Dear IM Colleagues,
Organizational phenomena are typically multilevel in nature. For example, research in OB and HR involves employees who are nested within jobs and teams, research in entrepreneurship involves entrepreneurs nested within ventures, research in strategy involves firms nested within industries, and research in international business involves countries nested within regions. In addition, all longitudinal research involves a multilevel data structure because data are collected over time for employees, entrepreneurs, teams, new ventures, or firms in general. Data that are nested, even if we don't acknowledge the presence of nesting, lead to misleading conclusions if we use typical single-level data-analytic techniques such as OLS regression that assume independence of observations. This is why multilevel analysis has become so popular in recent years.
In spite of the recent popularity of multilevel research and analysis, there are fundamental questions that researchers ask in many domains: Really... Do I HAVE to use multilevel modeling? In other words, is the nature of my data such that there is dependence and if I use traditional techniques my results and conclusions will be wrong? Should I invest time and effort in using multilevel modeling or could it be that single-level approaches such as OLS regression will be sufficient and appropriate-in my particular study?
We invite you to attend our PDW at the AOM meetings in Anaheim titled SHOULD I USE MULTILEVEL MODELING? A HANDS-ON DEMONSTRATION addressing the aforementioned questions. Based on research recently published in Organizational Research Methods, Journal of Management, Strategic Organization, Journal of Applied Psychology, and other journals, we will offer a decision-making process and online programs to understand whether there is sufficient dependence in a dataset that would warrant the use of multilevel modeling rather than single-level approaches. We will offer hands-on demonstrations with datasets and also encourage PDW participants to bring their own datasets to be able to conduct these computations real-time during the workshop.
SHOULD I USE MULTILEVEL MODELING? A HANDS-ON DEMONSTRATION
Saturday, August 6th, 8am-10am, Anaheim Convention Center, 207A
Presenters: Steven A. Culpepper (U. of Illinois) and Herman Aguinis (George Washington U.)
We look forward to seeing you in Anaheim!
All the best,
--Herman.
Some Relevant Sources [available at http://www.hermanaguinis.com/pubs.html]
· Aguinis, H., & Culpepper, S.A. 2015. An expanded decision making procedure for examining cross-level interaction effects with multilevel modeling. Organizational Research Methods, 18: 155-176.
· Aguinis, H, & Molina-Azorin, J. F. 2015. Using multilevel modeling and mixed methods to make theoretical progress in microfoundations for strategy research. Strategic Organization, 13: 353-364.
· Aguinis, H., Gottfredson, R. K., & Culpepper, S. A. 2013. Best-practice recommendations for estimating cross-level interaction effects using multilevel modeling. Journal of Management, 39: 1490-1528.
· Mathieu, J. E., Aguinis, H., Culpepper, S. A., & Chen. G. 2012. Understanding and estimating the power to detect cross-level interaction effects in multilevel modeling. Journal of Applied Psychology, 97: 951-966.
· Aguinis, H., Pierce, C. A., Bosco, F. A., & Muslin, I. S. 2009. First decade of Organizational Research Methods: Trends in design, measurement, and data-analysis topics. Organizational Research Methods, 12: 69-112.
Herman Aguinis, Ph.D.
Avram Tucker Distinguished Scholar and Professor of Management
George Washington University School of Business
2201 G Street, NW
Washington, DC 20052
http://hermanaguinis.com/