The course in survey design takes student beyond the introductory courses offered in BA and MA programmes, and discusses the state-of-the-art of one of the most important data collection techniques: surveys. The course focuses on the methodology of how to do surveys, and the use statistical techniques to analyse and correct for some specific survey errors. It combines short 1-hour lectures with exercises on most of the topics discussed. We assume course participants are proficient in working with R.
With Vera Toepoel , and people at NIDI and the German Institiute for Demographic Research we have in 2018 conducted a large experiment to tranfer the Gender and Generations Survey to a push-to-web survey. Several papers will document what we did, and what particular designs worked and did not work.These papers will come out later in 2021, and I may publish some more posts about this design. In this post I want to focus on the role of incentives in push-to-web surveys.
Vera Toepoel and I have been writing a few articles over the last two years about how survey respondents are taking up tablet computers and smartphones. We were interested in studying whether people in a probability-based web panel ( the LISS panel ) use different devices over time, and whether siwtches in devices for completing surveys are associated with more or less measurement error.
In order to answer this question, we have coded the User Agent Strings of the devices used by more than 6.
Frauke Kreuter once commented on a presentation I gave that I should really be looking at sequence analysis for studying attrition in panel surveys. She had written an article on the topic with Ulrich Kohler ( here ) in 2009, and as of late there are more people exploring the technique (e.g. Mark Hanly at Bristol, and Gabi Durrant at Southampton ).
I am working on a project on attrition in the British Household Panel, and linking attrition errors to measurement errors.
I’m not dead! In fact, I have been very alive over the past half a year: moving, finishing my Ph.D and starting a new job. With all that settled, I am determined to start where I left off.
I often get questions about software to do Structural Equation Modeling. There are quite a few packages out there, some more user-friendly or sophisticated than others. Here is an overview of existing packages and my opinion on the pro’s and cons of each.