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PRIISM

PRIISM, the Center for Practice and Research at the Intersection of Information, Society, and Methodology, is an interdisciplinary center that builds capacity for researchers within academia and beyond to collect data, build inferential models and predictive algorithms, and communicate findings in impactful and socially responsible ways.

Student Visualization of the Month

Decline in Subway Ridership: Pre- and Post-COVID

Congratulations to Joe Marlo, the winner of our first PRIISM/A3SR student visualization competition!

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Highlights

Test-Taking for Gifted and Talented Kindergarten

Sharon Weinberg and Ying Lu’s recently published article demonstrates the importance of addressing diversity in Gifted & Talented programs to help reduce inequality in gifted education.

Regression and Other Stories (Analytical Methods for Social Research)

Jennifer Hill, along with co-authors Andrew Gelman and Aki Vehtari, has a new textbook just out from Cambridge University Press.

What Nairobi Youth Think about Politics, the State & the Future

Read Elisabeth King, Daphna Harel, Dana Burde, Jennifer Hill, and Simon Grinsted's newly published paper.

Mischievous Responders

Read Joe Cimpian's three papers on "mischievous responders."

The Judicial System and Policing

Ravi Shroff has co-authored two papers on the judicial system and policing.

Statistics Using R: An Integrated Approach

Daphna Harel and Sharon Weinberg's new textbook is now available!

Job Opportunity

ThinkCausal Director of Research and Data Analysis

The thinkCausal project is now hiring a Director of Research and Data Analysis. This individual will have primary responsibility for organizing the research efforts for the project across a team of faculty, consultants, and students. In addition this individual will help to design, build, test, and evaluate a suite of educational tools and software developed to help education researchers to use and understand sophisticated causal inference methods.  For more information see this project summary.

Qualifications
The position requires a MS degree in Statistics or equivalent training in programming and statistics obtained through the pursuit of a graduate degree including coursework or other formal training in causal inference. Ability to communicate statistical concepts to non-technical audiences will be critical. Programming expertise in R and R Shiny is required, as is experience with applied data analysis projects. Experience with other programming languages or tools (particularly web development tools) would also be useful. Teaching experience or training in educational technology is a bonus.

Application Instructions
To apply please submit a cover letter and CV to jennifer.hill@nyu.edu and susana.toro@nyu.edu, in addition to applying on Interfolio. The cover letter should explain clearly how the applicant’s experience matches the desired skills and knowledge.  

Apply now via Interfolio for the ThinkCausal Director of Research and Data Analysis

The PRIISM Team

Meet the faculty, researchers, students, and affiliates working at PRIISM

Meet the Team

MS, Applied Statistics for Social Science Research

PRIISM is highly integrated with the academic program of Applied Statistics for Social Science Research. Learn advanced quantitative research techniques and apply them to critical policy issues across social, behavioral, and health sciences.

Degree Details How to Apply

Fellowships

PRIISM Social Impact Research Fellowship Program

Funded by the Moore Sloan Data Science Environment at NYU

PRIISM Opportunity Fellowship

highly-selective fellowship is to support the growth and development of a promising student in their first year of the Applied Statistics for Social Science Research MS program.

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Visualization by A3SR student, Joe Marlo, of decline of NYC subway ridership pre and post covid. Color represents change in mean daily entries by station

Student Visualization Winner, Joe Marlo

Decline in Subway Ridership: Pre vs. Post-covid

A3SR student, Joe Marlo's visualization shows the disparity in the decline of subway ridership across NYC neighborhoods. Stations in outer boroughs experiencing less decline compared to their Manhattan counterparts - perhaps due to essential worker status or employment flexibility. The decline captures the change in mean daily riders entering a given station, and the encompassing Voronoi diagram partitions geographies based on their closest station.