Evaluating the effect of within-household subsampling on the precision of crime victimization rates

By Vincent Iannacchione, Bonnie Shook-Sa

The decision to select a subsample of eligible members of a sampled household is influenced by a number of factors including burden on the household, data quality, cost, and the sampling variance of survey estimates. Design effects quantify the influence of a complex sampling design on the variance of survey estimates. Selecting a subsample of eligible persons within a sampled household can have counteracting impacts on design effects. On one hand, subsampling increases the design effects attributable to unequal weighting. On the other hand, subsampling could reduce the design effects attributable to clustering because the potential intra-household correlation among respondents in the same household may be reduced or eliminated. If the reduction in correlation is greater than the increase caused by unequal weighting, subsampling can achieve the same sampling variance as selecting all eligible household members, with less cost and burden. We present the results of a simulation study that evaluates the design effects associated with subsampling household members on personal victimization rates based on the 2008 National Crime Victimization Survey, which selected all persons 12 and older in a sampled household.


Iannacchione, V., & Shook-Sa, B. (2013). Evaluating the effect of within-household subsampling on the precision of crime victimization rates. (RTI Press Publication No. MR-0025-1307). Research Triangle Park, NC: RTI Press. https://doi.org/10.3768/rtipress.2013.mr.0025.1307

© 2019 RTI International. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.


Vincent IannacchioneVincent G. Iannacchione, MS, is a senior research statistician at RTI International in Washington, DC.

Bonnie Shook-SaBonnie E. Shook-Sa, MAS, is a research statistician at RTI International. Her research focuses on sampling frame development and evaluation, sample design and optimization, and the analysis of complex data for household and establishment surveys.

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