{"id":{"repo_id":"unr","oai_identifier":"oai:scholarwolf.unr.edu:11714/2493"},"canonical_url":"https://search.dev.ndltd.org/etd/unr/oai:scholarwolf.unr.edu:11714/2493","repository":{"repo_id":"unr","name":"University of Nevada - Reno","base_url":"https://scholarwolf.unr.edu/server/oai/request"},"display":{"title":"Picture Perfect: Predicting the Model Ex-Offender","abstract":"The cost of imprisonment remains high. Budgetary constraints faced by most states have resulted in an increased interest in reducing prison populations while simultaneously reducing recidivism rates. The latter goal has encouraged a renewed interest in offender reentry and reintegration. There are a multitude of reasons why offenders find themselves back in the criminal justice system; however, by identifying these issues, appropriate measures can be taken to reduce recidivism. This research uses data from Project Pride, a Nevada job readiness program, to examine the effect individual-level factors have on an offender's likelihood to successfully reintegrate. By using logistical regression, this analysis attempts to determine which factors are most likely to predict a person will remain in the community and out of prison. The ability to determine who will most and least likely succeed upon reentry can help guide effective correctional practices in Nevada.","abstract_html":"The cost of imprisonment remains high. Budgetary constraints faced by most states have resulted in an increased interest in reducing prison populations while simultaneously reducing recidivism rates. The latter goal has encouraged a renewed interest in offender reentry and reintegration. There are a multitude of reasons why offenders find themselves back in the criminal justice system; however, by identifying these issues, appropriate measures can be taken to reduce recidivism. This research uses data from Project Pride, a Nevada job readiness program, to examine the effect individual-level factors have on an offender's likelihood to successfully reintegrate. By using logistical regression, this analysis attempts to determine which factors are most likely to predict a person will remain in the community and out of prison. The ability to determine who will most and least likely succeed upon reentry can help guide effective correctional practices in Nevada.","abstract_has_math":false,"creators":["Saldana, Sara Noel"],"institution":null,"degree_name":null,"degree_level":"Master's Degree","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Leone, Matthew C."],"committee_chairs":[],"committee_members":["Griffin, Timothy","D'Andrea, Livia"],"year":2015,"date_issued":"2015","date_published":"2015","updated_at":"2026-07-27T21:46:08Z","subjects":[],"languages":[],"rights":["In Copyright(All Rights Reserved)"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11714/2493","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Leone, Matthew C."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Griffin, Timothy","D'Andrea, Livia"]},{"key":"dc:creator","label":"Author","values":["Saldana, Sara Noel"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-01-08T17:35:39Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-01-08T17:35:39Z"]},{"key":"dc:date.issued","label":"Date","values":["2015"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's Degree"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright(All Rights Reserved)"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11714/2493"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The cost of imprisonment remains high. Budgetary constraints faced by most states have resulted in an increased interest in reducing prison populations while simultaneously reducing recidivism rates. The latter goal has encouraged a renewed interest in offender reentry and reintegration. There are a multitude of reasons why offenders find themselves back in the criminal justice system; however, by identifying these issues, appropriate measures can be taken to reduce recidivism. This research uses data from Project Pride, a Nevada job readiness program, to examine the effect individual-level factors have on an offender's likelihood to successfully reintegrate. By using logistical regression, this analysis attempts to determine which factors are most likely to predict a person will remain in the community and out of prison. The ability to determine who will most and least likely succeed upon reentry can help guide effective correctional practices in Nevada."]},{"key":"dc:format","label":"Dc Format","values":["PDF"]},{"key":"dc:title","label":"Title","values":["Picture Perfect: Predicting the Model Ex-Offender"]}]}],"canonical_facts":{"dc:contributor.advisor":["Leone, Matthew C."],"dc:contributor.committeemember":["Griffin, Timothy","D'Andrea, Livia"],"dc:creator":["Saldana, Sara Noel"],"dc:date.accessioned":["2018-01-08T17:35:39Z"],"dc:date.available":["2018-01-08T17:35:39Z"],"dc:date.issued":["2015"],"dc:description.abstract":["The cost of imprisonment remains high. Budgetary constraints faced by most states have resulted in an increased interest in reducing prison populations while simultaneously reducing recidivism rates. The latter goal has encouraged a renewed interest in offender reentry and reintegration. There are a multitude of reasons why offenders find themselves back in the criminal justice system; however, by identifying these issues, appropriate measures can be taken to reduce recidivism. This research uses data from Project Pride, a Nevada job readiness program, to examine the effect individual-level factors have on an offender's likelihood to successfully reintegrate. By using logistical regression, this analysis attempts to determine which factors are most likely to predict a person will remain in the community and out of prison. The ability to determine who will most and least likely succeed upon reentry can help guide effective correctional practices in Nevada."],"dc:format":["PDF"],"dc:identifier.uri":["http://hdl.handle.net/11714/2493"],"dc:rights":["In Copyright(All Rights Reserved)"],"dc:title":["Picture Perfect: Predicting the Model Ex-Offender"],"dc:type":["Thesis"],"thesis:degree_level":["Master's Degree"]},"updated_at":"2026-07-27T21:46:08Z"}