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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/931269

Title
Designing state-trace experiments to assess the number of latent psychological variables underlying binary choices
Author/Creator
Hawkins, Guy;  Prince, Melissa;  Brown, Scott;  Heathcote, Andrew
Description
State-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.
Relation
32nd Annual Conference of the Cognitive Science Society (COGSCI 2010). Cognition in Flux: Proceedings of the 32nd Annual Meeting of the Cognitive Science Society (Portland, OR 11-14 August, 2010) p. 2224-2229
Relation
http://palm.mindmodeling.org/cogsci2010/papers/0521/index.html
Date
2010
Publisher
Cognitive Science Society
Keyword(s)
state-trace analysis;  Bayesian analysis;  Bayes Factor;  encompassing prior method;  simulation
Resource Type
conference paper
Identifier
http://hdl.handle.net/1959.13/931269
Language
eng
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Reviewed
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"Designing state-trace experiments to assess the number of latent psychological variables underlying binary choices"
 
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