The MI Procedure
- Overview
- Getting Started
-
Syntax
-
Details
Descriptive Statistics EM Algorithm for Data with Missing Values Statistical Assumptions for Multiple Imputation Missing Data Patterns Imputation Methods Monotone Methods for Data Sets with Monotone Missing Patterns Monotone and FCS Regression Methods Monotone and FCS Predictive Mean Matching Methods Monotone Propensity Score Method Monotone and FCS Discriminant Function Methods Monotone and FCS Logistic Regression Methods FCS Methods for Data Sets with Arbitrary Missing Patterns Checking Convergence in FCS Methods MCMC Method for Arbitrary Missing Multivariate Normal Data Producing Monotone Missingness with the MCMC Method MCMC Method Specifications Checking Convergence in MCMC Input Data Sets Output Data Sets Combining Inferences from Multiply Imputed Data Sets Multiple Imputation Efficiency Imputer’s Model Versus Analyst’s Model Parameter Simulation versus Multiple Imputation Summary of Issues in Multiple Imputation ODS Table Names ODS Graphics
-
Examples
EM Algorithm for MLE Monotone Propensity Score Method Monotone Regression Method Monotone Logistic Regression Method for CLASS Variables Monotone Discriminant Function Method for CLASS Variables FCS Method for Continuous Variables FCS Method for CLASS Variables FCS Method with Trace Plot MCMC Method Producing Monotone Missingness with MCMC Checking Convergence in MCMC Saving and Using Parameters for MCMC Transforming to Normality Multistage Imputation
- References
| Missing Data Patterns |
The MI procedure sorts the data into groups based on whether the analysis variables are observed or missing. Note that the input data set does not need to be sorted in any order.
For example, with variables 
, 
, and 
(in that order) in a data set, up to eight groups of observations can be formed from the data set. Figure 56.6 displays the eight groups of observations and an unique missing pattern for each group:
| Missing Data Patterns |
| Group | Y1 | Y2 | Y3 |
|---|---|---|---|
| 1 | X | X | X |
| 2 | X | X | . |
| 3 | X | . | X |
| 4 | X | . | . |
| 5 | . | X | X |
| 6 | . | X | . |
| 7 | . | . | X |
| 8 | . | . | . |
Here, an "X" means that the variable is observed in the corresponding group and a "." means that the variable is missing.
The variable order is used to derive the order of the groups from the data set, and thus determines the order of missing values in the data to be imputed. If you specify a different order of variables in the VAR statement, then the results are different even if the other specifications remain the same.
A data set with variables , , ..., 
(in that order) is said to have a monotone missing pattern when the event that a variable 
is missing for a particular individual implies that all subsequent variables 
, 
, are missing for that individual. Alternatively, when a variable is observed for a particular individual, it is assumed that all previous variables , 
, are also observed for that individual.
For example, Figure 56.7 displays a data set of three variables with a monotone missing pattern.
| Monotone Missing Data Patterns |
| Group | Y1 | Y2 | Y3 |
|---|---|---|---|
| 1 | X | X | X |
| 2 | X | X | . |
| 3 | X | . | . |
Figure 56.8 displays a data set of three variables with a non-monotone missing pattern.
| Non-monotone Missing Data Patterns |
| Group | Y1 | Y2 | Y3 |
|---|---|---|---|
| 1 | X | X | X |
| 2 | X | . | X |
| 3 | . | X | . |
| 4 | . | . | X |
A data set with an arbitrary missing pattern is a data set with either a monotone missing pattern or a non-monotone missing pattern.