#  Yuki Atsusaka (Applied Stats) 

 



####  calendar\_today Date and Time 

 **October 30, 2024** 

 12:00PM - 01:30PM EDT 

####  pin\_drop Location 

 **CGIS Knafel room K354, or virtual via Zoom**  



 

 



 

##  Today's Speaker

 Yuki Atsusaka (University of Houston), "Addressing Measurement Errors in Ranking Questions for the Social Sciences"

###  **Virtual attendance link**

 [https://harvard.zoom.us/j/93279970656?pwd=gwNbemZHBpfH44CjW53RFFctplpb0…](https://harvard.zoom.us/j/93279970656?pwd=gwNbemZHBpfH44CjW53RFFctplpb0c.1)

##  Abstract

 Social scientists often use ranking questions to study people's opinions and preferences. However, little is understood about the general nature of measurement errors in such questions, let alone their statistical consequences and what researchers can do about them. We introduce a statistical framework to improve ranking data analysis by addressing measurement errors in ranking questions. First, we characterize measurement errors from random responses---arbitrary and meaningless responses based on a wide range of random patterns. We then quantify bias due to random responses, show that the bias may change our conclusion in any direction, and clarify why item order randomization alone does not solve the statistical issue. Next, we introduce our methodology based on two key design-based considerations: item order randomization and the addition of an "anchor" ranking question with known correct answers. They allow researchers to (1) learn about the direction of the bias and (2) estimate the proportion of random responses, enabling our bias-corrected estimators. We illustrate our methods by studying the relative importance of people's partisan identity compared to their racial, gender, and religious identities in American politics. We find that about 30% of respondents offered random responses and that these responses may affect our substantive conclusions.

 *The Applied Statistics Workshop (Gov 3009) meets all academic year, Wednesdays, 12pm-1:30pm, in CGIS K354. This workshop is a forum for advanced graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers.  
  
All interested Harvard affiliates are invited to attend.*



 

 



 

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