Chapter 33 Further Reading

Campaign Data and Analytics

Issenberg, Sasha. The Victory Lab: The Secret Science of Winning Campaigns. Crown, 2012. The canonical popular account of the revolution in campaign data analytics, from the first field experiments in voter mobilization through the data-driven campaigns of the Obama era. Essential background for understanding where the tools in this chapter came from and why they were developed.

Hersh, Eitan D. Hacking the Electorate: How Campaigns Perceive Voters. Cambridge University Press, 2015. A political scientist's analysis of how campaigns use voter file data to make decisions about which voters to target. Particularly relevant for this chapter's discussion of what support scores and persuadability scores actually measure and what they miss.

Green, Donald P., and Alan S. Gerber. Get Out the Vote: How to Increase Voter Turnout. 4th ed. Brookings Institution Press, 2019. The foundational text on the experimental evidence about what voter contact methods actually increase turnout. Every campaign data analyst should understand the research base behind canvassing, phone banking, and mail programs.

Gerber, Alan S., and Donald P. Green. "The Effects of Canvassing, Telephone Calls, and Direct Mail on Voter Turnout: A Field Experiment." American Political Science Review, 2000. The paper that launched the modern field experiment tradition in campaign research. Reading the original article helps contextualize the KPI metrics used in this chapter — the conversion rates and mobilization effects are grounded in this experimental literature.

Python for Data Analysis

McKinney, Wes. Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter. 3rd ed. O'Reilly, 2022. The authoritative reference for pandas, written by its creator. The chapter on time series analysis and the cleaning/transformation chapters are directly relevant to the voter file pipeline built in this chapter.

VanderPlas, Jake. Python Data Science Handbook: Essential Tools for Working with Data. O'Reilly, 2016. Comprehensive reference for the scientific Python ecosystem (NumPy, pandas, matplotlib, scikit-learn). The matplotlib chapter provides the foundation for the visualization work in examples 01 and 02.

Plotly Documentation. plotly.com/python. The official Plotly Python documentation is well-maintained and includes extensive examples. The "Plotly Express" and "Graph Objects" sections cover the tools used in example 03.

Dashboard Design

Few, Stephen. Show Me the Numbers: Designing Tables and Graphs to Enlighten. 2nd ed. Analytics Press, 2012. The standard reference for data visualization principles applied to analytical dashboards. Few's critique of "chartjunk" and his principles for effective visual encoding directly inform the design decisions in this chapter's dashboard.

Knaflic, Cole Nussbaumer. Storytelling with Data: A Data Visualization Guide for Business Professionals. Wiley, 2015. Practical guidance on using data visualization to tell clear analytical stories to non-technical audiences — exactly what Nadia needs to do when presenting the dashboard to Yolanda Torres.

Shneiderman, Ben. "The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations." Proceedings of the IEEE Symposium on Visual Languages, 1996. The foundational paper on information visualization design, introducing the "overview first, zoom and filter, then details on demand" principle that directly maps to the three-tier dashboard architecture in this chapter.

Algorithmic Fairness and Equity

O'Neil, Cathy. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown, 2016. The most widely read critique of algorithmic systems that embed and amplify bias. Chapter 9, on political data and voter targeting, is directly relevant to this chapter's discussion of model calibration and equity weighting.

Chouldechova, Alexandra. "Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments." Big Data, 2017. A rigorous analysis of how predictive models can be simultaneously accurate in aggregate and systematically miscalibrated for specific subgroups. The technical framework applies directly to the support score calibration problem documented by Sam Harding in Case Study 2.

Obermeyer, Ziad, Brian Powers, Christine Vogeli, and Sendhil Mullainathan. "Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations." Science, 2019. Perhaps the clearest empirical demonstration of how algorithmic predictions trained on available data can encode systematic racial bias even when race is not an input variable. Essential reading for anyone building or using predictive models that affect public wellbeing.

Benjamin, Ruha. Race After Technology: Abolitionist Tools for the New Jim Code. Polity, 2019. Sociological analysis of how technological systems reproduce racial inequality, with implications for campaign analytics tools. Benjamin's concept of "discriminatory design" is relevant to the ODA calibration problem.

Voter File and Election Data Resources

Catalist. catalist.us. The data vendor that provides voter file data, modeling, and analytics to Democratic campaigns and progressive organizations. Understanding how commercial voter file vendors build and sell their data is background for understanding what ODA's open-source framework is providing as an alternative.

L2 Political. l2political.com. A non-partisan voter data vendor providing voter file data and modeling to campaigns across the political spectrum. Useful for understanding the commercial landscape.

OpenSecrets Campaign Finance Data. opensecrets.org. Campaign spending data that can be used to contextualize a campaign's field investment relative to comparable races.

MIT Election Data and Science Lab. electionlab.mit.edu. Repository of election research, data, and analysis, including geographic election results data useful for contextualizing campaign geography decisions.

Field Operations

Fieldworks. fieldworks.com. Campaign field organizing vendor whose documentation on canvassing protocols, VAN integration, and walk list optimization provides practical context for the technical tools developed in this chapter.

NGP VAN. ngpvan.com. The dominant field organizing platform for Democratic campaigns. Understanding VAN's data structure — how contacts are logged, how walk lists are generated, how results are entered — is practical context for the data pipeline concepts in this chapter.

Analyst Institute. analystinstitute.org. A progressive research organization that conducts and publishes research on field organizing methods, including experimental studies of different canvassing and phone banking approaches. Their research findings are the empirical foundation for many of the assumptions embedded in voter contact prioritization models.