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.
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
Section on Statistical Consulting A.M. Roundtable Discussion (Added Fee) — Roundtables AM Roundtable Discussion
Section on Statistical Consulting
ML01:
There’s Always Room to Negotiate: Building Negotiation Skills for Statisticians Terrie Vasilopoulos, University of Florida, College of Medicine; Sonja I. Ziniel , University of Colorado School of Medicine; Margaret Stedman, Stanford University; Salem Dehom, Loma Linda University School of Nursing; Li Zhang, Dept. of Medicine and Dept. of Epidemiology and Biostatistics, University of California; Emily Leary, University of Missouri - Columbia
Ixavier A. Higgins, Eli Lilly and Company C. Christina Mehta, Emory University School of Medicine Sonja I. Ziniel , University of Colorado School of Medicine
Observations Through a Foggy Lens: Modeling Complex Measurement Error and Non-Random Missingness in Ecological and Environmental Health Data — Invited Papers
Section on Statistical Consulting, Section on Statistics and the Environment, The International Environmetrics Society
Detecting Changes in Dynamic Social Networks Using Multiply Labeled Movement Data
Zaineb L. Boulil, Rady Children's Hostpital; Henry Scharf, San Diego State University; John W. Durban, Southall Environmental Associates, Inc.; Holly Fearnbach, Sr3 SeaLife Response, Rehabilitation and Research; Trevor W. Joyce, Environmental Assessment Services; Samantha G. M. Leander, Southall Environmental Associates, Inc.
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
Best Practices in Project Management and Quality for Statisticians and Data Scientists — Professional Development Continuing Education Course
ASA, Section on Statistical Consulting
Instructor(s): Michiko Wolcott, Msight Analytics
If you work on analysis projects using real-life data, you face a variety of challenges. Many of these challenges are not strictly statistical yet are often unique to projects in applied statistics and data science. They include issues with the quality of source data, difficulties in planning and scoping due to the unknowns, challenges with project expectations and timelines, balancing replicability and reproducibility with project fluidity, among others.
In this workshop, we present best practices and methodologies to address key challenges in the non-statistical aspects of our work: project management and delivery, project quality, and data quality. We discuss the application of project management best practices to statisticians and data scientists and how it relates to the quality of projects. We then translate broadly recognized quality ideas to statistical practice itself. Finally, we provide an overview of industry practices in data quality and management and present a methodology for ensuring the quality of data used for analysis.
The workshop is software-agnostic and no specific background is assumed. Participants are encouraged to “bring” his/her own projects (data, computing environment, scripts, etc.) to use as case studies for themselves while following along (project details and data need not be shared).
Elaine Eisenbeisz, Omega Statistics Karen Grace-Martin, The Analysis Factor Clark Kogan, StatsCraft LLC Kim Love, QCC Quantitative Consulting and Collaboration Nayak Polissar, The Mountain-Whisper-Light Statistics and Data Science
Treatment Effect in Randomized Trials with Noncompliance Zonghui Hu, National Institutes of Health; Zhiwei Zhang, National Cancer Institute/National Institutes of Health; Dean Follmann, National Institute of Allergy and Infectious Diseases
Career Development of Staff Statisticians in Academic Settings Xiaoming Sheng, University of Utah College of Nursing; Margaret Stedman, Stanford University; C. Christina Mehta, Emory University School of Medicine; Li Zhang, Dept. of Medicine and Dept. of Epidemiology and Biostatistics, University of California; Charlotte Bolch, Midwestern University; Salem Dehom, Loma Linda University School of Nursing
