The workshop will take place on July 14th, 9:00 AM CEST on Zoom
Course Details
Traditional design-based survey inference (aka the simple random sample) is increasingly costly and impractical. Model-based survey inference has gained prominence in the last twenty years. Specifically, multilevel regression with post-stratification (MrP) has become a standard for small area estimation. In this course I will introduce you to what MrP can do and identify two weaknesses of the classic approach. First, the census-data constraint for the individual-level information. Second, the lack of disciplined feature selection and functional form. But both of these problems can be addressed. I will cover MrsP (MrP’s better half) that is more flexible for individual-level information and then also cover autoMrP which leverages machine learning to produce an improved response model.
Instructor
Prof. Lucas Leemann
Lucas Leemann is Assistant Professor for Comparative Politics and Empirical Democracy Research at the University of Zurich. His research focuses on representation and how citizens’ preferences translate into policies. His methodological work is concerned with measurement in surveys with a special focus on small area estimation (MrP, MrsP, and other variations). He has published in the American Political Science Review, American Journal of Political Science, the Journal of Politics, Political Analysis and other journals.
Schedule
Session Starts
Modern Survey Methods:MrP and MrsP (Part I)
Short Break
Session Continues
Modern Survey Methods:MrP and MrsP (Part II)
Session Ends
Content Licensing
All workshop materials and recording are under Creative Commons Attribution-NonCommercial-ShareAlike 2.0 license. You are free to share — copy and redistribute the material in any medium or format, and adapt — remix, transform, and build upon the material. However, you must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not use the material for commercial purposes. If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
