Navigating Tough Conversations in Statistical Collaboration — Professional Development Professional Skills Development
ASA, Section on Statistical Consulting, Caucus for Women in Statistics
Instructor(s): Julia L Sharp, Colorado State University; Emily H Griffith, North Carolina State University
Statistical practitioners face difficult conversations in their interactions with their clients and collaborators. The topics of these conversations vary widely, from completion timelines to the use and interpretation of p-values. While there are no universal guidelines for navigating tough conversations, thoughtful discussion about common experiences and lessons learned; reflection on differences among individuals and situations; and exercises such as role playing can be helpful to prepare and build confidence for engaging in future tough conversations. In this course, we will build participants’ confidence to effectively communicate with clients and customers when challenging topics or situations arise. In this course, we will:
¦ Give and solicit examples of difficult conversations often encountered in statistical collaboration.
¦ Provide suggestions to approach and engage in these difficult conversations through multiple interactive activities, with a focus on leveraging participant strengths by using individual personality and skills to have these conversations in participants’ own style.
¦ Engage participants in the interactive session and learn from each other through discussion, role-playing, and conversations motivated by participants’ questions and recently produced videos portraying several difficult conversations between statisticians and their collaborators.
Recent Advances in Statistical Methods Applied to Racial Equity Research — Invited Papers
ENAR, American Public Health Association, Section on Statistics in Epidemiology, Justice Equity Diversity and Inclusion Outreach Group, Caucus for Women in Statistics
Organizer(s): Ruby Lee Bayliss, Dornsife School of Public Health, Drexel University
Chair(s): Loni Philip Tabb, Dornsife School of Public Health, Drexel University
2:05 PM
Predicting Asthma Morbidity in Multiethnic Urban Rhode Island Children Anarina Murillo, New York University; Loni Philip Tabb, Dornsife School of Public Health, Drexel University; Rachel Gaither, Brown University; Sheryl J Kopel, Brown University; Michelle L Rogers, Brown University; Melanie Morales Aquino, Brown University; Patrick M Vivier, Brown University; Daphne Koinis-Mitchell, Brown University
2:20 PM
A Spatial Assessment of the Impact of Residential Segregation on Racial/Ethnic Cardiovascular Health Inequities Ruby Lee Bayliss, Dornsife School of Public Health, Drexel University; Harrison Quick, Drexel University; Loni Philip Tabb, Dornsife School of Public Health, Drexel University; Sharrelle Barber, Dornsife School of Public Health, Drexel University; Mahasin Mujahid, University of California, Berkeley School of Public Health; Kiarri Kershaw, Northwestern University, Feinburg School of Medicine
Erin Chapman, Amazon AWS Cryptography Kimberly Sellers, Georgetown University Deirdre Middleton, RTI Eunice J. Kim, Microsoft Therri Usher, U.S. Food and Drug Administration Mark Otto, Fish and Wildlife Service
Career Development Panel: Networking Like a Pro: A Guided Networking Session — Professional Development Professional Skills Development
ASA, Committee on Career Development, Caucus for Women in Statistics
ASA Committee on Career Development (ASA CCD) is hosting a guided networking social for students and early career statisticians to practice in a friendly environment. We will have “pro networkers” discusses various topics such as introducing yourself confidently followed by practice time. During the practice sessions, students and early career professionals will be forced to “rotate” to meet and practice with new people (volunteers from industry, government, and academia).
Incorporating Ethical Thinking into Research and Innovation Through Education, Planning, Conduct, and Communication — Invited Panel
Committee on Professional Ethics, Statistics Without Borders, Committee on Scientific Freedom and Human Rights, Caucus for Women in Statistics, Biopharmaceutical Section
Organizer(s): Stephanie S Shipp, University of Virginia
Chair(s): Stephanie S Shipp, University of Virginia
James Giordano, Georgetown University and US Naval War College Jeri Metzger Mulrow, Westat Jing Cao, SMU Momin Malik, Mayo Clinic Nathan Colaner, Seattle University Matthew D. Rotelli, Eli Lilly and Company
Brittney Bailey, Amherst College Emily H Griffith, North Carolina State University Jo Hardin, Pomona College Renee Moore, Drexel University Venessa Singhroy, Queensborough Community College
Data-Driven Ethics as Statistical Practice — Topic Contributed Papers
Conference on Statistical Practice Steering Committee, Justice Equity Diversity and Inclusion Outreach Group, Caucus for Women in Statistics
