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Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments

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Published in:Personnel Assessment and Decisions
Format: Online Article RSS Article
Published: 2022
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container_title Personnel Assessment and Decisions
description
discipline_display Social Sciences
discipline_facet Social Sciences
format Online Article
RSS Article
genre Journal Article
id rss_article:26187
institution FRELIP
journal_source_facet Personnel Assessment and Decisions
publishDate 2022
publishDateSort 2022
record_format rss_article
spellingShingle Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments
— — — — — — Human Resources
Business & Management
Social Sciences
sub_discipline_display Business & Management
sub_discipline_facet Business & Management
subject_display — — — — — — Human Resources
Business & Management
Social Sciences
— — — — — — Human Resources
Business & Management
Social Sciences
subject_facet — — — — — — Human Resources
Business & Management
Social Sciences
title Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments
title_auth Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments
title_full Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments
title_fullStr Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments
title_full_unstemmed Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments
title_short Comparing Empirically Keyed and Random Forest Scoring Models in Biodata Assessments
title_sort comparing empirically keyed and random forest scoring models in biodata assessments
topic — — — — — — Human Resources
Business & Management
Social Sciences
url https://scholarworks.bgsu.edu/pad/vol8/iss1/7