Several researchers have begun this effort already. The post-survey adjustment methods applied to non-probability sampling have largely mirrored efforts in probability samples. Although this may be appropriate and effective to some extent, further consideration of selection bias mechanisms may be needed. We believe an agenda for advancing a method must include these attributes.

BackgroundIn conducting population-based surveys, it is important to thoroughly examine and adjust for potential non-response bias to improve the representativeness of the sample prior to conducting analyses of the data and reporting findings. This paper examines factors contributing to second stage survey non-response during the baseline data collection for the Millennium Cohort Family Study, a large longitudinal study of US service members and their spouses from all branches of the military.MethodsMultivariate logistic regression analysis was used to develop a comprehensive response propensity model.ResultsResults showed the majority of service member sociodemographic, military, and administrative variables were significantly associated with non-response, along with various health behaviours, mental health indices, and financial and social issues. However, effects were quite small for many factors, with a few demographic and survey administrative variables accounting for the most substantial variance.ConclusionsThe Millennium Cohort Family Study was impacted by a number of non-response factors that commonly affect survey research. In particular, recruitment of young, male, and minority populations, as well as junior ranking personnel, was challenging. Despite this, our results suggest the success of representative population sampling can be effectively augmented through targeted oversampling and recruitment, as well as a comprehensive survey weighting strategy.

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