Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Department of Applied Behavioral Science, University of Kansas, Lawrence, KS, USA.
J Exp Anal Behav. 2024 May;121(3):358-372. doi: 10.1002/jeab.910. Epub 2024 Mar 18.
In this meta-analysis, we describe a benchmark value of delay and probability discounting reliability and stability that might be used to (a) evaluate the meaningfulness of clinically achieved changes in discounting and (b) support the role of discounting as a valid and enduring measure of intertemporal choice. We examined test-retest reliability, stability effect sizes (d; Cohen, 1992), and relevant moderators across 30 publications comprising 39 independent samples and 262 measures of discounting, identified via a systematic review of PsychInfo, PubMed, and Google Scholar databases. We calculated omnibus effect-size estimates and evaluated the role of proposed moderators using a robust variance estimation meta-regression method. The meta-regression output reflected modest test-retest reliability, r = .670, p < .001, 95% CI [.618, .716]. Discounting was most reliable when measured in the context of temporal constraints, in adult respondents, when using money as a medium, and when reassessed within 1 month. Testing also suggested acceptable stability via nonsignificant and small changes in effect magnitude over time, d = 0.048, p = .31, 95% CI [-0.051, 0.146]. Clinicians and researchers seeking to measure discounting can consider the contexts when reliability is maximized for specific cases.
在这项荟萃分析中,我们描述了延迟和概率折扣的可靠性和稳定性的基准值,该基准值可用于:(a) 评估临床上实现的折扣变化的意义;(b) 支持折扣作为跨期选择的有效和持久衡量标准的作用。我们通过对 PsychInfo、PubMed 和 Google Scholar 数据库的系统回顾,检查了 30 篇出版物中的 39 个独立样本和 262 个折扣测量值的重测信度、稳定性效应量 (d; Cohen, 1992) 和相关的调节因素。我们计算了综合效应量估计,并使用稳健方差估计元回归方法评估了提出的调节因素的作用。元回归输出反映了适度的重测信度,r=0.670,p<0.001,95%置信区间 [0.618,0.716]。当在时间限制的情况下、在成年受访者中、使用金钱作为媒介以及在 1 个月内重新评估时,折扣的测量最可靠。测试还通过随着时间的推移效应大小的变化不显著和较小,表明具有可接受的稳定性,d=0.048,p=0.31,95%置信区间[-0.051,0.146]。寻求衡量折扣的临床医生和研究人员可以考虑在特定情况下可靠性最大化的情况下。