A framework for ML estimation of parameters of (mixtures of) common reaction time distributions given optional truncation or censoring

Conor V. Dolan, Han L.J. Van Der Maas, Peter C.M. Molenaar

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Abstract

We present a framework for distributional reaction time (RT) analysis, based on maximum likelihood (ML) estimation. Given certain information relating to chosen distribution functions, one can estimate the parameters of these distributions and of finite mixtures of these distributions. In addition, left and/or right censoring or truncation may be imposed. Censoring and truncation are useful methods by which to accommodate outlying observations, which are a pervasive problem in RT research. We consider five RT distributions: the Weibull, the ex-Gaussian, the gamma, the log-normal, and the Wald. We employ quasi-Newton optimization to obtain ML estimates. Multicase distributional analyses can be carried out, which enable one to conduct detailed (across or within subjects) comparisons of RT data by means of loglikelihood difference tests. Parameters may be freely estimated, estimated subject to boundary constraints, constrained to be equal (within or over cases), or fixed. To demonstrate the feasibility of ML estimation and to illustrate some of the possibilities offered by the present approach, we present three small simulation studies. In addition, we present three illustrative analyses of real data.

Original languageEnglish (US)
Pages (from-to)304-323
Number of pages20
JournalBehavior Research Methods, Instruments, and Computers
Volume34
Issue number3
DOIs
StatePublished - Aug 2002

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Psychology (miscellaneous)
  • Psychology(all)

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