Multiple Imputation in Mplus

• After creating the complete data sets, we estimated the multiple
regression model on each filled-in data set and subsequently
used Rubin’s (1987) formulas to combine the parameter
estimates and standard errors into a single set of results. Note
that methodologists currently regard multiple imputation as a
“state of the art” missing data technique (Schafer & Graham,
2002) because it requires less strict assumptions about the
mechanism that led to missing data and generally produces
more accurate estimates than traditional missing data handling
techniques (e.g., discarding cases).