Measuring the Impact of Status Manipulations Using Monte Carlo Simulations
ISBN: 978-1-78743-193-5, eISBN: 978-1-78743-192-8
Publication date: 12 August 2017
Abstract
Purpose
This paper introduces a method by which researchers can assess the strength of their status manipulations in experimental research by comparing them against Monte Carlo simulated distributions that use aggregate Status Characteristics Theory (SCT) data.
Methodology
This paper uses Monte Carlo methods to simulate the m and q parameter distributions and the proportion of stay (P(s)) score distributions for four commonly used status situations. It also presents findings from an experiment that highlight the processes by which researchers can utilize these simulated distributions in their assessment of novel status manipulations.
Findings
Findings indicate that implicitly relevant status manipulations have considerably more overlapping P(s) scores in the simulated distributions of high and low states of a status characteristic than explicitly relevant status manipulations. Findings also show that a novel status manipulation, the handedness manipulation, sufficiently creates high- and low-status differences in P(s) scores.
Research implications
Future researchers can use these simulated distributions to plot the mean P(s) scores of each of their experimental conditions on the overlapping distribution for the corresponding status manipulation. Manipulations that produce scores that fall outside of the range of overlapping values are also likely to create status differences between conditions in other settings or populations.
Keywords
Acknowledgements
Acknowledgments
This research was supported by a National Science Foundation grant (SES-1029741) and was conducted at the Laboratory for Sociological Research at the University of South Carolina. Thank you to David Melamed, Ashley Harrell, and an anonymous reviewer for improving upon this manuscript with your comments.
Citation
McLeer, J. (2017), "Measuring the Impact of Status Manipulations Using Monte Carlo Simulations", Advances in Group Processes (Advances in Group Processes, Vol. 34), Emerald Publishing Limited, Leeds, pp. 103-128. https://doi.org/10.1108/S0882-614520170000034005
Publisher
:Emerald Publishing Limited
Copyright © 2017 Emerald Publishing Limited