Have a personal or library account? Click to login
Regression discontinuity design and its applications to Science of Science: A survey Cover

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

By: Meiling Li,  Yang Zhang and  Yang Wang  
Open Access
|Jun 2023

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.

DOI: https://doi.org/10.2478/jdis-2023-0008 | Journal eISSN: 2543-683X | Journal ISSN: 2096-157X
Language: English
Page range: 43 - 65
Submitted on: Mar 31, 2023
Accepted on: Apr 3, 2023
Published on: Jun 7, 2023
Published by: Chinese Academy of Sciences, National Science Library
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Meiling Li, Yang Zhang, Yang Wang, published by Chinese Academy of Sciences, National Science Library
This work is licensed under the Creative Commons Attribution 4.0 License.