{"id":{"repo_id":"wku-diss","oai_identifier":"oai:digitalcommons.wku.edu:theses-1880"},"canonical_url":"https://search.dev.ndltd.org/etd/wku-diss/oai:digitalcommons.wku.edu:theses-1880","repository":{"repo_id":"wku-diss","name":"Western Kentucky University","base_url":"https://digitalcommons.wku.edu/do/oai/"},"display":{"title":"Using Biodata to Predict Alternative Measures of Training Period Turnover","abstract":"Logistic Regression was utilized to add to what is known about biodata and turnover. Biodata items from 958 former and current employees in a manufacturing environment were used to develop models to predict a) which employees will turnover prior to completion of a ninety-day training period, b) who will leave voluntarily versus involuntarily, and of those who leave voluntarily c) which leavers are functional versus dysfunctional. A significant relationship was found between biodata items and completion of the ninety-day training period. The resulting model indicated that those who completed training were employed at time of hire, had higher aptitude scores, and had a previous address close to the plant. In addition, those who left voluntarily had higher levels of performance than involuntary leavers. However, biodata items did not differentiate between voluntary and involuntary leavers or between functional and dysfunctional leavers.","abstract_html":"Logistic Regression was utilized to add to what is known about biodata and turnover. Biodata items from 958 former and current employees in a manufacturing environment were used to develop models to predict a) which employees will turnover prior to completion of a ninety-day training period, b) who will leave voluntarily versus involuntarily, and of those who leave voluntarily c) which leavers are functional versus dysfunctional. A significant relationship was found between biodata items and completion of the ninety-day training period. The resulting model indicated that those who completed training were employed at time of hire, had higher aptitude scores, and had a previous address close to the plant. In addition, those who left voluntarily had higher levels of performance than involuntary leavers. However, biodata items did not differentiate between voluntary and involuntary leavers or between functional and dysfunctional leavers.","abstract_has_math":false,"creators":["Pankratz, Ronald"],"institution":null,"degree_name":"Master of Arts","degree_level":null,"degree_discipline":"Department of Psychology","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":1996,"date_issued":"1996-12-01T08:00:00Z","date_published":"1996-12-01T08:00:00Z","updated_at":"2026-07-24T06:07:54Z","subjects":["Industrial and Organizational Psychology","Psychology"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.wku.edu/theses/877","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Pankratz, Ronald"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Department of Psychology"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Arts"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Industrial and Organizational Psychology","Psychology"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.wku.edu/theses/877"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Logistic Regression was utilized to add to what is known about biodata and turnover. Biodata items from 958 former and current employees in a manufacturing environment were used to develop models to predict a) which employees will turnover prior to completion of a ninety-day training period, b) who will leave voluntarily versus involuntarily, and of those who leave voluntarily c) which leavers are functional versus dysfunctional. A significant relationship was found between biodata items and completion of the ninety-day training period. The resulting model indicated that those who completed training were employed at time of hire, had higher aptitude scores, and had a previous address close to the plant. In addition, those who left voluntarily had higher levels of performance than involuntary leavers. However, biodata items did not differentiate between voluntary and involuntary leavers or between functional and dysfunctional leavers."]},{"key":"dc:title","label":"Title","values":["Using Biodata to Predict Alternative Measures of Training Period Turnover"]}]}],"canonical_facts":{"dc:creator":["Pankratz, Ronald"],"dc:description.abstract":["Logistic Regression was utilized to add to what is known about biodata and turnover. Biodata items from 958 former and current employees in a manufacturing environment were used to develop models to predict a) which employees will turnover prior to completion of a ninety-day training period, b) who will leave voluntarily versus involuntarily, and of those who leave voluntarily c) which leavers are functional versus dysfunctional. A significant relationship was found between biodata items and completion of the ninety-day training period. The resulting model indicated that those who completed training were employed at time of hire, had higher aptitude scores, and had a previous address close to the plant. In addition, those who left voluntarily had higher levels of performance than involuntary leavers. However, biodata items did not differentiate between voluntary and involuntary leavers or between functional and dysfunctional leavers."],"dc:identifier":["https://digitalcommons.wku.edu/theses/877"],"dc:subject":["Industrial and Organizational Psychology","Psychology"],"dc:title":["Using Biodata to Predict Alternative Measures of Training Period Turnover"],"dc:type":["Thesis"],"thesis:degree_discipline":["Department of Psychology"],"thesis:degree_name":["Master of Arts"]},"updated_at":"2026-07-24T06:07:54Z"}