Liu Yu, West Stephen G
Arizona State University.
J Pers. 2016 Oct;84(5):560-79. doi: 10.1111/jopy.12182. Epub 2015 Jun 29.
Daily diaries and other everyday experience methods are increasingly used to study relationships between two time-varying variables X and Y. Although daily data potentially often have weekly cyclical patterns (e.g., stress may be higher on weekdays and lower on weekends), the majority of daily diary studies have ignored this possibility. In this study, we investigated the effect of ignoring existing weekly cycles. We reanalyzed an empirical dataset (stress and alcohol consumption) and performed Monte Carlo simulations to investigate the impact of omitting weekly cycles. In the empirical dataset, ignoring cycles led to the inference of a significant within-person X-Y relation whereas modeling cycles suggested that this relationship did not exist. Simulation results indicated that ignoring cycles that existed in both X and Y led to bias in the estimated within-person X-Y relationship. The amount and direction of bias depended on the magnitude of the cycles, magnitude of the true within-person X-Y relation, and synchronization of the cycles. We encourage researchers conducting daily diary studies to address potential weekly cycles in their data. We provide guidelines for detecting and modeling cycles to remove their influence and discuss challenges of causal inference in daily experience studies.
日常日记和其他日常经验方法越来越多地用于研究两个随时间变化的变量X和Y之间的关系。尽管日常数据通常可能具有每周的周期性模式（例如，工作日的压力可能更高，周末的压力可能更低），但大多数日常日记研究都忽略了这种可能性。在本研究中，我们调查了忽略现有每周周期的影响。我们重新分析了一个实证数据集（压力和酒精消费），并进行了蒙特卡罗模拟，以研究省略每周周期的影响。在实证数据集中，忽略周期导致推断出显著的个体内X-Y关系，而对周期进行建模则表明这种关系不存在。模拟结果表明，忽略X和Y中都存在的周期会导致估计的个体内X-Y关系出现偏差。偏差的大小和方向取决于周期的大小、真实的个体内X-Y关系的大小以及周期的同步性。我们鼓励进行日常日记研究的研究人员在其数据中处理潜在的每周周期。我们提供了检测和建模周期以消除其影响的指导方针，并讨论了日常经验研究中因果推断的挑战。
