As a follow up to the message from Andreas below, those of you looking for
an accessible source that provides an overview of Bayesian methods and what
they have to offer may be interested in the following article to be
published in Organizational Research Methods:
Kruschke, J. K., Aguinis, H., & Joo, H. (in press). The time has come:
Bayesian methods for data analysis in the organizational sciences.
Organizational Research Methods.
The article’s Abstract is below. Also, a pre-print of this article is
available at
http://mypage.iu.edu/~haguinis/pubs.html
All the best,
--Herman.
*****************************************************
Herman Aguinis, Ph.D.
Dean's Research Professor and
Professor of Organizational Behavior and Human Resources
Founding Director, Institute for Global Organizational Effectiveness
Department of Management and Entrepreneurship
Kelley School of Business, Indiana University
http://mypage.iu.edu/~haguinis/
****************************************************
The Time Has Come: Bayesian Methods for Data Analysis in the Organizational
Sciences
The use of Bayesian methods for data analysis is creating a revolution in
fields ranging from genetics to marketing. Yet, results of our literature
review including more than 10,000 articles published in 15 journals from
January 2001 and December 2010 indicate that Bayesian approaches are
essentially absent from the organizational sciences. Our article introduces
organizational science researchers to Bayesian methods and describes why
and how they should be used. We use multiple linear regression as the
framework to offer a step-by-step demonstration, including the use of
software, regarding how to implement Bayesian methods. We explain and
illustrate how to determine the prior distribution, how to compute the
posterior distribution, how to possibly accept the null value, and how to
produce a write-up describing the entire Bayesian process including graphs,
results, and their interpretation. We also offer a summary of the
advantages of using Bayesian analysis and examples of how specific
published research based on frequentist analysis-based approaches failed to
benefit from the advantages offered by a Bayesian approach and how using
Bayesian analyses would have led to richer and, in some cases, different
substantive conclusions. We hope that our article will serve as a catalyst
for the adoption of Bayesian methods in organizational science research.
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Original Message:
From: Organization and Management Theory Division Listserv
[mailto:
OMT@AOMLISTS.PACE.EDU] On Behalf Of Schwab, Andreas [MGMT]
Sent: Tuesday, July 24, 2012 9:14 PM
To:
OMT@AOMLISTS.PACE.EDU
Subject: [OMT] PDW Bayesian Research Methods at the AOM Conference in Boston
Just a reminder about the two back-to-back PDWs on Bayesian Methods at the
upcoming Academy of Management Conference in Boston, MA. No pre-
registration is required, and we look forward to having you along to
discuss exciting advents in the area of Bayesian Methods. The details for
the two PDWs are as follows:
PDW #1 Title: Why We All Should Be Bayesians!
Time: Saturday, August 4, 2012 at 10:15 AM – 12:15 PM
Location: Westin Copley, Room: Great Republic
Presenters: David Krackhardt (Carnegie Mellon University), William H.
Starbuck (University of Oregon), Michael J. Zyphur (University of
Melbourne), Andreas Schwab (Iowa State University)
Abstract:
This workshop introduces management researchers to the opportunities of
Bayesian statistics for empirical research in the management sciences. We
will outline the fundamental features of the Bayesian method without
delving into the mathematical details. Instead, we will first outline the
conceptual differences and potential advantages of a Bayesian approach
compared to traditional statistical analyses involving null-hypothesis
significance tests (NHSTs). We will then show examples from empirical
management research that illustrates Bayesian data analysis. Finally, we
will discuss why in spite of strong arguments supporting the use of
Bayesian statistics, the field of management research has been very
reluctant considering Bayesian analysis as an alternative. The purpose of
this workshop is to convince participants of the potential opportunities
Bayesian methods can provide and to encourage organizational researchers to
apply these methods in future research.
PDW #2 Title: Bayesian and Frequentist Research Methods: Theory, History,
Estimation, Application, and Integration
Time: Saturday, August 4, 2012 at 12:45 AM – 2:45 PM
Location: Westin Copley, Room: St. George C & D
Presenters: Michael J. Zyphur (University of Melbourne), Dean Pierides
(University of Melbourne)
Abstract:
This workshop introduces a Bayesian theory of probability for inductive
inference in organization and management science. Currently, a frequentist
theory dominates. The difference between the two theories is that Bayesian
probability references a degree of belief in a proposition or state of
affairs, while frequentist probability references the relative frequency of
an observation or event in an infinite series of observations or events.
The foundations of Bayesian and frequentist probability will be described,
as well as their histories, methods of estimation, targets for application,
and how they can both be used to greatly expand the potential for rigorous
and relevant research. Estimation will be conducted in the popular
statistics program Mplus. Program code, datasets, and interpretations of
results will be incorporated into the workshop, including decision-
theoretic foundations of making inductive inferences using different
theories of probability.