CRITICAL REALISM AS A META-FRAMEWORK FOR UNDERSTANDING THE RELATIONSHIPS BETWEEN COMPLEXITY AND QUALITATIVE COMPARATIVE ANALYSIS
CRITICAL REALISM AS A META-FRAMEWORK FOR UNDERSTANDING THE RELATIONSHIPS BETWEEN COMPLEXITY AND QUALITATIVE COMPARATIVE ANALYSIS
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DOI：
10.1179/rea.12.2.p663527490513071
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发表时间：
2013-01-01
影响因子：
2.6
通讯作者：
Verweij, Stefan
中科院分区：
文献类型：
--
作者：
Gerrits, Lasse;Verweij, Stefan
复杂性研究中使用了许多方法。其中之一是定性比较分析（QCA）。尽管许多作者提到了复杂性和 QCA 之间的关系，但这些联系很少明确。我们建议这样做的一种方法是使用批判现实主义作为元框架。本文通过研究 QCA 是一种了解复杂性的方法的程度来讨论该方法的可行性。这个问题分三步回答。首先，我们讨论复杂性的本质及其认识论含义。其次，我们关注巴斯卡对批判现实主义的观点，并展示如何将其用作理解社会复杂性的框架。第三，我们研究了 QCA 背后的本体论和认识论假设，并将这些假设与我们对复杂性的批判现实主义方法进行综合。我们认为复杂的现实是不可分解的、偶然的、不可压缩的和时间不对称的。我们的结论是，尽管 QCA 不可避免地是还原性的（即它压缩现实）和部分性的（即它分解现实），但它的核心前提是建立在偶然性和时间不对称的概念之上的。因此，它不仅是进行复杂性信息研究的强大方法，而且本身也是一种复杂性信息方法。
Many methods are used in research on complexity. One of these is qualitative comparative analysis (QCA). Although many authors allude to the relationships between complexity and QCA, these links are rarely made explicit. We propose that one way of doing so is by using critical realism as a meta-framework. This article discusses the viability of this approach by examining the extent to which QCA is a complexity-informed method. This question is answered in three steps. First, we discuss the nature of complexity and its epistemological implications. Second, we focus on Bhaskar's perspective on critical realism and show how it can be used as a framework for understanding social complexity. Third, we examine the ontological and epistemological assumptions underlying QCA and synthesize these with our critical realist approach to complexity. We argue that complex reality is non-decomposable, contingent, non-compressible and time-asymmetric. We conclude that, although QCA is inevitably reductive (i.e. it compresses reality) and partial (i.e. it decomposes reality), its core premises are built upon the notions of contingency and time-asymmetry. Therefore, it is not only a powerful method for doing complexity-informed research, but is also a complexity-informed method by itself.