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Social scientists often analyze data using models--of which there are many different kinds. One class of models -- known variously as multilevel, random effects, hierarchical, or mixed models -- are now a major tool for social science data analysis, but we're still figuring out how to make the best use of them in different kinds of studies.

I'm a frequent user of these models, and I have in some cases been able to write papers with advice for others, based on what I've learned from using them myself. For example, I've written about how to analyze what I call comparative longitudinal survey data: survey data collected in a set of societies multiple times, but where the specific people surveyed change each time.

Particularly in my guise as a methodologist, I'm an active member of the European Survey Research Association. I'm currently on the board as one of the prize coordinators.

I'm a former visiting researcher at the Research and Expertise Centre for Survey Methodology in Barcelona.