The Classical Linear Regression Model with one Incomplete Binary Variable

We present three different methods based on the conditional mean
imputation when binary explanatory variables are incomplete. Apart
from the single imputation and multiple imputation especially the
so-called pi imputation is presented as a new procedure. Seven
procedures are compared in a simulation experiment when missing
data are confined to one independent binary variable: complete case
analysis, zero order regression, categorical zero order regression,
pi imputation, single imputation, multiple imputation, modified
first order regression. After a brief theoretical description of
the simulation experiment, MSE-ratio, variance and bias are used to
illustrate differences within and between the approaches.