Multiple imputation (MI) of missing values in hierarchical data can be tricky when the data do not have a simple two-level structure. In such a case, understanding and accounting for the hierarchical structure of the data can be challenging, and tools to handle these types
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The analysis of variance, or ANOVA, is among the most popular methods for analyzing how an outcome variable differs between groups, for example, in observational studies or in experiments with different conditions.
But how do we conduct the ANOVA when there are missing data?
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Other websites and blogs I enjoy and which are concerned with statistics, social science, or R include (among others):
Many other blogs about R can be found at R Bloggers.