Consortium for the Advancement of Research Methods & Analysis
Topic Area Workshop
Friday, October 17 | 10:30 AM – Noon ET
Macro Topic Area:
Panel Data Analysis Concepts
This workshop focuses on the concepts related to analyzing panel data (e.g. multiple, repeated observations on an entity over time). We will cover differences between econometric random effects and fixed effects models, including when their use is appropriate and their theoretical meaning. We will also explore specifications across multiple types of models and the use of the hybrid model. Finally, we will conclude with discussions around when the dependence in the data (such as time effects) may be a nuisance to be controlled for versus a variable with explanatory power.
Dr. DJ Schepker
Biography
Donald J. “DJ” Schepker is an Associate Professor of Strategic Management and Moore Distinguished Fellow in the Darla Moore School of Business at the University of South Carolina, where he also serves as the Research Director in the Center for Executive Succession. His research focuses on executive succession planning, the causes and consequences of executive succession, corporate governance, board decision making, and dynamics between executives and boards. His research has appeared in outlets such as the Academy of Management Journal, Strategic Management Journal, Journal of Applied Psychology, Journal of Management, and Journal of Management Studies. Dr. Schepker currently serves as an Associate Editor for The Leadership Quarterly. He received his Ph.D. in Strategic Management from the University of Kansas and B.S. from Babson College.
Micro Topic Area:
Computational Modeling
This brief topic workshop introduces participants to computational process models. Historically, organizational science has relied on narrative construct theories, construct measurement, and associated statistical construct models (e.g., structural equations modeling) to advance knowledge. Yet, the explanatory accounts, predictive capabilities, and interventions afforded by construct research remain one step removed from processes in operation and two steps removed from their generative mechanisms. The study of processes involves theorizing and modeling the mechanisms by which entities (e.g., individuals, teams, organizations) enact sequences of actions responsible for phenomena of interest (e.g., group differences, multivariate relations, longitudinal patterns, teams outperforming their talent, organizational gender stratification, etc.). To garner process thinking, we may develop computational process models, which generally facilitate the systematic study of processes by explicitly representing and simulating entities and their actions and mechanisms. This workshop walks participants through the fundamental ideas behind developing computational process models.
Biography
Professor Goran Kuljanin serves as an Associate Professor in the Department of Management and Entrepreneurship in the Driehaus College of Business at DePaul University. He primarily teaches courses on business and people analytics. His courses cover topics such as data visualization, analytical notebooks, unsupervised and supervised learning, natural language processing, web scraping, time series forecasting, business process analytics, and organizational network analysis.
Professor Kuljanin’s research focuses on developing computational process models of human functioning and workplace operations to enable process-oriented, strategic decision-making on human resources management. He has published his research in the Journal of Applied Psychology, Leadership Quarterly, Organizational Research Methods, and Psychological Methods. His research awards include Best Article in Organizational Research Methods in 2013, the 2015 William A. Owens Scholarly Achievement Award from the Society of Industrial and Organizational Psychology, and a monograph distinction from the Journal of Applied Psychology. As a co-investigator, he has won grants from the U.S. Army Research Institute for the Behavioral and Social Sciences.
Professor Kuljanin’s consulting work focuses on analyzing data on any organization’s most important resource: its people. Specifically, his consulting work involves applying advanced analytics (e.g., machine learning, dynamical modeling, organizational network analysis, natural language processing, computational process modeling) on employee, team, organizational, and client data to develop effective work strategies to meet organizational objectives.
Dr. Goran Kuljanin
