Methods for Handling Missing Secondary Respondent Data.

Secondary respondent data are underutilized because researchers avoid using these data in the presence of substantial missing data. We reviewed, critically evaluated, and tested potential solutions to this problem. Five strategies of dealing with missing partner data are reviewed: complete case analysis, inverse probability weighting, correction with a Heckman selection model, maximum likelihood estimation, and multiple imputation. Two approaches were used to evaluate the performance of these methods. First, we 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, we conducted a simulation based on these data testing the five methods and compared the results to estimates where the true value was known. We 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.