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Methods for Handling Missing Secondary Respondent Data


  • Department of Sociology, The Pennsylvania State University, 211 Oswald Tower, University Park, PA 16802.

  • This article was edited by Jay Teachman.

Department of Biostatistics, Collaborative Health Studies Coordinating Center, University of Washington, Box 354922, Seattle, WA 98195 (


Secondary respondent data are underutilized because researchers avoid using these data in the presence of substantial missing data. The authors reviewed, evaluated, and tested solutions to this problem. Five strategies of dealing with missing partner data were reviewed: (a) complete case analysis, (b) inverse probability weighting, (c) correction with a Heckman selection model, (d) maximum likelihood estimation, and (e) multiple imputation. Two approaches were used to evaluate the performance of these methods. First, the authors used data from the National Survey of Fertility Barriers (n = 1,666) to estimate a model predicting marital quality based on characteristics of women and their husbands. Second, they conducted a simulation testing the 5 methods and compared the results to estimates where the true value was known. They found that the maximum likelihood and multiple imputation methods were advantageous because they allow researchers to utilize all of the available information as well as produce less biased and more efficient estimates.