Abstract

A quantitative study of treatment effects may form many matched pairs of a treated subject and an untreated control who look similar in terms of covariates measured prior to treatment. When treatments are not randomly assigned, one inevitable concern is that individuals who look similar in measured covariates may be dissimilar in unmeasured covariates. Another concern is that quantitative measures may be misinterpreted by investigators in the absence of context that is not recorded in quantitative data. When text information is automatically coded to form quantitative measures, examination of the narrative context can reveal the limitations of initial coding efforts. An existing proposal entails a narrative description of a subset of matched pairs, hoping in a subset of pairs to observe quite a bit more of what was not quantitatively measured or automatically encoded. A subset of pairs cannot rule out subtle biases that materially affect analyses of many pairs, but perhaps a subset of pairs can inform discussion of such biases, perhaps leading to a reinterpretation of quantitative data, or perhaps raising new considerations and perspectives. The large literature on qualitative research contends that open-ended, narrative descriptions of a subset of people can be informative. Here, we discuss and apply a form of optimal matching that supports such an integrated, quantitative-plus-qualitative study. The optimal match provides many closely matched pairs plus a subset of exceptionally close pairs suitable for narrative interpretation. We illustrate the matching technique using data from a recent study of police responses to domestic violence in Philadelphia, where the police report includes both quantitative and narrative information.

摘要

治疗效果的定量研究可以形成许多匹配的治疗对象和未治疗对照，它们在治疗前测量的协变量方面看起来相似。当治疗不是随机分配时，一个不可避免的问题是在测量的协变量中看起来相似的个体可能在未测量的协变量中不同。另一个担忧是，如果没有定量数据中未记录的上下文，调查人员可能会误解定量测量。当文本信息被自动编码以形成定量测量时，对叙述上下文的检查可以揭示初始编码工作的局限性。现有提案需要对匹配对的子集进行叙述性描述，希望在一组对中观察到更多未定量测量或自动编码的内容。对的子集不能排除对许多对的分析产生重大影响的微妙偏差，但也许对的子集可以为此类偏差的讨论提供信息，可能导致对定量数据的重新解释，或者可能提出新的考虑和观点。大量关于定性研究的文献认为，对一部分人的开放式叙述性描述可以提供信息。在这里，我们讨论并应用一种支持这种综合、定量加定性研究的最佳匹配形式。最佳匹配提供了许多紧密匹配的配对以及适合叙事解释的异常紧密配对的子集。