Data Literacy and Evidence Building
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.
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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