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dc.contributor.advisorBabcock, Julia
dc.creatorCooper, Jason
dc.date.accessioned2012-09-28T12:49:10Z
dc.date.available2012-09-28T12:49:10Z
dc.date.created2012-08
dc.date.issued2012-09-28
dc.date.submittedAugust 2012
dc.identifier.urihttp://hdl.handle.net/10657/ETD-UH-2012-08-489
dc.description.abstractIn prisons, risk assessments are typically based on retrospective reports of factors known to be correlated with violence recidivism. Previous studies have used linear models that rely on variables that have been linked to past history of intimate partner violence (IPV) based on men’s report only. The current study compares the non-linear neural network model to traditional linear models in predicting a history of arrest for any crime in men who self-report a history of IPV. In addition, models that include men’s report only were compared to models that also include the victim’s report.Theneural network models were found to be superior to the linear models in their predictive power. Models that included victim report were superior to models that did not include victim report. These finding suggest that the prediction of violence recidivism may be enhanced through the use of neural network models and through models that include information gathered from victims.
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.subjectNeural Network
dc.subjectIntimate Partner Violence
dc.subjectDomestic Violence
dc.titleTESTING THE UTILITY OF NEURAL NETWORK MODELS TO PREDICT
dc.date.updated2012-09-28T12:49:12Z
dc.identifier.slug10657/ETD-UH-2012-08-489
dc.type.materialtext*
dc.type.genrethesis*
thesis.degree.namePsychology - Clinical
thesis.degree.levelDoctoral
thesis.degree.disciplineClinical Psychology
thesis.degree.grantorUniversity of Houston
thesis.degree.departmentPsychology
dc.contributor.committeeMemberFox, Daniel
dc.contributor.committeeMemberTian, Siva
dc.contributor.committeeMemberFox, David


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