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.

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.

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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.


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.


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.


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.


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