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BEGIN:VEVENT
DTSTAMP:20260428T075019Z
DTSTART;VALUE=DATE-TIME:20190603T130000
DTEND;VALUE=DATE-TIME:20190603T140000
SUMMARY:Henrik Singmann (ÌÇÐÄTV Psychology)
TZID:Europe/London
UID:20190603-8a1785d86a11d455016a510d2d961ba3@warwick.ac.uk
CREATED:20190424T204727Z
DESCRIPTION:A Bayesian and Frequentist Multiverse Pipeline for Multinomia
 l Processing Tree Models – Applications to Recognition Memory Authors: H
 enrik Singmann (University of ÌÇÐÄTV)\, Daniel W. Heck (Universität Man
 nheim)\, Marius Barth (Universität zu Köln)\, Julia Groß (Heinrich-Heine
 -Universität Düsseldorf)\, Beatrice G. Kuhlmann (Universität Mannheim) A
 bstract: Even with a clear hypothesis or cognitive model in mind\, most 
 statistical analyses contain several more or less arbitrary choices. In 
 the case of a model-based analysis\, these choices can concern the stati
 stical framework\, the aggregation-level\, and which parameter restricti
 ons to introduce. Usually one path through this ‘garden of forking paths
 ’ (Gelman & Loken\, 2013) is chosen and reported. However\, it is unclea
 r how much each choice affects the reported results. The multiverse appr
 oach (Steegen\, Tuerlinckx\, Gelman\, & Vanpaemel\, 2016) offers a princ
 ipled alternative in which results for all possible combinations of reas
 onable modeling choices are reported. We developed a software package fo
 r R that performs a model-based multiverse analysis for multinomial proc
 essing tree (MPT) models\, MPTmultiverse. Our package estimates MPT mode
 ls in a frequentist and Bayesian manner. In the frequentist case\, it us
 es no pooling (with and without bootstrap) and complete pooling. In the 
 Bayesian case\, it uses no pooling\, complete pooling\, and three differ
 ent variants of partial pooling. We applied our approach to a large conf
 idence-rating recognition memory data corpus consisting of 12 studies wi
 th over 450 participants using a relatively unrestricted variant of the 
 2-high threshold model for confidence ratings (Bröder\, Kellen\, Schütz\
 , & Rohrmeier\, 2013). Our results show that even for some core paramete
 rs\, the different analysis approaches reveal considerable variability i
 n the parameter estimates across estimation methods. Our results suggest
  that researchers should adopt a multiverse approach when using cognitiv
 e models.
LOCATION:D2.02 Engineering
CATEGORIES:
LAST-MODIFIED:20190424T204727Z
ORGANIZER;CN=Peter Brommer:
END:VEVENT
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