Qualitative Comparative Analysis: Addressing Methodological Challenges, with particular reference to survey data

拉金的定性比较分析（QCA）是一种创新的研究方法，旨在建立一个结果的必要和/或充分条件的配置。它在社会科学领域的使用越来越多，通常是由研究人员根据他们的分析提出政策建议。因此，重要的是它的用户和决策者了解它的长处和弱点。我们将评估QCA存在的突出问题，并制定应对措施。QCA反映了Ragin的假设，即社会科学中的许多理论都涉及案例集，特别是它们特征的因果属性，被单独或组合考虑。案例被视为特征的组合，后者使用集合来描述。例如，考虑一下关于白人工人阶级男孩教育成就低的理论主张。这些都是关于三种群体交集的案例：白人、工人阶级和男性。QCA将评估“白人工人阶级男孩”在多大程度上是某种结果的充分或必要条件。它还评估了存在或不存在这三个因素的所有其他组合。QCA有一种使用清晰集合的形式，其中一个情况要么在某个集合内要么在某个集合外，还有一种使用模糊集合的形式，其中一个情况可以在一个集合中具有部分隶属关系，从0到1进行缩放。模糊集质量分析是为了解决模糊集不能很好地描述某些因素的问题而发展起来的。考虑“成年”。18岁并不是简单的成人或非成人。在成人群体中，模糊的隶属度得分为0.8可能比较合适。对于某些结果，条件的充分或必要程度可以对模糊集进行评估，就像对清晰集一样，但会产生额外的复杂性。QCA是在政治学中为少数案例而发展起来的。从那以后，包括我们在内的研究人员都在调查数据中使用了它。这类数据集最常用的方法是回归分析的形式。有了回归，评估充分性和必要性的关系就困难得多。在确定所谓的独立变量的净影响时，回归也有忽视因果关系的配置方面和特定案例背景的危险。QCA解决了这些问题。然而，考虑到与更成熟的方法相比，它仍处于发展的早期阶段，它本身也有未解决的问题，我们将解决这些问题。我们将在三个方面开展工作：抽样、技术问题和QCA结果的解释。抽样问题包括有偏差的样本和缺失的数据如何影响QCA结果。技术问题包括，例如，使用模糊逻辑引起的逻辑悖论，例如，条件可能足以产生结果或不产生结果的情况。另一个重要的技术问题是校准，即对病例分配模糊值（例如，0.8为18岁的“成年”）。校准影响哪些条件配置被确定为产生结果的充分和/或必要条件。解释涉及的问题包括，是否可以，如果可以，如何在QCA结果的基础上提出因果主张，以及QCA发现的多个因果途径是否可能反映结果概念化不足。我们将承担数学逻辑工作，但也与现有的数据集工作。后一项工作将作为开发的更抽象的论点的试验台，提供使用QCA的说述性案例研究。它还将使我们能够评估QCA和传统技术作为分析各种类型和各种目的数据的方法的相对优点，它将使我们能够讨论QCA和回归技术的相对优点，作为向政策制定者通报介导社会理想结果产生的复杂因果途径的手段。

Ragin's Qualitative Comparative Analysis (QCA) is an innovative research method which aims to establish the configurations of necessary and/or sufficient conditions for an outcome. It is used increasingly within the social sciences, often by researchers who make policy recommendations based on their analyses. It is therefore important that its strengths and weaknesses are understood by its users and policymakers. We will evaluate outstanding problems with QCA, and develop responses to them. QCA reflects Ragin's assumption that much theorising in the social sciences concerns sets of cases, especially the causal properties of their characteristics, considered singly and in combination. Cases are treated as combinations of characteristics, with the latter described using sets. Consider, e.g., theoretical claims about why white working class boys have low educational achievement. These are claims about cases in the intersection of three sets: white, working class and male. QCA would assess the degree to which being a "white working class boy" is a sufficient or necessary condition for some outcome. It also assesses this for all other combinations of the presence or absence of these three factors.QCA has a form employing crisp sets where a case is either simply in or out of some set and a form employing fuzzy sets where a case can have partial membership in a set, scaled from 0 to 1. Fuzzy set QCA was developed to address the concern that some factors are not well described by crisp sets. Consider "adulthood". An 18 year old is not simply adult or non-adult. A fuzzy membership score of, say, 0.8 in the set of adults might be appropriate here. The degree to which conditions are sufficient or necessary for some outcome can be assessed for fuzzy sets, as for crisp sets, but additional complications arise.QCA was developed in political science for small numbers of cases. It has since fruitfully been used with survey data by researchers including ourselves. The most commonly used methods for such datasets are forms of regression analysis. With regression, it is much harder to assess relations of sufficiency and necessity. Regression, in determining the net effects of supposedly independent variables, also carries the danger of ignoring, as well as the configurational aspects of causation, the context of particular cases. QCA addresses these problems. However, given that, compared to more established methods, it is still in its early stages of development, it has unresolved problems of its own which we will address.We will work in three areas: sampling, technical problems and the interpretation of QCA results. Sampling concerns include how biased samples and missing data affect QCA results. Technical concerns include, e.g., logical paradoxes arising from the use of fuzzy logic, such as situations where conditions can be sufficient for both an outcome and its absence. Another important technical issue is calibration, i.e. the allocation of fuzzy values to cases (e.g., 0.8 for an 18 year old for "adulthood"). Calibration affects which configurations of conditions are identified as sufficient and/or necessary for an outcome. Interpretation concerns issues such as whether, and if so, how, causal claims can be made on the basis of QCA results, and whether multiple causal pathways uncovered by QCA might reflect inadequate conceptualisation of outcomes.We will undertake mathematical-logical work, but also work with existing datasets. The latter work will serve as a test bed for the more abstract arguments developed, providing illustrative case studies of the use of QCA. It will also enable us to evaluate the relative merits of QCA and conventional techniques as ways of analysing data of various types and for various purposes, and it will allow us to discuss the relative merits of QCA and regression techniques as means of informing policymakers about the complex causal pathways that mediate the production of socially desirable outcomes.