Studies of Welfare Populations: Data Collection and Research Issues. The Nature of Nonresponse Error in Survey Statistics

06/01/2002

Sample surveys used to describe low-income populations are effective only when several things go right. The target population must be defined well, having the geographical and temporal extents that fit the goals of the survey. The sampling frame, the materials used to identify the population, must include the full target population. The measurement instrument must be constructed in a way that communicates the intent of the research question to the respondents, ideally in their nomenclature and within their conceptual framework. The sample design must give known, nonzero chances of selection to each low-income family/person in the sampling frame. All sample persons must be contacted and measured, eliminating nonresponse error. Finally, the administration of the measurement instrument must be conducted in a manner that fulfills the design.

Rarely does everything go exactly right. Because surveys are endeavors that are (1) customized to each problem, and (2) constructed from thousands of detailed decisions, the odds of imperfections in survey statistics are indeed large. As survey methodology, the study of how alternative survey designs affect the quality of statistics, matures, it is increasingly obvious that errors are only partially avoidable in surveys of human populations. Instead of having the goal of eliminating errors, survey researchers must learn how to reduce them within reason and budget and then attempt to gain insight into their impacts on key statistics in the survey.

This paper is a review of a large set of classic and recent findings in the study of survey nonresponse, a growing concern about survey quality. It begins with a review of what nonresponse means and how it affects the quality of survey statistics. It notes that nonresponse is relevant to simple descriptive statistics as well as measures of the relationship between two attributes (e.g., length of time receiving benefits and likelihood of later job retention). It then reviews briefly what survey statisticians can do to reduce the impact of nonresponse after the survey is complete, through various changes in the analysis approach of the data.

After this brief overview of the basic approaches to reducing the impacts of nonresponse on statistical conclusions from the data concludes, the paper turns to reducing the problem of nonresponse. It reviews current theoretical viewpoints on what causes nonresponse as well as survey design features that have been found to be effective in reducing nonresponse rates.

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