About this resource

    Type of resources
    Reading
    Education level(s)
    College / Upper Division (Undergraduates), Graduate / Professional
    Primary user(s)
    Student, Teacher, Researcher / Scientist
    Subject area(s)
    Education, Math & Statistics, Social Science
    Language(s)
    English
    Cluster
    Ways of Working, Replication Crisis and Credibility Revolution
    Last updated
    29 October 2026
    Reading

    Estimating the Unobserved: Hierarchical Bayesian Estimation of Evidence Accumulation Models with Missing or Contaminant Data

    By Jeroen E. Timmerman, Andrew J. Heathcote

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    Level College / Upper Division (Undergraduates), Graduate / Professional · For Student, Teacher, Researcher / Scientist · Language English

    Abstract

    This student project involved creating likelihood functions for the Bayesian estimation of cognitive parameters behind choices and response times in R, for the R package EMC2 of the Amsterdam Mathematical Psychology Lab. Particularly, the likelihood functions allow for good parameter recovery when data is missing (censoring and truncation), and when there are contaminant responses in the data. The improvement of censoring (taking the number of missing response times or choices into account) over truncation (discarding missing response times and choices) is tested in a fully reproducible simulation study.

    Keywords OSCA Awards 2025