Applying Mixed-Effects Modeling to Behavioral Economic Demand: An Introduction
Applying Mixed-Effects Modeling to Behavioral Economic Demand: An Introduction
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DOI：
10.1007/s40614-021-00299-7
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发表时间：
2021-07-21
影响因子：
2
通讯作者：
Koffarnus, Mikhail N.
中科院分区：
文献类型：
--
作者：
Kaplan, Brent A.;Franck, Christopher T.;Koffarnus, Mikhail N.
行为经济需求方法越来越多地应用于物质使用和消费者行为分析等各个领域。拟合需求数据的传统分析技术已被证明是有用的，但其中一些方法需要对数据进行预处理，忽略数据的依赖性，并存在统计局限性。我们将这些方法称为“适合群体”和“两阶段”，前者对群体或人口水平估计感兴趣，后者对个体受试者估计感兴趣。作为这些回归技术的扩展，混合效应（或多级）建模可以作为对这些传统方法的改进。显着的好处包括提供同时的群体（即群体）水平估计（具有更准确的标准误差）和个体水平预测，同时容纳“非系统”响应集和协变量。这些模型还可以适应复杂的实验设计，包括重复测量。本文的目标是介绍并提供应用于行为经济需求数据的混合效应建模技术的高级概述。我们将传统技术的结果与物种和实验设计不同的两个数据集的混合效应模型的结果进行比较和对比。我们讨论这些方法的相对优点和缺点，并提供对统计代码和数据的访问，以支持比较的分析可复制性。
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.