{"id":{"repo_id":"gmu","oai_identifier":"oai:MARS:1920/14105"},"canonical_url":"https://search.dev.ndltd.org/etd/gmu/oai:MARS:1920/14105","repository":{"repo_id":"gmu","name":"George Mason University","base_url":"https://mars.gmu.edu/server/oai/request"},"display":{"title":"Does Workplace Climate Predict Employee Retention? Insight from the Federal Employee Viewpoint Survey","abstract":"In this thesis, we investigate the relationship between employee retention and workplace climate. Our investigation is based on the Federal Employee Viewpoint Survey (FEVS) conducted annually by the U.S Office of Personnel Management. We arrange employees into cohorts and summarize their responses using three methods from the literature on big data and high-dimensional statistics: Manhattan Plots, Elastic-Net with Stability Selection, and Principal Component Analysis. We find that employee retention is best explained by how respondents answer three questions: “I know what is expected of me on the job,” “When needed I am willing to put in the extra effort to get a job done,” and “My workload is reasonable.” However, respondent answers are highly correlated, with the first principal component explaining nearly two-thirds of the overall variation. Moreover, this variable explains a significant portion of employee retention.","abstract_html":"In this thesis, we investigate the relationship between employee retention and workplace climate. Our investigation is based on the Federal Employee Viewpoint Survey (FEVS) conducted annually by the U.S Office of Personnel Management. We arrange employees into cohorts and summarize their responses using three methods from the literature on big data and high-dimensional statistics: Manhattan Plots, Elastic-Net with Stability Selection, and Principal Component Analysis. We find that employee retention is best explained by how respondents answer three questions: “I know what is expected of me on the job,” “When needed I am willing to put in the extra effort to get a job done,” and “My workload is reasonable.” However, respondent answers are highly correlated, with the first principal component explaining nearly two-thirds of the overall variation. Moreover, this variable explains a significant portion of employee retention.","abstract_has_math":false,"creators":["Schoenberger, Alayna"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-12-01","date_published":"2023-12-01","updated_at":"2026-07-27T19:51:44Z","subjects":["Employee Surveys","Stability Selection","Manhattan Plot","Principal Component Analysis","Elastic-Net","Regression"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:1920/14105"],"render_values":[{"text":"hdl:1920/14105","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2023-12-01"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Employee Surveys","Stability Selection","Manhattan Plot","Principal Component Analysis","Elastic-Net","Regression"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:1920/14105"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["In this thesis, we investigate the relationship between employee retention and workplace climate. Our investigation is based on the Federal Employee Viewpoint Survey (FEVS) conducted annually by the U.S Office of Personnel Management. We arrange employees into cohorts and summarize their responses using three methods from the literature on big data and high-dimensional statistics: Manhattan Plots, Elastic-Net with Stability Selection, and Principal Component Analysis. We find that employee retention is best explained by how respondents answer three questions: “I know what is expected of me on the job,” “When needed I am willing to put in the extra effort to get a job done,” and “My workload is reasonable.” However, respondent answers are highly correlated, with the first principal component explaining nearly two-thirds of the overall variation. Moreover, this variable explains a significant portion of employee retention."]},{"key":"dc:title","label":"Title","values":["Does Workplace Climate Predict Employee Retention? Insight from the Federal Employee Viewpoint Survey"]}]}],"canonical_facts":{"dc:date.issued":["2023-12-01"],"dc:description.other":["In this thesis, we investigate the relationship between employee retention and workplace climate. Our investigation is based on the Federal Employee Viewpoint Survey (FEVS) conducted annually by the U.S Office of Personnel Management. We arrange employees into cohorts and summarize their responses using three methods from the literature on big data and high-dimensional statistics: Manhattan Plots, Elastic-Net with Stability Selection, and Principal Component Analysis. We find that employee retention is best explained by how respondents answer three questions: “I know what is expected of me on the job,” “When needed I am willing to put in the extra effort to get a job done,” and “My workload is reasonable.” However, respondent answers are highly correlated, with the first principal component explaining nearly two-thirds of the overall variation. Moreover, this variable explains a significant portion of employee retention."],"dc:identifier":["hdl:1920/14105"],"dc:subject":["Employee Surveys","Stability Selection","Manhattan Plot","Principal Component Analysis","Elastic-Net","Regression"],"dc:title":["Does Workplace Climate Predict Employee Retention? Insight from the Federal Employee Viewpoint Survey"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T19:51:44Z"}