Dr. Aaron Hill is an Associate Professor in the Management Department of the Warrington College of Business at the University of Florida. His research focuses on strategic leadership and governance, examining what drives strategic leaders like executives and politicians to act as well as the ultimate implications of these individuals for organizational outcomes. Aaron’s research has been published in outlets such as the Academy of Management Journal, Strategic Management Journal, and Journal of Management, among others. Aaron is an active member of professional organizations dedicated to the field of management, including the Academy of Management, Strategic Management Society, and Southern Management Association. His recent published intellectual contributions are as follows:
- Abdurakhmonov, M., Ridge, J., Hill, A., & Loncarich, H. In Press. Strategic Risk and Lobbying: Investigating Lobbying Breadth as Risk Management. Journal of Management.
- Klein, F., Hill, A., Hammond, R., & Stice-Lusvardi, R. 2021. The Gender Equity Gap: A Multistudy Investigation of Within-Job Inequality in Equity-Based Awards. Journal of Applied Psychology
- Abdurakhmonov, M., Ridge, J., & Hill, A. 2021. Unpacking Firm External Dependence: How Government Contract Dependence Affects Firm Investments and Market Performance. Academy of Management Journal.
When investigating relationships of interest empirically, researchers typically must make many decisions (e.g., about sampling frames, variable measurements, model specifications, and analytical approaches). These decisions amount to researcher degrees of freedom that may affect both the resulting estimates of a relationship and the respective inferences drawn. In this webcast, we will discuss multiverse analysis as a tool that can help estimate, illustrate, and investigate the impact of researcher degrees of freedom on estimated relationships. Webinar attendees will gain insight into (1) the overall idea of multiverse analysis, (2) how to run it, and (3) the value the tool offers for judging the robustness and reliability of estimates of interest.