Multivariate imputation by chained equations (MICE) is a particular multiple imputation technique (Raghunathan et al., 2001; Van Buuren, 2007). MICE operates under the assumption that given the variables used in the imputation procedure, the missing data are Missing At Random (MAR), which means that the probability that a value is missing depends only on observed values and not on unobserved values (Schafer & Graham, 2002). In other words, after controlling for all of the available data (i.e., the variables included in the imputation model) “any remaining missingness is completely random” (Graham, 2009). Implementing MICE when data are not MAR could result in biased estimates. In the remainder of this paper, we assume that the MICE procedures are used with data that are MAR. … Multivariate Imputation by Chained Equations (MICE)

# If you did not already know: “Multivariate Imputation by Chained Equations (MICE)”

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