Bayesian and maximum likelihood estimation of hierarchical response time models

SA Farrell, CJH Ludwig

Research output: Contribution to journalArticle (Academic Journal)peer-review

48 Citations (Scopus)

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.
Translated title of the contributionBayesian and maximum likelihood estimation of hierarchical response time models
Original languageEnglish
Pages (from-to)1209 - 1217
Number of pages9
JournalPsychonomic Bulletin and Review
Volume15
DOIs
Publication statusPublished - Dec 2008

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