How can I perform multiple imputation on longitudinal data?
The following example shows how to impute longitudinal data, accommodating the structure of this type of data. The example dataset contains data on student’s reading and math scores at three time points ( read and math respectively), as well as data on the time invariant covariates female, private, and ses.
What do you need to know about multiple imputation?
Multiple imputation and other modern methods such as direct maximum likelihood generally assumes that the data are at least MAR, meaning that this procedure can also be used on data that are missing completely at random.
How is the multiply imputation data stored in Stata?
A dataset that is mi set is given an mi style. This tells Stata how the multiply imputed data is to be stored once the imputation has been completed. For information on these style type help mi styles into the command window.
Which is data set for multiple imputation in SAS?
The data set hsb_mar.sas7bdat which is based on hsb2.sas7bdat used for this seminar can be downloaded from the link. The SAS code for this seminar is developed u sing SAS 9.4 and SAS/STAT 13.1. So me of the variables have value labels (formats) associated with them.
Stata has a suite of multiple imputation (mi) commands to help users not only impute their data but also explore the patterns of missingness present in the data. In order to use these commands the dataset in memory must be declared or mi set as “mi” dataset.
Are there any drawbacks to using multiple imputation?
One of the main drawbacks of this method is no consistent sample size and the parameter estimates produced are often much different than the estimates obtained from analysis on the full data or the listwise deletion approach. Unless the mechanism of missing data is MCAR, this method will introduce bias into the parameter estimates.