
Justin Ryan Grimmer
Professor of Political Science and Senior Fellow at the Hoover Institution
Web page: http://www.justingrimmer.org
Bio
Justin Grimmer is a Professor in Stanford University's Department of Political Science. His primary research interests include Congress, representation, and political methodology.
2021-22 Courses
- American Political Institutions
POLISCI 420A (Win) - How to Write and Publish a Quantitative Political Science Paper
POLISCI 462 (Spr) - Political Methodology III: Model-Based Inference
POLISCI 450C (Spr) - The Science of Politics
POLISCI 1 (Aut) - What's Wrong with American Government? An Institutional Approach
POLISCI 120Z (Sum) -
Independent Studies (6)
- Advanced Individual Study in Political Methodology
POLISCI 359 (Aut, Win, Spr, Sum) - Curricular Practical Training for PhD Students
POLISCI 309 (Sum) - Directed Reading and Research in American Politics
POLISCI 229 (Aut, Win, Spr, Sum) - Directed Reading and Research in American Politics
POLISCI 329 (Aut, Win, Spr, Sum) - Directed Reading and Research in Political Methodology
POLISCI 259 (Aut, Win, Spr, Sum) - Independent Studies in Ethics in Society
ETHICSOC 199 (Aut, Win)
- Advanced Individual Study in Political Methodology
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Prior Year Courses
2020-21 Courses
- Political Methodology III: Model-Based Inference
POLISCI 450C (Spr) - The Science of Politics
POLISCI 1 (Aut)
2019-20 Courses
- How to Write and Publish a Quantitative Political Science Paper
POLISCI 462 (Spr) - Political Methodology III: Model-Based Inference
POLISCI 450C (Spr) - Programming for Political Scientists
POLISCI 450X (Aut) - The Science of Politics
POLISCI 1 (Win) - What's Wrong with American Government? An Institutional Approach
POLISCI 120Z (Sum) - Workshop in Political Methodology
POLISCI 353A (Aut) - Workshop in Political Methodology
POLISCI 353B (Win)
2018-19 Courses
- Machine Learning with Application to Text as Data
POLISCI 452 (Spr) - Political Methodology III: Model-Based Inference
POLISCI 450C (Spr) - The Science of Politics
POLISCI 1 (Win) - What's Wrong with American Government? An Institutional Approach
POLISCI 120Z (Sum) - Workshop in Political Methodology
POLISCI 353A (Aut) - Workshop in Political Methodology
POLISCI 353B (Win) - Workshop in Political Methodology
POLISCI 353C (Spr)
- Political Methodology III: Model-Based Inference
Stanford Advisees
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Doctoral Dissertation Reader (AC)
Alejandra Aldridge, Apoorva Lal -
Postdoctoral Faculty Sponsor
Matthew Tyler -
Doctoral Dissertation Co-Advisor (AC)
Jason Luo -
Doctoral (Program)
Liam Bethlendy, Katie Clayton, Sandy Handan-Nader, Shadie Khubba, Kasey Rhee, Jennifer Wu
All Publications
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Cyberwar: How Russian Hackers and Trolls Helped Elect a President-What We Don't, Can't, and Do Know (Book Review)
PUBLIC OPINION QUARTERLY
2019; 83 (1): 159–63
View details for DOI 10.1093/poq/nfy049
View details for Web of Science ID 000469811800009
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Mirrors for Princes and Sultans: Advice on the Art of Governance in the Medieval Christian and Islamic Worlds
JOURNAL OF POLITICS
2018; 80 (4): 1150–67
View details for DOI 10.1086/699246
View details for Web of Science ID 000445660100009
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Obstacles to Estimating Voter ID Laws' Effect on Turnout
JOURNAL OF POLITICS
2018; 80 (3): 1045–51
View details for DOI 10.1086/696618
View details for Web of Science ID 000436308500032
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Money in Exile: Campaign Contributions and Committee Access
JOURNAL OF POLITICS
2016; 78 (4): 974-988
View details for DOI 10.1086/686615
View details for Web of Science ID 000384884200024
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We Are All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work Together
PS-POLITICAL SCIENCE & POLITICS
2015; 48 (1): 80-83
View details for DOI 10.1017/S1049096514001784
View details for Web of Science ID 000347159500028
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Elevated threat levels and decreased expectations: How democracy handles terrorist threats
POETICS
2013; 41 (6): 650-669
View details for DOI 10.1016/j.poetic.2013.06.003
View details for Web of Science ID 000329558200005
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Congressmen in Exile: The Politics and Consequences of Involuntary Committee Removal
JOURNAL OF POLITICS
2013; 75 (4): 907-920
View details for DOI 10.1017/S0022381613000704
View details for Web of Science ID 000325771600017
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Appropriators not Position Takers: The Distorting Effects of Electoral Incentives on Congressional Representation
AMERICAN JOURNAL OF POLITICAL SCIENCE
2013; 57 (3): 624-642
View details for DOI 10.1111/ajps.12000
View details for Web of Science ID 000321109300008
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Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
POLITICAL ANALYSIS
2013; 21 (3): 267-297
View details for DOI 10.1093/pan/mps028
View details for Web of Science ID 000321825000001
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How Words and Money Cultivate a Personal Vote: The Effect of Legislator Credit Claiming on Constituent Credit Allocation
AMERICAN POLITICAL SCIENCE REVIEW
2012; 106 (4): 703-719
View details for DOI 10.1017/S0003055412000457
View details for Web of Science ID 000311519300002
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General purpose computer-assisted clustering and conceptualization
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
2011; 108 (7): 2643-2650
Abstract
We develop a computer-assisted method for the discovery of insightful conceptualizations, in the form of clusterings (i.e., partitions) of input objects. Each of the numerous fully automated methods of cluster analysis proposed in statistics, computer science, and biology optimize a different objective function. Almost all are well defined, but how to determine before the fact which one, if any, will partition a given set of objects in an "insightful" or "useful" way for a given user is unknown and difficult, if not logically impossible. We develop a metric space of partitions from all existing cluster analysis methods applied to a given dataset (along with millions of other solutions we add based on combinations of existing clusterings) and enable a user to explore and interact with it and quickly reveal or prompt useful or insightful conceptualizations. In addition, although it is uncommon to do so in unsupervised learning problems, we offer and implement evaluation designs that make our computer-assisted approach vulnerable to being proven suboptimal in specific data types. We demonstrate that our approach facilitates more efficient and insightful discovery of useful information than expert human coders or many existing fully automated methods.
View details for DOI 10.1073/pnas.1018067108
View details for Web of Science ID 000287377000009
View details for PubMedID 21292983
View details for PubMedCentralID PMC3041127
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An Introduction to Bayesian Inference via Variational Approximations
POLITICAL ANALYSIS
2011; 19 (1): 32-47
View details for DOI 10.1093/pan/mpq027
View details for Web of Science ID 000286572100003
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Approval regulation and endogenous consumer confidence: Theory and analogies to licensing, safety, and financial regulation
REGULATION & GOVERNANCE
2010; 4 (4): 383-407
View details for DOI 10.1111/j.1748-5991.2010.01091.x
View details for Web of Science ID 000284486300001