ESTIMATING THE Q-DIFFUSION MODEL PARAMETERS BY APPROXIMATE BAYESIAN COMPUTATION

dc.contributor.advisorLiu, Yangen_US
dc.contributor.authorTian, Chenen_US
dc.contributor.departmentHuman Developmenten_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2023-10-06T05:40:34Z
dc.date.available2023-10-06T05:40:34Z
dc.date.issued2023en_US
dc.description.abstractThe Q-diffusion model is a cognitive process model that considers decision making as an unobservable information accumulation process. Both item and person parameters decide the trace line of the cognitive process, which further decides observed response and response time. Because the likelihood function for the Q-diffusion model is intractable, standard parameter estimation techniques such as the maximum likelihood estimation is difficult to apply. This project applies Approximate Bayesian Computation (ABC) to estimate parameters of the Q-diffusion model. Different from standard Markov chain Monte Carlo samplers that require pointwise evaluation of the likelihood function, ABC builds upon a program for data generation and a metric on the data space to gauge the similarity between imputed and observed data. This project aims to compare the performance of two criteria for gauging the similarity or distance. The limited-information criterion measures the distance in suitable summary statistics (i.e., variances, covariances, and means) between imputed and observed data. The enhanced limited information criterion additionally considers the dependencies among persons’ responses and response times. Bias, rooted mean squared error, and coverage of credible intervals were reported. Results show that when using posterior median as the point estimate, by jointly considering a person’s responses and response time, the enhanced criterion yielded less biased estimation on population scale of person power and slightly better item parameters. This SMC-ABC algorithm informs researchers about key data features that should be captured when determining the stopping rule for the algorithm.en_US
dc.identifierhttps://doi.org/10.13016/dspace/ugle-cqcd
dc.identifier.urihttp://hdl.handle.net/1903/30755
dc.language.isoenen_US
dc.subject.pqcontrolledEducational tests & measurementsen_US
dc.subject.pqcontrolledQuantitative psychologyen_US
dc.subject.pqcontrolledStatisticsen_US
dc.subject.pquncontrolledApproximate Bayesian Computationen_US
dc.subject.pquncontrolledProcess modelen_US
dc.subject.pquncontrolledQ-diffusion modelen_US
dc.subject.pquncontrolledSequential Monte Carloen_US
dc.titleESTIMATING THE Q-DIFFUSION MODEL PARAMETERS BY APPROXIMATE BAYESIAN COMPUTATIONen_US
dc.typeDissertationen_US

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