Advanced Topics in Applied Regression: Modelling Issues

Note: This course can be followed by SD207B – Advanced Topics in Applied Regression: Data Issues and Causal Approaches Short Bio Levi holds a PhD in Political Science from the University of Nebraska-Lincoln where he also studied Survey Research and Methodology. Held visiting positions at Washington State, Eotvos Lorand and Zagreb University and taught a number of workshops on statistical topics. Predominantly a methodologist, Levi’s research strives to find new, interdisciplinary analytical strategies to complex problems and research questions. Currently researches statistical methods, political behavior and political psychology. Prerequisite knowledge Note from the Academic Convenors to prospective participants: by registering to this course, you certify that you possess the prerequisite knowledge that is requested to be able to follow this course. The instructor will not teach again these prerequisite items. If you doubt whether you possess that knowledge to a sufficient extent, we suggest you contact the instructor before you proceed to your registration. You need a solid understanding of linear and logistic regression to the level that is described in the following texts. Michael Lewis-Beck. (1980). Applied Regression: An Introduction. Newbury Park, CA: Sage, John Fox. (1991). Regression Diagnostics. Newbury Park, CA: Sage and Fred C. Pampel. (2000). Logistic Regression: A Primer. Newbury Park, CA: Sage (All books are from the Quantitative Applications in the Social Science, aka. little green books, series.) If you have never used R, you are required to take the refresher course in R. If you have some basic working knowledge of R (even if it is not much), you know how to use and manage data and you ran models before, you should be fine in this class. In this course we will use R and Mplus. You do not need a working knowledge of Mplus for the class. But some basic knowledge of R is necessary. This course starts where the two-week ECPR Summer Course on Multiple Regression Analysis ends. Please also consult that study plan to assess if you are ready for this course or if you should be taking that course instead. Short outline Once a researcher becomes comfortable with regression, often the question arises. What next? Building on the assumptions regression models make (especially independence, causality, linearity and lack of measurement error), this course offers an overview of multitude of ways the assumptions can be relaxed. In the process the course trains researchers to carefully think about these assumptions and become better data analysts and social scientists at the same time. The relaxing of regression assumptions allows us to look at the world from a new angle, to ask novel research questions. The course offers an introduction to many statistical techniques that either complement or build on regression analysis. These include in depth understanding of interactions, fixed and random effects, multilevel, and quasi-experimental causal models. We also discuss more precise estimation through corrections for less precise measures, sampling bias, unit nonresponse, item nonresponse, bootstrapping and advanced model selection.