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Using the monotone logistic aspect of the multiple imputation procedure, we imputed the missing data.
After exploring the missing data, we imputed the missing values using multiple imputation by chained equations (Van Buuren et al. 1999; Royston, 2004).
We imputed the missing data using four different imputation models.
All estimates were robust as to whether we imputed the missing income data or excluded the imputed income data from the analysis.
To reduce the risk of biased results and to increase the statistical power of our analysis, we imputed the missing values five times using multiple imputation.
We imputed the Hap550 genotypes from these samples and compared the imputed genotypes with the SNPs previously present only in the ImmunoChip data.
We imputed the data in a way that was congenial with the analysis model.
If <10 % of observations on a variable were missing, we imputed the missing values using the mean or median.
As data on immigrants' education level at the time of migration are not well registered, we imputed the missing data as follows.
In cases of inconsistent data, we imputed the educational level that corresponded to the educational level that was closest to the ISCO level.
For each rate rij, whose maximum likelihood estimate was zero, we imputed the value of the rate using the following heuristic.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com