论文标题
模型经常符合道路的地方：结合统计，正式和案例研究方法
Where the Model Frequently Meets the Road: Combining Statistical, Formal, and Case Study Methods
论文作者
论文摘要
本文分析了正式，统计和案例研究传统中的工作或默认假设，通常存在于无法解释的差异的来源，异常值，参数价值观，人类动机，功能形式，时间和外部有效性的含义。我们认为，这些工作假设通常对于每种方法并不是必需的，并且可以通过允许多途径工作的方式放松这些假设。然后，我们分析了各种理论建设和理论测试研究目标的形式，统计和案例研究方法不同组合的比较优势。我们说明了这些优势，并通过分析和批评多途径研究的突出例子，提供有关如何结合不同方法的方法论建议。
This paper analyzes the working or default assumptions researchers in the formal, statistical, and case study traditions typically hold regarding the sources of unexplained variance, the meaning of outliers, parameter values, human motivation, functional forms, time, and external validity. We argue that these working assumptions are often not essential to each method, and that these assumptions can be relaxed in ways that allow multimethod work to proceed. We then analyze the comparative advantages of different combinations of formal, statistical, and case study methods for various theory-building and theory-testing research objectives. We illustrate these advantages and offer methodological advice on how to combine different methods, through analysis and critique of prominent examples of multimethod research.
