Optimal Matching for Observational Studies That Integrate Quantitative and Qualitative Research

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...

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Autores principales:	Ruoqi Yu, Dylan S. Small, David Harding, José Aveldanes, Paul R. Rosenbaum
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	causal inference narrative description optimal matching threshold algorithms Political institutions and public administration (General) JF20-2112 Probabilities. Mathematical statistics QA273-280
Acceso en línea:	https://doaj.org/article/6c4a1a7bf3534a7ebe36f3cc33ce3ffc
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spelling	oai:doaj.org-article:6c4a1a7bf3534a7ebe36f3cc33ce3ffc2021-11-26T11:19:50ZOptimal Matching for Observational Studies That Integrate Quantitative and Qualitative Research2330-443X10.1080/2330443X.2021.1919260https://doaj.org/article/6c4a1a7bf3534a7ebe36f3cc33ce3ffc2021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/2330443X.2021.1919260https://doaj.org/toc/2330-443XA 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.Ruoqi YuDylan S. SmallDavid HardingJosé AveldanesPaul R. RosenbaumTaylor & Francis Grouparticlecausal inferencenarrative descriptionoptimal matchingthreshold algorithmsPolitical institutions and public administration (General)JF20-2112Probabilities. Mathematical statisticsQA273-280ENStatistics and Public Policy, Vol 8, Iss 1, Pp 42-52 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	causal inference narrative description optimal matching threshold algorithms Political institutions and public administration (General) JF20-2112 Probabilities. Mathematical statistics QA273-280
spellingShingle	causal inference narrative description optimal matching threshold algorithms Political institutions and public administration (General) JF20-2112 Probabilities. Mathematical statistics QA273-280 Ruoqi Yu Dylan S. Small David Harding José Aveldanes Paul R. Rosenbaum Optimal Matching for Observational Studies That Integrate Quantitative and Qualitative Research
description	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.
format	article
author	Ruoqi Yu Dylan S. Small David Harding José Aveldanes Paul R. Rosenbaum
author_facet	Ruoqi Yu Dylan S. Small David Harding José Aveldanes Paul R. Rosenbaum
author_sort	Ruoqi Yu
title	Optimal Matching for Observational Studies That Integrate Quantitative and Qualitative Research
title_short	Optimal Matching for Observational Studies That Integrate Quantitative and Qualitative Research
title_full	Optimal Matching for Observational Studies That Integrate Quantitative and Qualitative Research
title_fullStr	Optimal Matching for Observational Studies That Integrate Quantitative and Qualitative Research
title_full_unstemmed	Optimal Matching for Observational Studies That Integrate Quantitative and Qualitative Research
title_sort	optimal matching for observational studies that integrate quantitative and qualitative research
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/6c4a1a7bf3534a7ebe36f3cc33ce3ffc
work_keys_str_mv	AT ruoqiyu optimalmatchingforobservationalstudiesthatintegratequantitativeandqualitativeresearch AT dylanssmall optimalmatchingforobservationalstudiesthatintegratequantitativeandqualitativeresearch AT davidharding optimalmatchingforobservationalstudiesthatintegratequantitativeandqualitativeresearch AT joseaveldanes optimalmatchingforobservationalstudiesthatintegratequantitativeandqualitativeresearch AT paulrrosenbaum optimalmatchingforobservationalstudiesthatintegratequantitativeandqualitativeresearch
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Optimal Matching for Observational Studies That Integrate Quantitative and Qualitative Research

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