<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec>
<h3>Purpose</h3>
<p>With the availability of large-scale scholarly datasets, scientists from various domains hope to understand the underlying mechanisms behind science, forming a vibrant area of inquiry in the emerging “science of science” field. As the results from the science of science often has strong policy implications, understanding the causal relationships between variables becomes prominent. However, the most credible quasi-experimental method among all causal inference methods, and a highly valuable tool in the empirical toolkit, Regression Discontinuity Design (RDD) has not been fully exploited in the field of science of science. In this paper, we provide a systematic survey of the RDD method, and its practical applications in the science of science.</p>
</sec>
<sec>
<h3>Design/methodology/approach</h3>
<p>First, we introduce the basic assumptions, mathematical notations, and two types of RDD, i.e., sharp and fuzzy RDD. Second, we use the Web of Science and the Microsoft Academic Graph datasets to study the evolution and citation patterns of RDD papers. Moreover, we provide a systematic survey of the applications of RDD methodologies in various scientific domains, as well as in the science of science. Finally, we demonstrate a case study to estimate the effect of Head Start Funding Proposals on child mortality.</p>
</sec>
<sec>
<h3>Findings</h3>
<p>RDD was almost neglected for 30 years after it was first introduced in 1960. Afterward, scientists used mathematical and economic tools to develop the RDD methodology. After 2010, RDD methods showed strong applications in various domains, including medicine, psychology, political science and environmental science. However, we also notice that the RDD method has not been well developed in science of science research.</p>
</sec>
<sec>
<h3>Research Limitations</h3>
<p>This work uses a keyword search to obtain RDD papers, which may neglect some related work. Additionally, our work does not aim to develop rigorous mathematical and technical details of RDD but rather focuses on its intuitions and applications.</p>
</sec>
<sec>
<h3>Practical implications</h3>
<p>This work proposes how to use the RDD method in science of science research.</p>
</sec>
<sec>
<h3>Originality/value</h3>
<p>This work systematically introduces the RDD, and calls for the awareness of using such a method in the field of science of science.</p>
</sec>
</abstract>

Purpose
With the availability of large-scale scholarly datasets, scientists from various domains hope to understand the underlying mechanisms behind science, forming a vibrant area of inquiry in the emerging “science of science” field. As the results from the science of science often has strong policy implications, understanding the causal relationships between variables becomes prominent. However, the most credible quasi-experimental method among all causal inference methods, and a highly valuable tool in the empirical toolkit, Regression Discontinuity Design (RDD) has not been fully exploited in the field of science of science. In this paper, we provide a systematic survey of the RDD method, and its practical applications in the science of science.


Design/methodology/approach
First, we introduce the basic assumptions, mathematical notations, and two types of RDD, i.e., sharp and fuzzy RDD. Second, we use the Web of Science and the Microsoft Academic Graph datasets to study the evolution and citation patterns of RDD papers. Moreover, we provide a systematic survey of the applications of RDD methodologies in various scientific domains, as well as in the science of science. Finally, we demonstrate a case study to estimate the effect of Head Start Funding Proposals on child mortality.


Findings
RDD was almost neglected for 30 years after it was first introduced in 1960. Afterward, scientists used mathematical and economic tools to develop the RDD methodology. After 2010, RDD methods showed strong applications in various domains, including medicine, psychology, political science and environmental science. However, we also notice that the RDD method has not been well developed in science of science research.


Research Limitations
This work uses a keyword search to obtain RDD papers, which may neglect some related work. Additionally, our work does not aim to develop rigorous mathematical and technical details of RDD but rather focuses on its intuitions and applications.


Practical implications
This work proposes how to use the RDD method in science of science research.


Originality/value
This work systematically introduces the RDD, and calls for the awareness of using such a method in the field of science of science.

Regression discontinuity design and its applications to Science of Science: A survey

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution 4.0 International License.

Purpose
With the availability of large-scale scholarly datasets, scientists from various domains hope to understand the underlying mechanisms behind science,...

Regression discontinuity design and its applications to Science of Science: A survey

Article Category: Research Paper

Published Online: Jun 07, 2023

Page range: 43 - 65

Received: Mar 31, 2023

Accepted: Apr 03, 2023

DOI: https://doi.org/10.2478/jdis-2023-0008

Keywords
Regression Discontinuity Design, Science of Science, Causal Inference

© 2023 Meiling Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Regression discontinuity design and its applications to Science of Science: A survey

Article Category: Research Paper

Published Online: Jun 07, 2023

Page range: 43 - 65

Received: Mar 31, 2023

Accepted: Apr 03, 2023

DOI: https://doi.org/10.2478/jdis-2023-0008

KeywordsRegression Discontinuity Design, Science of Science, Causal Inference

© 2023 Meiling Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Regression Discontinuity Design, Science of Science, Causal Inference