developed by the Researchers at the Survey Methodology Program,
Survey Research Center, Institute for Social Research, University of
- Single or multiple imputations of missing values using the Sequential
Regression Imputation Method described in the article
technique for multiply imputing missing values using a sequence of
regression models" by Raghunathan, Lepkowski, Van Hoewyk and
- A variety of descriptive and model based analyses accounting for
complex design features such as clustering, stratification and
- Multiple imputation analyses for both descriptive and model-based
- Create partial or full synthetic data sets using the sequential
regression approach to protect confidentiality and limit statistical
- Combine information from multiple sources by vertically
concatenating data sets and multiply imputing the missing portions to
create a larger rectangular data set.
includes six modules: IMPUTE, DESCRIBE, REGRESS, SASMOD, SYNTHESIZE and
IVEware is available in several different versions targeting different operating systems and configurations:
- IMPUTE uses a multivariate sequential regression
for multiply imputing item missing values in a data set.
- DESCRIBE estimates the population means, proportions,
differences, contrasts and linear combinations of means and
proportions. For complex surveys, the Taylor Series approach is used to
estimates. The item missing values can be multiply imputed for the
variables while perfoming the analysis.
- REGRESS fits linear, logistic, polytomous, Poisson, Tobit
proportional hazard regression models. The Jackknife Repeated
Replication (JRR) approach is used to estimate
the sampling variances for complex survey data. The item missing values
may be multiply imputed while performing the regression analysis.
- SASMOD allows users to analyze data with several SAS
procedures. Currently the
following SAS PROCS can be called: CALIS, CATMOD, GENMOD, LIFEREG,
MIXED, NLIN, PHREG, and PROBIT. The JRR approach is used for complex
survey data and the missing values can be multiply imputed while
performing these analyses.
uses multivariate sequential regression approach to create full or partial synthetic data sets to limit statistical disclosure
Raghunathan, Reiter and Rubin (2003) ,
Reiter (2002) and
Little,Liu and Raghunathan (2004) for more details.)
All item missing values will also be imputed when creating synthetic data sets.
However, DESCRIBE, REGRESS and SASMOD modules cannot be used to analyze synthetic data sets
as they DO NOT implement the appropriate combining rules.
is useful for combining information from multiple sources through
Suppose that Data 1 provides variables X and Y, Data 2 provides
variables X and Z and Data 3 provides variables Y and Z. COMBINE can be
used to concatenate the three data sets and multiply impute the missing
values of X, Y and Z to create large data sets with complete data on
all three variables. All item missing values in the individual data
sets will also be imputed. The multiply imputed combined data sets can
be analyzed using DESCRIBE, REGRESS and SASMOD modules.
Version 0.2 Desktop IVEware (Requires SAS) and SRCware (Stand-alone) Download Documentation, Software, and Examples
Version 0.1 Desktop IVEware (Requires SAS) and SRCware (Stand-alone) Download Documentation, Software, and Examples
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Last updated February 2, 2016