Pornprasertmanit Sunthud, Little Todd D
University of Kansas.
Int J Behav Dev. 2012 Jul 1;36(4):313-322. doi: 10.1177/0165025412448944.
Directional dependency is a method to determine the likely causal direction of effect between two variables. This article aims to critique and improve upon the use of directional dependency as a technique to infer causal associations. We comment on several issues raised by von Eye and DeShon (2012), including: encouraging the use of the signs of skewness and excessive kurtosis of both variables, discouraging the use of D'Agostino's , and encouraging the use of directional dependency to compare variables only within time points. We offer improved steps for determining directional dependency that fix the problems we note. Next, we discuss how to integrate directional dependency into longitudinal data analysis with two variables. We also examine the accuracy of directional dependency evaluations when several regression assumptions are violated. Directional dependency can suggest the direction of a relation if (a) the regression error in population is normal, (b) an unobserved explanatory variable correlates with any variables equal to or less than .2, (c) a curvilinear relation between both variables is not strong (standardized regression coefficient ≤ .2), (d) there are no bivariate outliers, and (e) both variables are continuous.
方向依赖性是一种确定两个变量之间可能的因果效应方向的方法。本文旨在对方向依赖性作为一种推断因果关联的技术的使用进行批判和改进。我们对冯·艾和德肖恩（2012年）提出的几个问题进行评论，包括：鼓励使用两个变量的偏度和过度峰度的符号，不鼓励使用达戈斯蒂诺检验，并鼓励仅在时间点内使用方向依赖性来比较变量。我们提供了改进的步骤来确定方向依赖性，以解决我们指出的问题。接下来，我们讨论如何将方向依赖性整合到具有两个变量的纵向数据分析中。我们还研究了在违反几个回归假设时方向依赖性评估的准确性。如果（a）总体中的回归误差是正态的，（b）一个未观察到的解释变量与任何小于或等于0.2的变量相关，（c）两个变量之间的曲线关系不强（标准化回归系数≤0.2），（d）没有双变量异常值，以及（e）两个变量都是连续的，那么方向依赖性可以表明关系的方向。
