I am spending time at the Institute for Social and Economic Research in Colchester, UK where I will work on a research project that investigates whether there is a tradeoff between nonresponse and measurement errors in panel surveys.
Survey methodologists have long believed that multiple survey errors have a common cause. For example, when a respondent is less motivated, this may result in nonresponse (in a panel study attrition), or in reduced cognitive effort during the interview, which in turn leads to measurement errors.
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.
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.
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.