Yu-Sung Su

Yu-Sung Su
Tsinghua University | TH · Department of Political Science

PhD in Political Science, CUNY

About

24
Publications
6,151
Reads
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3,015
Citations
Additional affiliations
August 2010 - present
Tsinghua University
Position
  • Professor (Associate)

Publications

Publications (24)
Preprint
Understanding the relationships among dispositional factors, brain activities and hazard recognition caters for development of auxiliary technologies, improving construction safety performance. However, current bivariate statistical approaches embedded potential bias to the interpretation of brain activities. This study aims to reinvestigate the me...
Article
This paper introduces a Bayesian multilevel model based on the age-period-cohort framework to examine Chinese happiness. Using 8 waves of the Chinese General Social Survey (CGSS) data between 2005–2015, the model not only solves the co-linearity problem with weakly informative priors and explicit assumptions, it also produces more computationally s...
Article
Full-text available
Past studies on the Chinese Internet management system have revealed a smart Internet management system that takes advantage of time to filter content with collective action potential. How and why such a system was institutionalized? We offer a historical institutional analysis to explain the way in which the system evolved. We implement social net...
Article
Full-text available
A model for identifying, analyzing, and quantifying the mechanisms for the influence of improper workplace environment on human error in elevator installation is proposed in this study. By combining a modification of a human error model with real-world inspection data collected by an elevator installation company, the influence paths of improper wo...
Article
Because human error plays a direct role in accidents, studying the causal relationship between the environment and human error is essential to prevent mishaps. However, these relationships have been explored solely using bivariate statistical analysis and thus require more intermediate factors to emphasize the need for monitoring and controlling hu...
Article
Full-text available
Item non-response is endemic to most survey studies, and hinders the researcher in making sensible inferences. One plausible solution to this problem, multiple imputation (MI), is becoming a widely used approach in dealing with the problem of missing data thanks to the development of various software packages. Nonetheless, MI is not a panacea. Impu...
Code
R package for Data Analysis using multilevel/hierarchical model
Article
Full-text available
Causal inference in observational studies typically requires making com-parisons between groups that are dissimilar. For instance, researchers inves-tigating the role of a prolonged duration of breastfeeding on child outcomes may be forced to make comparisons between women with substantially dif-ferent characteristics on average. In the extreme the...
Article
Based on Chinese patent data from 1985 to 2004, this study aims to provide a comprehensive analysis of formal university–industry collaborations in China, with a specific focus on the compound effect of geographic distance and other predictors. The results show that geographic distance is indeed an obstructive factor in achieving university–industr...
Conference Paper
One commonly acknowledged challenges in polls or surveys is item non-response, i.e., a significant proportion of respondents conceal their preferences about particular questions. This paper presents how multiple imputation (MI) techniques are applied to the reconstruction of vote choice distribution in telephone survey samples. Given previous studi...
Article
Full-text available
Our mi package in R has several features that allow the user to get inside the imputation process and evaluate the reasonableness of the resulting models and imputations. These features include: choice of predictors, models, and transformations for chained imputation models; standard and binned residual plots for checking the fit of the conditional...
Article
Full-text available
Iterative imputation, in which variables are imputed one at a time each given a model predicting from all the others, is a popular technique that can be convenient and flexible, as it replaces a potentially difficult multivariate modeling problem with relatively simple univariate regressions. In this paper, we begin to characterize the stationary d...
Article
Objectives. Income inequality in the United States has risen during the past several decades. Has this produced an increase in partisan voting differences between rich and poor? Methods. We examine trends from the 1940s through the 2000s in the country as a whole and in the states. Results. We find no clear relation between income inequality and cl...
Article
Full-text available
School vouchers are one of the most contested issues in educational policy. Yet, various survey data often yield different results on support for vouchers {Moe 2001}. Voucher opinions are better understood by looking at American people into several demographical and geographical segments. However, by doing this, we encounter a data problem because...
Article
The fundamental problem of causal inference is that an individual cannot be simultaneously observed in both the treatment and control states (Holland 1986). Propensity score methods that compare the treatment and control groups by discarding the unmatched units are now widely used to deal with this problem. Propensity score matching works well when...
Article
Full-text available
The fundamental problem of causal inference is that an individual cannot be simultaneously observed in both the treatment and control states. The propen-sity score matching methods that compare the treatment and control groups by discarding the unmatched units is now widely used to deal with this prob-lem. In some situations, however, it is possibl...
Article
The purpose of default settings in a graphic tool is to make it easy to produce good graphics that accord with the principles of statistical graphics, e.g., [Tufte, E.R., 1990. Envisioning Information. Graphics Press, Cheshire, Conn, Tufte, E.R., 1997. Visual Explanations: Images and Quantities, Evidence and Narrative, 2nd Edition. Graphics Press,...
Article
We propose a new prior distribution for classical (non-hierarchical) logistic regres- sion models, constructed by first scaling all nonbinary variables to have mean 0 and standard deviation 0.5, and then placing independent Student-t prior distributions on the coefficients. As a default choice, we recommend the Cauchydistribution with center 0 and...

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