Applying Mixed-Effects Modeling to Behavioral Economic Demand: An Introduction.
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.


Supplementary Information
The online version contains supplementary material available at 10.1007/s40614-021-00299-7.
['Kaplan|Brent A|BA|0000-0002-3758-6776', 'Franck|Christopher T|CT|', 'McKee|Kevin|K|https://orcid.org/0000-0003-0803-9826', 'Gilroy|Shawn P|SP|https://orcid.org/0000-0002-1097-8366', 'Koffarnus|Mikhail N|MN|https://orcid.org/0000-0002-7923-7734']
mixed-effects model behavioral economics demand R programming language behavioral science multilevel model operant purchase task