TRAINING KEY 123: Imputation for Item Nonresponse

Practical Methods for Design and Analysis of Complex Surveys.
Risto Lehtonen and Erkki Pahkinen

TRAINING KEY 123: Imputation for Item Nonresponse



INTRODUCTION

A) REFERENCE EXAMPLE 4.3: SAS CODE AND OUTPUT (Single Imputation)

Imputation implies that a missing value of the study variable y for a sample element k in the data matrix is substituted by an imputed value . For example, in some computer packages, a technique called mean imputation is available, in which an overall respondent mean ,calculated from the respondent values of the study variable, is inserted in place of the missing values for that variable. Then the imputed value for element k is = . This method is not generally valid, and alternative methods are demonstrated in this Training Key. The methods include single imputation methods and multiple imputation methods. Further instructions will be given once you start.
 

 


B) REFERENCE EXAMPLE 4.3: SAS CODE AND OUTPUT (Multiple Imputation)

In multiple imputation, we predict m values for each missing item. We thus create m completed data sets. Further instructions will be given once you start.
 

 


C) INTERACTIVE SAS USE

Please download the SAS code for your own further training. Select your own sample or several samples and exercise imputation with different sample sizes for a SRSWOR sample. The macro parameters used in the application are DATA = data set now, n = sample size, SEED = seed for the random number generator, VAR = dependent variable, AUX = auxiliary variable and REP = (in MI) for number of complete data sets (recommendation m = 2,3,4 or 5). You may choose 1 < n < 32 elements in the sample and by changing the seed a different sample configuration will be obtained.

NOTE! You need to have access to SAS in your computer.
 

Single Imputation macro
Multiple Imputation macro