title: Bayesian and maximum likelihood estimation of hierarchical response time models.
abstract: 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.
authors.affiliation: University of Bristol, Bristol, England. simon.farrell@bristol.ac.uk
authors.forename: Simon
authors.initials: S
authors.lastname: Farrell
authors.forename: Casimir J H
authors.initials: CJ
authors.lastname: Ludwig
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D016013:Likelihood Functions; D016011:Normal Distribution; D011336:Probability; D011594:Psychometrics / Q000706:statistics & numerical data*; D011930:Reaction Time