{"594443":{"#nid":"594443","#data":{"type":"event","title":"PhD Defense by Elizabeth Williams","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EName: Elizabeth Williams\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EDate:\u003C\/strong\u003E Wednesday, August 23\u003Csup\u003Erd\u003C\/sup\u003E, 2017\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETime:\u003C\/strong\u003E 1:00pm\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ELocation:\u003C\/strong\u003E JS Coon 148\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EAdvisors:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EProfessor James S. Roberts, Ph.D. (Georgia Tech)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003EThesis Committee Members:\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EProfessor Rick Thomas, Ph.D. (Georgia Tech)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EProfessor Susan Embretson, Ph.D. (Georgia Tech)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EProfessor Daniel Spieler, Ph.D. (Georgia Tech)\u003C\/p\u003E\r\n\r\n\u003Cp\u003EProfessor Brian Habing, Ph.D. (University of South Carolina)\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003ETitle : Estimation of Parameters in the Generalized Graded Unfolding Model Using a Genetic Algorithm\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003E\u003Cstrong\u003E\u0026nbsp;\u003C\/strong\u003E\u003C\/p\u003E\r\n\r\n\u003Cp\u003EIn the current study, a genetic algorithm was used in conjunction with the expectation-maximization algorithm to estimate parameters in a polytomous unfolding IRT model known as the generalized graded unfolding model (GGUM). One advantage of using a genetic algorithm for IRT parameter estimation is that this global optimization procedure is not easily affected by local maxima in the likelihood function \u0026ndash; a condition that is often encountered in unfolding IRT models including the GGUM. Additionally, because genetic algorithms do not use derivatives to maximize the likelihood function, it is computationally simple and could be deployed efficiently with higher dimensional data. The focus of this study was to implement the genetic algorithm in the context of the GGUM, and then evaluate the speed and accuracy of the resulting parameter estimates\u0026nbsp;\u0026nbsp; Program development was done with the R computer language, and the efficacy of estimates was examined with simulation methods, which systematically vary sample size, test length and number of response categories.\u0026nbsp; The resulting estimation strategy was also illustrated with real data from an abortion attitude questionnaire.\u0026nbsp;\u003C\/p\u003E\r\n","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":"","field_summary_sentence":[{"value":"Estimation of Parameters in the Generalized Graded Unfolding Model Using a Genetic Algorithm"}],"uid":"27707","created_gmt":"2017-08-15 18:00:43","changed_gmt":"2017-08-15 18:00:43","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2017-08-23T14:00:00-04:00","event_time_end":"2017-08-23T16:00:00-04:00","event_time_end_last":"2017-08-23T16:00:00-04:00","gmt_time_start":"2017-08-23 18:00:00","gmt_time_end":"2017-08-23 20:00:00","gmt_time_end_last":"2017-08-23 20:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"},{"id":"174045","name":"Graduate students"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}