I’m currently working on an analysis of the latest state election in Rhineland-Palatinate using aggregate data alone, i.e. electoral returns and structural information, which is available at the level of the state’s roughly 2300 municipalities. The state’s Green party (historically very weak) has roughly tripled their share of the vote since the last election in 2006, and I want to know were all these additional votes come from. And yes, I’m treading very careful around the very large potential ecological fallacy that lurks at the centre of my analysis, regressing Green gains on factors such as tax receipts and distance from next university town, but never claiming that the rich or the students or both turned to the Greens.

One common problem with this type of analysis is that not all municipalities are created equal. There is a surprisingly large number of flyspeck villages with only a few dozen voters on, whereas the state’s capital boasts more than 140,000 registered voters. Most places are somewhere in between. Having many small municipalities in the regression feels wrong for at least two reasons. First, small-scale changes of political preferences in tiny electorates will result in relatively large percentage changes. Second, the behaviour of a relatively large number of voters who happen to live in a small number of relatively large municipalities will be grossly underrepresented, i.e. the countryside will drive the results.

Continue reading “Robust Regression of Aggregate Data in Stata” »

Should one weight their survey data? Is it worth the effort? The short answer must be ‘maybe’ or ‘it depends’. A slightly longer and much more useful answer was given by Leslie Kish in his enormously helpful paper ‘Weighting: Why, when and how’. Today (well, actually I submitted the final manuscript 2.5 years ago – that’s scientific progress for you!), I have added my own two cent with a short chapter that looks at the effects and non-effects of common weighting procedures (in German). The bottom line is that if you employ the usual weighting variables (age, gender, education and maybe class or region) as controls in your regression, weighting will make next to no difference but might mess with your standard errors.
Continue reading “Weighting Survey Data: Not Necessarily a Brilliant Idea” »

Six weeks ago, I have reviewed Kam’s and Franzese’s Modeling and Interpreting Interactive Hypotheses in Regression Analysis. This week, the topic of interaction effects pops up on the Social Science Statistics Blogs, with pointers to useful FAQs and other pages.
Technorati Tags: interaction effects, statistics, regression