How do you do multiple imputation?
How Multiple Imputation Works
- Create m sets of imputations for the missing values using a good imputation process.
- The result is m full data sets.
- Analyze each completed data set.
- Combine results, calculating the variation in parameter estimates.
How do you do imputation in SAS?
Imputation in SAS requires 3 procedures. The first is proc mi where the user specifies the imputation model to be used and the number of imputed datasets to be created. The second procedure runs the analytic model of interest (here it is a linear regression using proc glm) within each of the imputed datasets.
What does PROC MI do in SAS?
In SAS, Proc MI is used to replace missing values with multiple imputation. 1. Monotone : If a variable has missing data, all variables to the right of the missing data variable in a rectangular data array are also missing.
What are Rubin’s rules?
Rubin´s Rules (RR) are designed to pool parameter estimates, such as mean differences, regression coefficients, standard errors and to derive confidence intervals and p-values. We illustrate RR with a t-test example in 3 generated multiple imputed datasets in SPSS.
What are the types of multiple imputation treatments?
Complete Case Analysis: This methods involves deleting cases in a particular dataset that are missing data on any variable of interest.
What is multiple imputation analysis?
Multiple imputation (MI) is a way to deal with nonresponse bias — missing research data that happens when people fail to respond to a survey. The technique allows you to analyze incomplete data with regular data analysis tools like a t-test or ANOVA.
How many imputations are needed?
An old answer is that 2–10 imputations usually suffice, but this recommendation only addresses the efficiency of point estimates. You may need more imputations if, in addition to efficient point estimates, you also want standard error (SE) estimates that would not change (much) if you imputed the data again.
What is single and multiple imputation?
In single imputation, the imputed value is treated as the true value, ignoring the fact that the no imputation method can provide the exact value. Multiple imputation was proposed by Rubin (1987). In this method D imputed values for each of the missing observation is generated and hence we get D complete data set.
Which variables include in multiple imputation?
The general strategy is to include at least all variables involved in the planned analysis. For example, when imputing missing predictors, the outcome variables should be included in imputation to retain the association between the outcome and predictors.
How many variables should be in multiple imputation?
As an example, with 100 cases and 40% missing data, 60 cases have complete data. Hence, no more than 60/3 = 20 variables should be used in the imputation model.