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dc.contributor.advisor Babcock, Julia
dc.creator Cooper, Jason
dc.date.accessioned 2012-09-28T12:49:10Z
dc.date.available 2012-09-28T12:49:10Z
dc.date.created 2012-08
dc.date.issued 2012-09-28
dc.date.submitted August 2012
dc.identifier.uri http://hdl.handle.net/10657/ETD-UH-2012-08-489
dc.description.abstract In 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.mimetype application/pdf
dc.language.iso eng
dc.subject Neural Network
dc.subject Intimate Partner Violence
dc.subject Domestic Violence
dc.title TESTING THE UTILITY OF NEURAL NETWORK MODELS TO PREDICT
dc.date.updated 2012-09-28T12:49:12Z
dc.identifier.slug 10657/ETD-UH-2012-08-489
dc.type.material text *
dc.type.genre thesis *
thesis.degree.name Psychology - Clinical
thesis.degree.level Doctoral
thesis.degree.discipline Clinical Psychology
thesis.degree.grantor University of Houston
thesis.degree.department Psychology
dc.contributor.committeeMember Fox, Daniel
dc.contributor.committeeMember Tian, Siva
dc.contributor.committeeMember Fox, David

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