๐บ๐ธA Bayesian network analysis of the Federal Employee Viewpoint Survey (FEVS) for the U.S. EPA
John F. Carriger, U.S. Environmental Protection Agency
Presented at the 7th Annual BayesiaLab Conference at the North Carolina Biotechnology Center.
Abstract
Bayesian networks are useful for generating insights from survey data on workforce satisfaction and beyond. Employee viewpoint survey interpretations may be supported by data-driven probabilistic graphical support tools. The capabilities for a Bayesian network survey analysis are demonstrated and explored through an initial analysis of the 2018 Federal Employee Viewpoint Survey (FEVS) response data from personnel at the U.S. Environmental Protection Agency (EPA). The FEVS is a voluntarily-taken survey that has been administered annually to federal employees across the U.S. since 2002. A focused analysis of EPA data was conducted to examine the insights from applying Bayesian networks. First, EPA data were isolated from the rest of the federal employee responses. Three partitions of the EPA survey response data were further made for separate analyses: all data from the EPA, data from only the Office of Research and Development personnel, and data from all personnel except from the Office of Research and Development. Core survey questions were used for this analysis that comprised questions related to viewpoints on workplace experiences, supervision, and employee satisfaction. Demographics and work/life balance questions were not included in this analysis. Each of the three partitions of responses was separately analyzed with Bayesian networks and then compared. An exploratory analysis was first conducted to examine the importance of each variable from contribution to the joint probability of a tree-based network. Node force statistics provided quantitative measures for the centrality of the response question in the model, and the visual relationships and arc force measures were used to examine associations. Next, supervised learning was conducted to examine the relationships between the core questions and responses to a target question. The resulting model was used with dynamic profile and target optimization tree methods to develop a priority order and pathway proposals for maximizing a positive response to the target question. Additional approaches for generating insights with the survey data, including clustering of survey questions, were also examined but not fully implemented in this exploratory analysis. Advances in Bayesian network methods for handling large and complex data sets from surveys can allow for clear insights from multivariate survey data and clarification of potential pathways for optimization under uncertainty.
EPA Disclaimer: The views expressed in this presentation are those of the authors and do not necessarily represent the views or policies of the U.S. Environmental Protection Agency.
Authors
John F. Carriger
U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, Land and Materials Management Division, Life Cycle and Decision Support Branch, Cincinnati, OH USA
Carolyn Acheson
U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, Land and Materials Management Division, Remediation and Technology Evaluation Branch, Cincinnati, OH USA
Ronald Herrmann
U.S. Environmental Protection Agency, Office of Research and Development, National Risk Management Research Laboratory, Water Systems Division, Water Resources Recovery Branch, Cincinnati, OH USA
About the Presenter
John Carriger is a research scientist at the U.S. Environmental Protection Agencyโs Office of Research and Development in Cincinnati, Ohio. John has a marine science Ph.D. from the College of William and Mary. Johnโs research interests include applying risk assessment, decision analysis, and weight of evidence tools to environmental problems.
Previous Conference Presentations
๐บ๐ธpageA Probabilistic Community Analysis for Coral Reef Ecosystems in Puerto Rico๐บ๐ธpageAssessing Coral Reef Condition Indicators for Local and Global Stressors Using Bayesian NetworksLast updated
