Dual balance correction in ANOVA repeated measures by missing data


The repeated measures ANOVA is being used in study of longitudinal data. In its standard form, the method requires full data. In practice, this is not always possible. In this paper, we consider an approach in which the missing data are neither ignored nor imputed. The   incomplete data result in biased estimations of the time mean. This bias consists of the individual and the group components which are constructed using Markov sequence. The last adjustment is necessary in cases of different numbers of missing values incompared groups.

DOI Code: 10.1285/i20705948v10n1p146

Keywords: ANOVA Repeated Measures; missing data; centralization of model; Markov sequence


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