- Title
- Designing state-trace experiments to assess the number of latent psychological variables underlying binary choices
- Creator
- Hawkins, Guy; Prince, Melissa; Brown, Scott; Heathcote, Andrew
- 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
- Publisher
- Cognitive Science Society
- Resource Type
- conference paper
- Date
- 2010
- 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.
- Subject
- state-trace analysis; Bayesian analysis; Bayes Factor; encompassing prior method; simulation
- Identifier
- http://hdl.handle.net/1959.13/931269
- Identifier
- uon:11035
- Language
- eng
- Full Text
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