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

Abstract

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 testing that is familiar to psychology in general 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, were compared the techniques across 2,000 simulated data sets. The data sets were created using a Monte Carlo method based of 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.

Meta‐analyses and effect sizes in applied behavior analysis: A review and discussion

“…We have reviewed current meta-analytic methods and advocate for their increased use in applied behavior analysis research and literature reviews. The perspective advanced herein is consistent with a larger movement toward incorporating newer quantitative tools and methods in behavior science and behavior analysis in general (Caron, 2019;Craig & Fisher, 2019;DeHart & Kaplan, 2019;Elliffe & Elliffe, 2019;Falligant et al, 2020;Franck et al, 2019;Friedel et al, 2019;Gilroy et al, 2020;Greene et al, 2017;Jacobs, 2019;Kaplan et al, 2019;Killeen, 2019;Kyonka, 2019;Lanovaz et al, 2020;Levin et al, 2019;Riley et al, 2019;Turgeon & Lanovaz, 2020;Villarreal et al, 2019;Young, 2019). To borrow a quote attributed to Galileo, mathematics is the language of science.…”

Comparison of delay discounting of different outcomes in cigarette smokers, smokeless tobacco users, e‐cigarette users, and non‐tobacco users

“…To compare the degree of discounting across both group and outcome type we used a Generalized Estimating Equation (GEE; Friedel et al, 2019;Hanley et al, 2003). A GEE is a statistical model designed to account for potentially autocorrelated data that exist in clusters.…”

Hyperbolic modeling and assessment of hypothetical health behaviors during a viral outbreak using crowdsourced samples

“…There are other ways that the current discounting and decision‐making data could have been analyzed, notably via multilevel modeling (MLM; Young, 2017), Generalized Estimating Equations (GEE; Friedel et al, 2019), or inclusion of scaling parameters in the multiplicative hyperbolic model used (e.g., Cox & Dallery, 2016; Jarmolowicz, Reed, Bruce, et al, 2018; Vanderveldt et al, 2015). While MLMs address certain issues with the traditional modeling methods used in the present study, they still require assumption of a functional form of the data (i.e., linear or nonlinear regression).…”