A Review of Methods for Missing Data

This paper reviews methods for handling missing data in a research study. Many researchers use ad hoc methods such as complete case analysis, available case analysis (pairwise deletion), or single-value imputation. Though these methods are easily implemented, they require assump-tions about the data that rarely hold in practice. Model-based methods such as maximum likelihood using the EM algorithm and multiple imputation hold more promise for dealing with difficulties caused by missing data. While model-based methods require specialized computer programs and assumptions about the nature of the missing data, these methods are appropriate for a wider range of situations than the more commonly used ad hoc methods. The paper provides an illustration of the methods using data from an intervention study designed to increase students ’ ability to control their asthma symptoms. All researchers have faced the problem of missing quantitative data at some point in their work. Research informants may refuse or forget to answer a survey question, files are lost, or data are not recorded properly. Given the expense of collecting data, we cannot afford to start over or to wait until we have developed foolproof methods of gathering information, an unattainable goal. We find ourselves left with the decision of how to analyze data when we do not have complete information from all informants. We are not alone in this problem; the United States Census Bureau has been involved in a debate with the U.S. Congress and the U.S. Supreme Court over the handling of the under-count in the 2000 U.S. Census. Given that most researchers do not have the resources of the U.S. Census Bureau, what are the options available for analyzing data with missing information?