Multiple Imputation in Mplus

• Prior to performing the analyses, we used multiple imputation to
deal with the missing data. Briefly, multiple imputation uses a
regression-based procedure to generate multiple copies of the
data set, each of which contains different estimates of the missing
values. We used the fully conditional specification algorithm in the
SPSS multiple imputation procedure to generate 50 imputed data
sets. An exploratory analysis suggested that the data sets should
be separated by at least 100 iterations, so we took a conservative
approach of saving a data set after every 300 th computational
cycle. The imputation model included the five regression model
parameters and IQ scores.