Data Literacy and Evidence Building
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Data Literacy and Evidence Building

About the Book

Data is transforming economic activity everywhere—from targeted advertising and advanced service delivery to the rise of $900,000 AI jobs. However, this transformation hasn't reached much of government and public policy, nor have other entities been tasked with steering organizations. This textbook aims to change that by teaching government decision-makers, policy students, and all others interested in policy and evidence-based steering how to use public data to create evidence that serves people more effectively. It is based on training classes designed for federal, state, and local government agency staff, where they learn data science skills through real-world projects. Over the past six years, over 1,000 staff members from over 450 agencies have been trained using this approach, leading to successful products like the Multi-State Postsecondary Report, the TANF Data Collaborative, the Arkansas Dashboards, and the Unemployment to Reemployment Portal. The approach has also resulted in published scientific papers.

About the Author

Carolina Haensch
Anna-Carolina Haensch, Ben Feder, Julia Lane, Angela Tombari, Frauke Kreuter


Anna-Carolina ("Caro") Haensch is currently an Assistant Research Professor at the University of Maryland, teaching statistics and methodology in the Joint Program in Survey Methodology (JPSM) and a Researcher ("Akademische Rätin") at Ludwig-Maximilians-Universität München (LMU). Her research interests include survey methodology, synthetic data, and the societal impact of large language models. She is passionate about teaching data science and statistics to diverse audiences and welcomes collaboration opportunities.


Benjamin Feder is a Senior Manager of Training and Analytics at the Coleridge Initiative, a non-profit organization whose goal is to use data to transform the way government access and use data for the social good through training programs, research projects, and a secure data facility. At the Coleridge Initiative, Benjamin has participated in the instruction of 25 Applied Data Analytics training programs in a variety of roles, currently as a lead lecturer, curriculum developer, and project team lead. Along with his training work, in his current role, Benjamin collaborates with data providers and data stewards to help build new infrastructure and data models spanning across government agency and state silos.


Julia Lane is is a Professor at the NYU Wagner Graduate School of Public Service and a NYU Provostial Fellow for Innovation Analytics. She cofounded the Coleridge Initiative, whose goal is to use data to transform the way governments access and use data for the social good through training programs, research projects and a secure data facility She currently serves on the Advisory Committee on Data for Evidence Building and the National AI Research Resources Task Force.


Angela Tombari is a the Director of Research Partnerships & Chief Methodologist at the Kentucky Center for Statistics.


Frauke Kreuter is is the Professor of Statistics and Data Science in Social Sciences and the Humanities at the Ludwig-Maximilians-University of Munich, Germany; Co-director of the Social Data Science Center (SoDa), and faculty member in the Joint Program in Survey Methodology (JPSM) at the University of Maryland, USA; and until recently head of the statistical methods group at the Institute for Employment Research (IAB) in Nuremberg, Germany. In addition to her academic work, Frauke Kreuter is Co-Founder of the Coleridge Initiative, whose goal is to accelerate data-driven research and policy around human beings and their interactions for program management,and Co-Founder of the German language podcast Dig Deep.





Table of Contents

    • Preface
    • Welcome
      • Background
      • Structure
      • Target Audience
    • Project Scoping
      • Developing a Theory of Change
      • Example: Education to Workforce
      • Planning
    • Data Acquisition
      • Finding the right data
      • Relevance
      • Timeliness
      • Accessibility
      • Additional Ethical Considerations
      • Approaches for Sharing Restricted Use Data
      • Best Practices Before Data Exploration
    • Data Discovery and Management
      • Understanding Data
      • Data Discovery
      • Data Exploration
      • Data Management
      • A note of caution: Data quality challenges
    • Data Linkage
      • The Problem
      • Data Linkage is Critical for Evidence Building
      • Examples of Data Linkage Initiatives in Administration
      • Why Data Linkage is difficult
      • Importance of Preprocessing
      • How Linkage Works: Rules, People, and Computers
      • Errors in Data Linkage
    • Measurement
      • Mapping policy concepts to measures
      • Why does measurement matter?
      • Example: Measuring Employment
      • Example: Measurement of Education
      • Typical Errors During Measurement
    • Visualization
      • Getting started: The grammar of graphics
      • Choosing the right visualization type
      • Best Practices
      • Accessibility in Data Visualization
      • Summary and SUCCESS
    • Data Description
      • Importance of data description
      • Descriptive Measures: Mean, Median, Variance, Standard Deviation, and Interquartile Range
      • Summary
    • Data Analytics and Prediction
      • Predictive Analytics
      • Machine Learning
    • (Causal) Inference
      • Correlation and causation
      • A very basic intro to causal models
      • Example: Building a “Simple” Causal Model
      • More real-world problems
    • Supplemental Project Template
      • Project Scoping
      • Data Acquisition
      • Data Exploration and Management
      • Data Linkage
      • Measurement
      • Visualization and Data Description
      • Advanced* Analytics and Prediction/Supervised ML
      • Inference
      • Bias and Ethics
      • Communicating Results

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