Handling missing data in survey research.

Missing data occur in survey research because an element in the target population is not included on the survey's sampling frame (noncoverage), because a sampled element does not participate in the survey (total nonresponse) and because a responding sampled element fails to provide acceptable responses to one or more of the survey items (item nonresponse). A variety of methods have been developed to attempt to compensate for missing survey data in a general purpose way that enables the survey's data file to be analysed without regard for the missing data. Weighting adjustments are often used to compensate for noncoverage and total nonresponse. Imputation methods that assign values for missing responses are used to compensate for item nonresponses. This paper describes the various weighting and imputation methods that have been developed, and discusses their benefits and limitations.

Overview of important design issues for a National Human Exposure Assessment Survey.

Exposure issues have important consequences for regulatory decisions. Reliable answers to exposure questions are critical for site cleanup, model validation, and cumulative risk issues, as well as giving perspective on our risk estimates. This paper discusses some of the important issues in designing the National Human Exposure Assessment Survey (NHEXAS) and, by implication, other exposure-monitoring-based studies as well. Sampling design issues are discussed in terms useful to exposure assessors. These issues include simple random sample designs versus more complex multistage designs, design efficiency, how to determine the sample size for the desired precision of the estimate, and the effects of stratification and oversampling on the needed sample size. This paper also discusses several important nonsampling issues such as population definition, response rates, and several potential sources of error in interpreting the monitoring results.