National Institute for Occupational Safety and Health.
Virginia Tech Carillion Research Institute.
J Exp Anal Behav. 2019 Mar;111(2):207-224. doi: 10.1002/jeab.497. Epub 2019 Jan 24.
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.
折扣是指结果失去价值的过程。许多折扣研究都集中在不同群体之间的折扣程度差异上。这项研究主要依赖于心理学家所熟悉的传统零假设显著性检验，如 t 检验和方差分析。随着折扣研究问题变得更加复杂，同时关注个体内差异和组间差异，传统的统计检验通常不适用于所获得的数据。广义估计方程 (GEE) 是一种混合效应模型，旨在处理自相关数据，例如个体内重复测量数据，因此更适合折扣数据。为了确定 GEE 是否提供与传统统计检验相似的结果，我们比较了 2000 个模拟数据集的技术。数据集是使用基于现有数据集的蒙特卡罗方法创建的。在模拟数据集中，GEE 和传统的统计检验通常提供相似的结果模式。由于 GEE 和更传统的统计检验提供了相同的结果模式，我们建议研究人员使用 GEE，因为它是专门为处理具有典型折扣数据结构的数据而设计的。
