Tutorial Papers

x-rayMy current academic career is actually my second career. Before studying psychology I studied medical imaging techniques and worked for a couple of years in a hospital. I worked mainly at the emergency room where I was responsible for making X-rays. Maybe it is because of this background that I really enjoy helping other researchers with their data analyses. I also like it very much to write (and publish) tutorial papers in which I challenge researchers to use state-of-the-art techniques instead of their usual techniques. Below I briefly describe the tutorials I have published over the years. If you need any additional information (like syntax files) just drop me a message.

Bayesian statistics

A very easy paper to start reading about Bayesian statistics is my paper together with Sarah Depaoli: Bayesian analyses: Where to start and what to report. A next step could be the gentle introduction with applications to research in Child Development together with, among other co-authors, David Kaplan. In this paper we also re-analyzed four datasets and used posterior results from a previous dataset as prior input for the next dataset. This way, we updated our confidence in the model to obtain more certainty in the end. Since applying Bayesian estimation to real life data is not so easy Sarah Depaoli and me published a 10-point checklist in Psychological Methods which can be really useful when applying Bayes’ yourself, if you are supervising someone who applies Bayesian estimation, or when reviewing a Bayesian paper. One of the steps is conducting a sensitivity analysis, which is explained in more details in a paper where we showed that Bayesian analyses is really helpful with small datasets and in a paper applying Bayesian latent growth mixture modeling to data on PTSD (under review).

history

Measurement Invariance

Testing for measurement invariance (MI) for latent MIvariables is an essential assumption to test when comparing multiple groups (e.g., countries) or following individuals over time. Together with Joop Hox and Peter Lugtig we published a simple to use checklist.  An alternative to establishing strict or strong MI is to apply approximate measurement invariance which is introduced in a paper together with Bengt Muthén (and others). Applications of this method and other state-of-of-the-art MI techniques are presented in our special issue on MI.

Missing data handling

Often researchers have missing data, but what to do? We introduced and compared several methods to deal with missing data and attrition in longitudinal studies.

Multilevel Models

Nested data (analyzed with multilevel modeling) sometimes involves non-normally distrusted variables and as Joop Hox and I argue robust methods need to be used.  Moreover, often the sample size at higher levels is limited and alternative (i.e., Bayesian estimation methods) need to be used to obtain reliable results.

 multilevel

 

Informative Hypotheses

A full description of this topic can be found on my page devoted entirely to this topic or on the general informative hypothesis website, but this is a bullet-wise overview of all tutorials I have published (together with many others) on this topic:

 

 

 

Pictures obtained from (in order of appearance):
1. https://nl.pinterest.com/carbonrider/x-ray-images/
2. https://trustmeimastatistician.wordpress.com/2013/03/22/yet-another-history-of-life-as-we-know-it/
3. van de Schoot, R., Schmidt, P., De Beuckelaer, A., eds. (2015). Measurement Invariance. Lausanne: Frontiers Media. doi: 10.3389/978-2-88919-650-0
4. http://www.joophox.net

 

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