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Designing state-trace experiments to assess the number of latent psychological variables underlying binary choices
conference contribution
posted on 2023-05-23, 09:58 authored by Hawkins, G, Prince, M, Brown, S, Heathcote, AState-trace analysis is a non-parametric method that can identify the number of latent variables (dimensionality) required to explain the effect of two or more experimental factors on performance. Heathcote, Brown & Prince (submitted) recently proposed a Bayes Factor method for estimating the evidence favoring one or more than one latent variable in a state-trace experiment, known as Bayesian Ordinal Analysis of State-Traces (BOAST). We report results from a series of simulations indicating that for larger sample sizes BOAST performs well in identifying dimensionality for single and multiple latent variable models. A method of group analysis convenient for smaller sample sizes is presented with mixed results across experimental designs. We use the simulation results to provide guidance on designing state-trace experiments to maximize the probability of correct classification of dimensionality.
History
Publication title
Cognition in Flux: Proceedings of the 32nd Annual Meeting of the Cognitive Science SocietyEditors
S Ohlsson & R CatramboneISBN
978-0-9768318-6-0Department/School
Tasmanian School of MedicinePublisher
Cognitive Science SocietyPlace of publication
United StatesEvent title
32nd Annual Conference of the Cognitive Science Society (COGSCI 2010)Event Venue
Portland, OregonDate of Event (Start Date)
2010-08-11Date of Event (End Date)
2010-08-14Rights statement
Copyright unknownRepository Status
- Restricted