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Applying Mixed-Effects Modeling to Behavioral Economic Demand: An Introduction.
Brent A Kaplan1, Christopher T Franck2, Kevin McKee2
1Department of Family and Community Medicine, University of Kentucky, 2195 Harrodsburg Rd., Suite 125, Lexington, KY 40504 USA.
Perspectives on Behavior Science
|October 11, 2021
Summary
Mixed-effects modeling offers a powerful alternative for analyzing behavioral economic demand data, improving upon traditional methods by handling complex data structures and providing simultaneous group and individual estimates.
Area of Science:
- Behavioral economics
- Quantitative psychology
- Econometrics
Background:
- Behavioral economic demand methodology is widely applied in substance use and consumer behavior.
- Traditional analytical techniques have limitations, including data preprocessing needs and statistical constraints.
Purpose of the Study:
- To introduce mixed-effects modeling for behavioral economic demand data.
- To compare mixed-effects models with traditional methods ('fit to group', 'two stage').
Main Methods:
- Application of mixed-effects (multilevel) modeling to behavioral economic demand data.
- Comparison of results from mixed-effects models and traditional techniques across two datasets.
- Utilized statistical code and data for analytical replicability.
Main Results:
- Mixed-effects models provide simultaneous group and individual level estimates with improved standard errors.
- These models accommodate complex experimental designs, covariates, and nonsystematic response sets.
- Demonstrated benefits over traditional methods in analyzing demand data.
Conclusions:
- Mixed-effects modeling is a superior analytical approach for behavioral economic demand data.
- Offers enhanced statistical power and flexibility compared to traditional techniques.
- Facilitates more accurate and comprehensive analysis of demand elasticity.
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