Behavioral economic demand methodology is increasingly being used in various fields such as substance use and consumer behavior analysis. Traditional analytical techniques to fitting demand data have proven useful yet some of these approaches require preprocessing of data, ignore dependence in the data, and present statistical limitations. We term these approaches “fit to group” and “two stage” with the former interested in group or population level estimates and the latter interested in individual subject estimates. As an extension to these regression techniques, mixed-effect (or multilevel) modeling can serve as an improvement over these traditional methods. Notable benefits include providing simultaneous group (i.e., population) level estimates (with more accurate standard errors) and individual level predictions while accommodating the inclusion of “nonsystematic” response sets and covariates. These models can also accommodate complex experimental designs including repeated measures. The goal of this article is to introduce and provide a high-level overview of mixed-effects modeling techniques applied to behavioral economic demand data. We compare and contrast results from traditional techniques to that of the mixed-effects models across two datasets differing in species and experimental design. We discuss the relative benefits and drawbacks of these approaches and provide access to statistical code and data to support the analytical replicability of the comparisons.
|Number of pages||26|
|Journal||Perspectives on Behavior Science|
|State||Published - Sep 2021|
Bibliographical noteFunding Information:
Brent A. Kaplan and Mikhail N. Koffarnus's roles were funded by the National Institute on Alcohol Abuse and Alcoholism of the National Institutes of Health under award number R01 AA026605 to MNK. Kevin McKee’s role was funded by the National Center for Advancing Translational Sciences of the National Institutes of Health, Award Number KL2TR003016. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funding source did not have a role in writing this manuscript or in the decision to submit it for publication. All authors had full access to the data in this study and the corresponding author had final responsibility for the decision to submit these data for publication.
© 2021, Association for Behavior Analysis International.
- R programming language
- behavioral economics
- behavioral science
- mixed-effects model
- multilevel model
- purchase task
ASJC Scopus subject areas
- Social Psychology
- Experimental and Cognitive Psychology
- Clinical Psychology