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.
Some colleagues in the United Kingdom have started a half-year initiative to discuss the possibilities of conducting web surveys among the general population. Their website can be found here One aspect of their discussions focused on whether any web survey among the population should be complemented with another, secondary survey mode. This would for example enable those without Internet access to participate. Obviously, this means mixing survey modes.
I recently gave a talk at an internal seminar on planned missingness for a group of developmental psychologists. The idea behind planned missingness is that you can shorten interview time or reduce costs, if you decide as a researcher not to administer all your instruments to everyone in your sample. When you either randomly assign people to receive a particular instrument, or do so by design (i.e. only collect bio-markers in an at-risk group), your missing data will either be Missing Completely At Random (MCAR) or Missing at Random (MAR).
In August, I organised a three-week summerschool with my colleagues in Utrecht on new features in MPLUS 7, the software we use to build Structural Equation Models. Video’s of all lectures can be found here. The latest issue of Psychological Methods contains background reading of Bayesian SEM, which is the main new feature of SEM. I find this fascinating stuff, and can think of hundreds of articles that could be written about replications of Maximum Likelihood based approaches.
Sorry for the long silence: have been caught up in work and other things that were always more pressing than writing blog posts. Perhaps it is also because I found it hard to write about statistical modeling. Statistical models are usually complex, and therefore it is difficult to write about them in an accessible way.
Statistical models are everywhere; their goal is to summarize our world in such a way as to capture the essence, and leave out the irrelevant complexities.
One of the professors at the department where I work ( Joop Hox ) told me at our first meeting ever that good survey methodologists know their way around in the world of statistics. I think this saying should also go in the reverse order by the way, but I did take his advice seriously, and I am getting more and more interested in statistics, and specifically statistical modeling.
A good statistical model in my view should be able to answer a specific (complicated) research questions about our social world, in a relatively straightforward way.