Opportunities and Challenges in Dealing with Selection Bias in Cross-sectional and Longitudinal Surveys 1 |
|
| Coordinator 1 | Professor Sabine Zinn (Socio-Economic Panel at DIW ) |
| Coordinator 2 | Dr Jason M. Fields (U.S. Census Bureau) |
| Coordinator 3 | Dr Hans Walter Steinhauer (Socio-Economic Panel at DIW) |
Analysing survey data usually also means coping with selection bias. There are proven and well-established strategies for doing so, such as survey weighting or selection modelling. However, still many data users struggle in understanding how to apply these strategies, especially when confronted with the diversity of the information given by the survey providers. Beyond that, increasingly researchers use machine learning and Bayesian statistics in survey data analysis. This is also true for conducting and controlling surveys. Specifically, adaptive contact or motivational strategies are designed for upcoming survey studies or waves based on response processes observed in previous surveys or survey waves. The estimation of population statistics is improved by including information about the entire selection process in the statistical model, both developing these methods and communicating their use are critical.
In this session, we welcome research on novel approaches and strategies to ease data users understanding of how to handle selection bias in their statistical analysis. This research might cover:
-Methods for easing, and communicating, the appropriate use of weights or other methods for addressing selection biases in published microdata files. These may include, but are not limited to, longitudinal weights, calendar year weights, replicate weights, multiple implicates, and other tools to improve the population representativeness and communication of uncertainty in public data products.
-Novel methods to assess and adjust for sources of bias in cross-sectional and longitudinal surveys, including, but not limited to, machine learning interventions, adaptive design, post-hoc weighting calibrations, informed sampling, etc. How are these communicated to data users? How are they adapted as response and biases change?
-Papers are encouraged that investigate the selection processes, papers that leverage novel modelling strategies for coping with selection bias in statistical analysis, and papers that include examples of modelling non-ignorable selection bias in substantive analysis.