Organizer(s): David Corliss, Peace-Work
Chair(s): Jana Lynn Asher, Slippery Rock University
4:05 PM
Ethical considerations for data involving human gender and sex variables Suzanne Thornton, Swarthmore College Dept. of Mathematics and Statistics; Dooti Roy, University of Connecticut; Stephan Parry, Cornell University; Donna LaLonde, ASA; Wendy Martinez, Bureau of Labor Statistics; Renee Ellis, U.S. Census; David Corliss, Peace-Work
Best Practices in Coordinating Large-Scale Data Science Initiatives Joel Thurston, UVA Biocomplexity Institute Social and Decision Analytics Division; Sallie Keller, Biocomplexity Institute, University of Virginia; Aaron Schroeder, Biocomplexity Institute, University of Virginia; Stephanie S Shipp, University of Virginia
A Hierarchical Approach to Customer Lifetime Value Xiaojing Dong, Santa Clara University; Mark Scarr, Atlassian Corporation PLC; Stephan Curiskis, Atlassian Corporation PLC; Fan Jiang, Atlassian Corporation PLC
Making ASA an Anti-Racist Organization: Report from the ASA Anti-Racism Task Force — Invited Papers
Justice Equity Diversity and Inclusion Outreach Group, Committee on Minorities in Statistics, Committee on Membership Retention and Recruitment, Caucus for Women in Statistics
Organizer(s): David A Marker, Marker Consulting, LLC
Teaching Social Justice Through Statistics and Biostatistics: The Case for a DEI-Infused Curriculum — Invited Papers
Section on Teaching of Statistics in the Health Sciences, Justice Equity Diversity and Inclusion Outreach Group, Section on Statistics and Data Science Education, Caucus for Women in Statistics
Organizer(s): Rongwei (Rochelle) Fu (she/her/hers), School of Public Health, Oregon Health & Science University
Chair(s): Byung Park (he/him/his), Knight Cancer institute, Oregon Health & Science University
Incorporating Data Equity into Biostatistics Curriculum Rongwei (Rochelle) Fu (she/her/hers), School of Public Health, Oregon Health & Science University ; Meike Niederhausen, OHSU-PSU School of Public Health; Janne Boone-Heinonen, OHSU-PSU School of Public Health; Byung Park (he/him/his), Knight Cancer institute, Oregon Health & Science University; Thuan Nguyen, Oregon Health Science University; Kelly Gonzales, OHSU-PSU School of Public Health; Amber Lin, Oregon Health & Science University; Jodi Lapidus, OHSU-PSU School of Public Health
9:50 AM
Discussant: Scarlett (she/her/hers) L. Bellamy , Drexel University, Dornsife School of Public Health
Improving Data Science Education Infrastructure at Community Colleges, Teaching, and Research Universities — Invited Panel
Section on Statistics and Data Science Education, Section on Teaching of Statistics in the Health Sciences, Council on Undergraduate Research, Caucus for Women in Statistics
Organizer(s): Mine Dogucu, University of California Irvine
Chair(s): Babak Shahbaba, University of California Irvine
Sam Behseta, California State University, Fullerton Alex Franks, University of California, Santa Barbara Mariam Salloum, University of California, Riverside
Anna Hui, Missouri Department of Labor and Industrial Relations Julia Lane, New York University Barry Johnson, Statistics of Income, IRS Nancy Potok, NAPx Consulting
Equity in Innovation: Should Race and Ethnicity Be Included in Clinical Prediction Models and Algorithms? — Topic Contributed Papers
Biometrics Section, Justice Equity Diversity and Inclusion Outreach Group, Section on Statistical Learning and Data Science, Caucus for Women in Statistics
Organizer(s): Yates Coley, Kaiser Permanente Washington Health Research Institute
Chair(s): Trang Nguyen, Johns Hopkins Bloomberg School of Public Health
Bill Burgos, Minnesota Timberwolves John Saintignon, FIBA Jonathan Martinez, Las Vegas Raiders Mauricio Elizondo, Association for Tennis Professionals (ATP Tour)
Committee on Statistics and Disability, Justice Equity Diversity and Inclusion Outreach Group, Quantitative Communication Interest Group, Caucus for Women in Statistics
Organizer(s): Gwynn Sturdevant, Harvard Business School
Mark Hansen, Columbia University Ben Rubin, Ear Studio Kimberly Arcand, Harvard University Jonathan Godfrey, Massey University & ONZM Allyson Bieryla, Harvard & Smithsonian
Innovations on Teaching Design of Experiments: Active Learning, Data Science, and Computer-Generated Designs — Invited Panel
Section on Physical and Engineering Sciences, Quality and Productivity Section, Section on Statistics and Data Science Education, Caucus for Women in Statistics
Organizer(s): Byran J Smucker, Miami University
Chair(s): David J. Edwards, Virginia Commonwealth University
Byran J Smucker, Miami University Nathaniel Stevens, University of Waterloo Jacqueline Asscher, Kinneret College on the Sea of Galilee Alan Vasquez, UCLA
Lorin Crawford, Brown University Branko Miladinovic, Janssen Research & Development Bonnie Shook-Sa, University of North Carolina - Chapel Hill Sally C Morton, Arizona State University Adam J Sullivan, Takeda Pharmaceuticals Company
