von Eye Alexander, Wiedermann Wolfgang
Michigan State University, 220 Trowbridge Rd, East Lansing, MI, 48824, USA.
University of Missouri, Columbia, MO, 65211, USA.
Integr Psychol Behav Sci. 2017 Jun;51(2):324-344. doi: 10.1007/s12124-017-9378-6.
Three fundamental types of causal relations are those of necessity, sufficiency, and necessity and sufficiency. These types are defined in contexts of categorical variables or events. Using statement calculus or Boolean algebra, one can determine which patterns of events are in support of a particular form of causal relation. In this article, we approach the analysis of these forms of causality taking the perspective of the analyst of empirical data. It is proposed using Configural Frequency Analysis (CFA) to test hypotheses about type of causal relation. Models are proposed for two-variable and multi-variable cases. Two CFA approaches are proposed. In the first, individual patterns (configurations) are examined under the question whether they are in support of a particular type of causal relation. In the second, patterns that are in support are compared with corresponding patterns that are not in support. In an empirical example, hypotheses are tested on the prediction of sustainability of change in dietary fat intake habits.
因果关系有三种基本类型，即必要性、充分性以及必要性和充分性。这些类型是在分类变量或事件的背景下定义的。使用命题演算或布尔代数，可以确定哪些事件模式支持特定形式的因果关系。在本文中，我们从实证数据分析者的角度来探讨这些因果关系形式的分析。建议使用构型频率分析（CFA）来检验关于因果关系类型的假设。针对双变量和多变量情况提出了模型。提出了两种CFA方法。第一种方法是，在个体模式（构型）是否支持特定类型的因果关系这一问题下对其进行检验。第二种方法是，将支持的模式与不支持的相应模式进行比较。在一个实证例子中，对饮食脂肪摄入习惯变化可持续性的预测假设进行了检验。
