到目前为止，尚未定义用于（重复测量）方差分析合并检验的明确规则。在本文中，我们概述了对多重填补数据集进行方差分析结果的适当程序。这涉及将方差分析模型重新表述为使用预测变量效应编码的回归模型，并应用已有的回归模型组合规则。使用三个示例数据集说明了所提出的程序。这三个示例的汇总结果提供了合理的 - 值和 - 值。
