Hierarchical (or multilevel) statistical models have become increasingly popular in psychology in the last few years. In this article, we consider the application of multilevel modeling to the ex-Gaussian, a popular model of response times. We compare single-level and hierarchical methods for estimation of the parameters of ex-Gaussian distributions. In addition, for each approach, we compare maximum likelihood estimation with Bayesian estimation. A set of simulations and analyses of parameter recovery show that although all methods perform adequately well, hierarchical methods are better able to recover the parameters of the ex-Gaussian, by reducing variability in the recovered parameters. At each level, little overall difference was observed between the maximum likelihood and Bayesian methods.
|Translated title of the contribution||Bayesian and maximum likelihood estimation of hierarchical response time models|
|Pages (from-to)||1209 - 1217|
|Number of pages||9|
|Journal||Psychonomic Bulletin and Review|
|Publication status||Published - Dec 2008|