Theory and Methods for Building Successful Data Analyses — Topic Contributed Papers
Section on Statistics and Data Science Education, Business Analytics/Statistics Education Interest Group, Section on Statistical Consulting, Caucus for Women in Statistics
Organizer(s): Roger Peng, Johns Hopkins Bloomberg School of Public Health
Chair(s): Stephanie C Hicks, Johns Hopkins Bloomberg School of Public Health
Forecasting for Policy in an Uncertain and Rapidly Changing World — Invited Papers
Business and Economic Statistics Section, Government Statistics Section, Section on Statistical Learning and Data Science, Caucus for Women in Statistics
Organizer(s): Andrew B Martinez, US Department of the Treasury
Chair(s): Andrew B Martinez, US Department of the Treasury
The Wisdom of Diversity in Committees Neil R Ericsson, Federal Reserve Board; David Hendry, Nuffield College; Yanki Kalfa, Rady School of Management; Jaime Marquez, Johns Hopkins University
Challenges, Successes, and Innovations in Collaborative Statistics: A Discussion Among Women in Academia, Government, and Industry — Topic Contributed Panel
Caucus for Women in Statistics, Committee on Women in Statistics, Committee on Career Development
Organizer(s): Samantha Seals, University of West Florida
Chair(s): Samantha Seals, University of West Florida
Cynthia Bland, RTI International Amanda Koepke, National Institute of Standards and Technology Motomi (Tomi) Mori, St. Jude Children's Research Hospital Gulcin Ozer, Eli Lilly and Company
CANCELED; Using Statistics to Advance Human Rights — Invited Papers
Social Statistics Section, Committee on Law and Justice Statistics, Committee on Scientific Freedom and Human Rights, Justice Equity Diversity and Inclusion Outreach Group, Caucus for Women in Statistics
Transforming Higher Education to Achieve Equity — Topic Contributed Panel
Section on Statistics and Data Science Education, Justice Equity Diversity and Inclusion Outreach Group, Committee of Representatives to AAAS, ASA Caucus of Academic Representatives, Caucus for Women in Statistics
Organizer(s): Julia L Sharp, Colorado State University
Chair(s): Sastry Pantula, California State University - San Bernardino
Deep Learning, Nonparametric Statistics, and Beyond — Invited Papers
Section on Nonparametric Statistics, Section on Statistical Learning and Data Science, Section on Statistical Computing, Caucus for Women in Statistics
Organizer(s): Yufeng Liu, University of North Carolina
Chair(s): Yufeng Liu, University of North Carolina
What We Know About What We Don’t Know: Overcoming Incomplete Data in Practice — Invited Papers
ENAR, Caucus for Women in Statistics, Section on Statistics in Epidemiology
Organizer(s): Sarah C. Lotspeich, University of North Carolina at Chapel Hill
Chair(s): Marissa C. Ashner, University of North Carolina at Chapel Hill
2:05 PM
Missing Data in the Baseline Health Surveys of the All of Us Research Program and the Opportunity from Multiple Information Sources Qingxia Chen, Vanderbilt University Medical Center; Robert M Cronin, The Ohio State University; Xiaoke Feng, Vanderbilt University Medical Center; Lina Sulieman, Vanderbilt University Medical Center; Brandy Mapes, Vanderbilt University Medical Center; Shawn Garbett, Vanderbilt University Medical Center; Ashley Able, Vanderbilt University Medical Center; Rebecca Johnston, Vanderbilt University Medical Center; Mick P. Couper, University of Michigan; Brian K Ahmedani, Henry Ford Health System
Bayesian Index Models for Heterogeneous Treatment Effects Hyung G. Park, NYU School of Medicine; Danni Wu, NYU School of Medicine; Eva Petkova, NYU School of Medicine; Thaddeus Tarpey, New York University; R. Todd Ogden, Columbia University
Changes in Crime Rates During the COVID-19 Pandemic Mikaela M Meyer, Carnegie Mellon University; Ahmed Hassafy, Carnegie Mellon University; Gina Lewis, Carnegie Mellon University; Prasun Shrestha, Carnegie Mellon University; Amelia M. Haviland, Carnegie Mellon University; Daniel Nagin, Carnegie Mellon University
3:25 PM
Discussant: Daniel Nagin, Carnegie Mellon University
Malicious URL Detection Using Machine Learning for Security Traffic Aritra Guha, Data Science & AI Research, AT&T Chief Data Office; Srivathsan Srinivasagopalan, Data Science & AI Research, AT&T Chief Data Office; Mani Subramaniam, Data Science & AI Research, AT&T Chief Data Office
Causal Inference for Undergraduates: Teaching Correlation Does Not Imply Students Understand Causal Inference — Invited Papers
Section on Statistics and Data Science Education, Section on Teaching of Statistics in the Health Sciences, ASA-MAA Joint Committee on Undergraduate Statistics, Caucus for Women in Statistics
Organizer(s): Kelly McConville, Harvard University
Melody Goodman, New York University Michele Andrasik, Fred Hutch Cancer Research Center Yates Coley, Kaiser Permanente Washington Health Research Institute Sahar Z Zangeneh, RTI International