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Handling missing data in survey research

J M Brick¹, G Kalton

¹Westat Inc., Rockville, Maryland 20850-3129, USA.

Statistical Methods in Medical Research

|September 1, 1996

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Survey research often faces missing data due to noncoverage, total nonresponse, or item nonresponse. This paper reviews weighting and imputation methods designed to address these issues, enabling robust data analysis.

Area of Science:

Survey Methodology
Statistical Data Analysis

Background:

Missing data is a common challenge in survey research.
It arises from noncoverage, total nonresponse, and item nonresponse.
These issues can compromise the integrity of survey data analysis.

Purpose of the Study:

To describe various methods for compensating for missing survey data.
To discuss the benefits and limitations of these compensation techniques.
To enable survey data analysis without the impediment of missing data.

Main Methods:

Review of weighting adjustments for noncoverage and total nonresponse.
Description of imputation methods for item nonresponses.
Discussion of the advantages and disadvantages of each technique.

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Main Results:

Weighting adjustments are commonly applied to address noncoverage and total nonresponse.
Imputation methods are utilized to fill in item nonresponses.
Both approaches offer solutions but have specific benefits and limitations.

Conclusions:

Effective methods exist to compensate for missing survey data.
Understanding the benefits and limitations of weighting and imputation is crucial.
These methods facilitate comprehensive analysis of survey data despite missing values.