A Monte Carlo method for comparing generalized estimating equations to conventional statistical techniques for discounting data

• Published in: Journal of the experimental analysis of behavior 2019-03, Vol.111 (2), p.207-224
• Main Authors: Friedel, Jonathan E., DeHart, William B., Foreman, Anne M., Andrew, Michael E.
• Format: Article
• Language: English
• Subjects: Accidents, Traffic - statistics & numerical data; Analysis of Variance; Data Interpretation, Statistical; Datasets; Decision Making; Delay Discounting; discounting; generalized estimating equations; Humans; Likelihood Functions; mixed‐effects models; Models, Statistical; Monte Carlo; Monte Carlo Method; Monte Carlo simulation; Probability; Research Design; Statistical analysis; Statistics as Topic; Text Messaging - statistics & numerical data
• ISSN: 0022-5002; 1938-3711
• DOI: 10.1002/jeab.497

Discounting is the process by which outcomes lose value. Much of discounting research has focused on differences in the degree of discounting across various groups. This research has relied heavily on conventional null hypothesis significance tests that are familiar to psychologists, such as t‐tests and ANOVAs. As discounting research questions have become more complex by simultaneously focusing on within‐subject and between‐group differences, conventional statistical testing is often not appropriate for the obtained data. Generalized estimating equations (GEE) are one type of mixed‐effects model that are designed to handle autocorrelated data, such as within‐subject repeated‐measures data, and are therefore more appropriate for discounting data. To determine if GEE provides similar results as conventional statistical tests, we compared the techniques across 2,000 simulated data sets. The data sets were created using a Monte Carlo method based on an existing data set. Across the simulated data sets, the GEE and the conventional statistical tests generally provided similar patterns of results. As the GEE and more conventional statistical tests provide the same pattern of result, we suggest researchers use the GEE because it was designed to handle data that has the structure that is typical of discounting data.