This article was written by Leda Zimmerman for MIT Political Science and MIT News. The complete article is available online with the introductory section on research with MIT GOV/LAB faculty director Prof. Lily Tsai below.
Finding patterns in the noise
Using novel computational approaches, graduate student Sean Liu develops better tools for analyzing data.
When social scientists administer surveys and questionnaires, they cannot always count on the scrupulous cooperation of their respondents: It’s human nature to get distracted when faced with a form. So how can researchers sort through what may be unreliable data to identify statistically significant answers to their questions? That’s where Shiyao “Sean” Liu comes in.
“I have designed a tool for reducing measurement errors when respondents don’t pay serious attention to online questions,” says Liu, a sixth-year PhD candidate in political science. Through statistical methods he has devised, Liu can detect and eliminate random-seeming answers that make for a noisy dataset. “With cleaner data, it’s easier to discover patterns and generate meaningful results.”
Liu’s work on this computational tool earned the Best Graduate Student Poster Award from the Society of Political Methodology in 2019. It is one thrust of his dissertation research, which focuses on optimizing social science survey methods and data analysis.
After arriving at MIT in 2015 from Peking University with undergraduate degrees both in statistics and in philosophy, politics, and economics, Liu found himself in demand. During his first summer in Cambridge, Massachusetts, he was engaged by Lily Tsai, faculty director of the MIT Governance Lab, as both a field researcher and methodologist.
“She was writing about public support for authoritarian regimes, and investigating the role played by retributive justice,” says Liu. “The idea is that when authoritarian leaders punish their lower-level officials for wrongdoing, public support rises for the leaders because people perceive that the regime is pursuing justice.”
Liu visited China twice, and with MIT colleagues and local Chinese collaborators, helped conduct 1,600 face-to-face interviews.
Some political science theories suggest that punishing corruption improves the image of authoritarian leaders because it makes them appear more competent. But with computational help from Liu, the research team learned that authoritarian image-building wasn’t just about making the trains run on time.
“By leveraging new surveying methods, I was able to show that increased support for top leadership also flowed from people’s belief that leaders were behaving in a moral way — that they knew the difference between right and wrong,” he says.
Liu, who co-authored a forthcoming paper with Tsai on the popularity of anti-corruption punishment, believes this research provides a useful prism for examining governments today.
“When the Soviet Union collapsed in 1989, people thought democracy would be the only surviving political system in the world, but it wasn’t the case,” he says. “Regimes emerged that frequently used anti-corruption campaigns as a way of building up their popularity among the people.” Think Duterte in the Philippines and Bolsonaro in Brazil, says Liu. People are eager to prop up even the most non-democratic dictators, if they perceive them as pursuing justice.