TY - JOUR
T1 - From Nuisance to Novel Research Questions
T2 - Using Multilevel Models to Predict Heterogeneous Variances
AU - Lester, Houston F.
AU - Cullen-Lester, Kristin L.
AU - Walters, Ryan W.
N1 - Funding Information:
Houston F. Lester is an instructor of medicine at the Baylor College of Medicine and an investigator at the Michael E. DeBakey VA Medical Center, Houston, Texas. He received a PhD in Educational Psychology with a specialization in Quantitative, Qualitative, and Psychometric Methods from the University of Nebraska-Lincoln. His research focuses on mixed-effects location-scale models and team psychometrics. His research has been funded by the Agency for Healthcare Research and Quality. Kristin L. Cullen-Lester is an assistant professor at the C. T. Bauer College of Business at the University of Houston. She received a PhD in Industrial-Organizational Psychology from Auburn University. Her research examines relational aspects of leadership, including leaders’ social networks and the role of organizational networks in shared leadership, complex collaboration, and the implementation of strategic and large-scale change. Her research has been funded by the National Science Foundation. Ryan W. Walters is an assistant professor at the Creighton University Department of Medicine. He received a PhD in Quantitative Psychology from the University of Nebraska-Lincoln. His research focuses on multilevel modeling, mixed-effects location-scale models, and applying statistics in medicine. His research has been funded by the Robert Wood Johnson Foundation among other national funding agencies. 1 Baylor College of Medicine, Houston TX, USA 2 University of Houston Bauer College of Business, Houston, TX, USA 3 School of Medicine, Creighton University, Omaha, NE, USA Houston F. Lester, Baylor College of Medicine, 2450 Holcombe Blvd., Suite 01Y, Houston, TX 77021, USA. Email: hfl000151@gmail.com 2019 1094428119887434 © The Author(s) 2019 2019 SAGE Publications Constructs that reflect differences in variability are of interest to many researchers studying workplace phenomena. The aggregation methods typically used to investigate “variability-based” constructs suffer from several limitations, including the inability to include Level 1 predictors and a failure to account for uncertainty in the variability estimates. We demonstrate how mixed-effects location-scale (MELS) and heterogeneous variance models, which are direct extensions of traditional mixed-effects (or multilevel) models, can be used to test mean (location)- and variability (scale)-related hypotheses simultaneously. The aims of this article are to demonstrate (a) how the MELS and heterogeneous variance models can be estimated with both nested cross-sectional and longitudinal data to answer novel research questions about constructs of interest to organizational researchers, (b) how a Bayesian approach allows for the inclusion of random intercepts and slopes when predicting both variability and mean levels, and finally (c) how researchers can use a multilevel approach to predict between-group heterogeneous variances. In doing so, this article highlights the added value of viewing variability as more than a statistical nuisance in organizational research. multilevel models mixed-effects location-scale models heterogeneous variance models variability-related hypotheses Bayesian edited-state corrected-proof Authors’ Note We would like to thank Justin Jones as well as special feature editor Rory Eckardt, the other editors, and anonymous reviewers for the Feature Topic on New Approaches to Multilevel Methods and Statistics for their comments and suggestions throughout the review of this article. Houston F. Lester is also affiliated with Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, Texas. Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was partially funded by the first author’s VA Health Services Research & Development (HSR&D) Postdoctoral Fellowship: HX-17-014. ORCID iD Houston F. Lester https://orcid.org/0000-0003-3961-6219
PY - 2019
Y1 - 2019
N2 - Constructs that reflect differences in variability are of interest to many researchers studying workplace phenomena. The aggregation methods typically used to investigate “variability-based” constructs suffer from several limitations, including the inability to include Level 1 predictors and a failure to account for uncertainty in the variability estimates. We demonstrate how mixed-effects location-scale (MELS) and heterogeneous variance models, which are direct extensions of traditional mixed-effects (or multilevel) models, can be used to test mean (location)- and variability (scale)-related hypotheses simultaneously. The aims of this article are to demonstrate (a) how the MELS and heterogeneous variance models can be estimated with both nested cross-sectional and longitudinal data to answer novel research questions about constructs of interest to organizational researchers, (b) how a Bayesian approach allows for the inclusion of random intercepts and slopes when predicting both variability and mean levels, and finally (c) how researchers can use a multilevel approach to predict between-group heterogeneous variances. In doing so, this article highlights the added value of viewing variability as more than a statistical nuisance in organizational research.
AB - Constructs that reflect differences in variability are of interest to many researchers studying workplace phenomena. The aggregation methods typically used to investigate “variability-based” constructs suffer from several limitations, including the inability to include Level 1 predictors and a failure to account for uncertainty in the variability estimates. We demonstrate how mixed-effects location-scale (MELS) and heterogeneous variance models, which are direct extensions of traditional mixed-effects (or multilevel) models, can be used to test mean (location)- and variability (scale)-related hypotheses simultaneously. The aims of this article are to demonstrate (a) how the MELS and heterogeneous variance models can be estimated with both nested cross-sectional and longitudinal data to answer novel research questions about constructs of interest to organizational researchers, (b) how a Bayesian approach allows for the inclusion of random intercepts and slopes when predicting both variability and mean levels, and finally (c) how researchers can use a multilevel approach to predict between-group heterogeneous variances. In doing so, this article highlights the added value of viewing variability as more than a statistical nuisance in organizational research.
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U2 - 10.1177/1094428119887434
DO - 10.1177/1094428119887434
M3 - Article
AN - SCOPUS:85075372869
JO - Organizational Research Methods
JF - Organizational Research Methods
SN - 1094-4281
ER -