Publications

How Monitoring Influences Trust: A Tale of Two Faces

In a repeated trust game with monitoring, we found trustors misattributed the strategic, compliant behavior they observed as signals of trustees’ trustworthiness. As a result, trustors misplaced their trust when they were unable to monitor their counterparts.
In Management Science, 2016.

Delay Discounting, Genetic Sensitivity, and Leukocyte Telomere Length

We study the relationship between impatience (measured by delay discounting task) and telomere length, a biological marker of aging at the cellular level.
In PNAS, 2016.

Recent Posts

U-Shape Test using "Two Lines" - A Simple Solution for Discrete IV

Motivation Two-lines Approaches Issue with Discrete Dependent Variable Motivation In this paper by Uri Simonsohn (2017), the author proposed a noval method to test U-Shape relationship. In the literature, the popular way of testing U-shapeness relationship between x and y is to add a quadratic term in the regression $y=\beta_0+\beta_1 x + \beta_2 x^2 +\epsilon$ ($\epsilon$ is an i.i.d noise). If $\beta_1$ is statistitally significant, then the relationship betewen x and y are U-shape.

Fake News Consumption and Segregation on Twitter

To form accurate beliefs about the world (e.g., whether the earth is flat or a sphere, whether vaccination causes autism, etc), people must encounter diverse views and opinions which will sometimes contradict their pre-existing views. Many scholars concerned that the emergence of internet especially recent social media reduces the cost of acquiring information from a wide range of sources, facilitating consumers to self-segregate and limit themselves to the information sources that are likely to confirm their views.

Personalized Data Summary Function Using "data.table"

One function I miss about Stata is its tabstat. By using just one line code, it can produce very useful summary statistics such as mean, and standard error by groups by conditions. R has its own built-in summary function – summary(), too, but in most cases in my research, I found the summaries produced is barely useful. Consider the following pseudo-data: library(data.table) set.seed(10) N = 120 DT = data.table(x = rnorm(N,1), y = rnorm(N,2), category = sample(letters[1:3], N, replace = T)) DT[1:10] ## x y category ## 1: 1.

Is it the end of the world if $\alpha=0.005$ is the new norm?

In this paper by Benjamin et al (2017) on redefining statistical significance, they proposed to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of new discoveries. That is the proposed p-value is one tenth of the conventional one!! Suppose the world changed to p=0.005. Do we need 10X more sample? As a researcher without sufficient funding, we care about how much additional sample we need suppose our hypothesis is true.

Anonymize Individuals using digest()

When requesting individual level data from others (a company or a government agency), we usually need to properly anomymize the individuals to protect their privacy. The following is an example: (Data = data.frame(Name = c("John Smith", "Jenny Ford","Vivian Lee"), Secret = c("Hate dog","Afraid of ghost","A bathroom dancer"))) ## Name Secret ## 1 John Smith Hate dog ## 2 Jenny Ford Afraid of ghost ## 3 Vivian Lee A bathroom dancer One simple way is we can just drop the Name, and only keep the Secret since we are more interested in their secrets.

Teaching Experiences

• Customer Analytics, Master of Science in Business Analytics, SKK GSB
• Marketing Analytics, full-time and professional MBA program, SKK GSB
• Marketing Managment, full-time and professional MBA program, SKK GSB
• Principles of Marketing, 2015, NUS
• Part-time guitar tutor, 2006-2007, Jinan University Guitar Club