Justin Ryan Grimmer

All Publications

• Who Are the Election Skeptics? Evidence from the 2022 Midterm Elections ELECTION LAW JOURNAL Holliday, D. E., Grimmer, J., Lelkes, Y., Westwood, S. J. 2024

  View details for DOI 10.1089/elj.2024.0010

  View details for Web of Science ID 001316152600001

• Evaluating a New Generation of Expansive Claims about Vote Manipulation ELECTION LAW JOURNAL Grimmer, J., Herron, M. C., Tyler, M. 2024

  View details for DOI 10.1089/elj.2022.0070

  View details for Web of Science ID 001223971500001

• How Election Rules Affect Who Wins JOURNAL OF LEGAL ANALYSIS Grimmer, J., Hersh, E. 2024; 16 (1): 1-25

  View details for DOI 10.1093/jla/laae001

  View details for Web of Science ID 001197285800001

• Naive regression requires weaker assumptions than factor models to adjust for multiple cause confounding JOURNAL OF MACHINE LEARNING RESEARCH Grimmer, J., Knox, D., Stewart, B. M. 2023; 24

  View details for Web of Science ID 001111683500001

• Legislator Advocacy on Behalf of Constituents and Corporate Donors: A Case Study of the Federal Energy Regulatory Commission ACCOUNTABILITY RECONSIDERED Powell, E., Judge-Lord, D., Grimmer, J., Cameron, C. M., Canes-Wrone, B., Gordon, S. C., Huber, G. A. 2023: 265-294

  View details for Web of Science ID 001064359800012

• How to make causal inferences using texts. Science advances Egami, N., Fong, C. J., Grimmer, J., Roberts, M. E., Stewart, B. M. 2022; 8 (42): eabg2652

  Abstract

  Text as data techniques offer a great promise: the ability to inductively discover measures that are useful for testing social science theories with large collections of text. Nearly all text-based causal inferences depend on a latent representation of the text, but we show that estimating this latent representation from the data creates underacknowledged risks: we may introduce an identification problem or overfit. To address these risks, we introduce a split-sample workflow for making rigorous causal inferences with discovered measures as treatments or outcomes. We then apply it to estimate causal effects from an experiment on immigration attitudes and a study on bureaucratic responsiveness.

  View details for DOI 10.1126/sciadv.abg2652

  View details for PubMedID 36260669

• Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS Feder, A., Keith, K. A., Manzoor, E., Pryzant, R., Sridhar, D., Wood-Doughty, Z., Eisenstein, J., Grimmer, J., Reichart, R., Roberts, M. E., Stewart, B. M., Veitch, V., Yang, D. 2022; 10: 1138-1158

  View details for DOI 10.1162/tacl_a_00511

  View details for Web of Science ID 000923425200002

• Reply to Kalmoe and Mason: The pitfalls of using surveys to measure low-prevalence attitudes and behavior. Proceedings of the National Academy of Sciences of the United States of America Westwood, S. J., Grimmer, J., Tyler, M., Nall, C. 2022; 119 (32): e2207584119

  View details for DOI 10.1073/pnas.2207584119

  View details for PubMedID 35878008

• Partisan Enclaves and Information Bazaars: Mapping Selective Exposure to Online News JOURNAL OF POLITICS Tyler, M., Grimmer, J., Iyengar, S. 2022

  View details for DOI 10.1086/716950

  View details for Web of Science ID 000752056100003

• Current research overstates American support for political violence. Proceedings of the National Academy of Sciences of the United States of America Westwood, S. J., Grimmer, J., Tyler, M., Nall, C. 2022; 119 (12): e2116870119

  Abstract

  SignificanceRecent political events show that members of extreme political groups support partisan violence, and survey evidence supposedly shows widespread public support. We show, however, that, after accounting for survey-based measurement error, support for partisan violence is far more limited. Prior estimates overstate support for political violence because of random responding by disengaged respondents and because of a reliance on hypothetical questions about violence in general instead of questions on specific acts of political violence. These same issues also cause the magnitude of the relationship between previously identified correlates and partisan violence to be overstated. As policy makers consider interventions designed to dampen support for violence, our results provide critical information about the magnitude of the problem.

  View details for DOI 10.1073/pnas.2116870119

  View details for PubMedID 35302889

• How Does the Rising Number of Women in the US Congress Change Deliberation? Evidence from House Committee Hearings QUARTERLY JOURNAL OF POLITICAL SCIENCE Ban, P., Grimmer, J., Kaslovsky, J., West, E. 2022; 17 (3): 355-387

  View details for DOI 10.1561/100.00020112

  View details for Web of Science ID 000910177200002

• No evidence for systematic voter fraud: A guide to statistical claims about the 2020 election. Proceedings of the National Academy of Sciences of the United States of America Eggers, A. C., Garro, H., Grimmer, J. 2021; 118 (45)

  Abstract

  After the 2020 US presidential election Donald Trump refused to concede, alleging widespread and unparalleled voter fraud. Trump's supporters deployed several statistical arguments in an attempt to cast doubt on the result. Reviewing the most prominent of these statistical claims, we conclude that none of them is even remotely convincing. The common logic behind these claims is that, if the election were fairly conducted, some feature of the observed 2020 election result would be unlikely or impossible. In each case, we find that the purportedly anomalous fact is either not a fact or not anomalous.

  View details for DOI 10.1073/pnas.2103619118

  View details for PubMedID 34728563

• Causal Inference with Latent Treatments AMERICAN JOURNAL OF POLITICAL SCIENCE Fong, C., Grimmer, J. 2021

  View details for DOI 10.1111/ajps.12649

  View details for Web of Science ID 000695052200001

• The durable differential deterrent effects of strict photo identification laws POLITICAL SCIENCE RESEARCH AND METHODS Grimmer, J., Yoder, J. 2022; 10 (3): 453-469

  View details for DOI 10.1017/psrm.2020.57

  View details for Web of Science ID 000792151300001

• Machine Learning for Social Science: An Agnostic Approach ANNUAL REVIEW OF POLITICAL SCIENCE, VOL 24, 2021 Grimmer, J., Roberts, M. E., Stewart, B. M., Levi, M., Rosenblum, N. L. 2021; 24: 395-419

  View details for DOI 10.1146/annurev-polisci-053119-015921

  View details for Web of Science ID 000652490700019

• Political cultures: measuring values heterogeneity POLITICAL SCIENCE RESEARCH AND METHODS Blaydes, L., Grimmer, J. 2020; 8 (3): 571–79

  View details for DOI 10.1017/psrm.2019.43

  View details for Web of Science ID 000540277500013

• Cyberwar: How Russian Hackers and Trolls Helped Elect a President-What We Don't, Can't, and Do Know (Book Review) PUBLIC OPINION QUARTERLY Book Review Authored by: Grimmer, J. 2019; 83 (1): 159–63

  View details for DOI 10.1093/poq/nfy049

  View details for Web of Science ID 000469811800009

• Mirrors for Princes and Sultans: Advice on the Art of Governance in the Medieval Christian and Islamic Worlds JOURNAL OF POLITICS Blaydes, L., Grimmer, J., McQueen, A. 2018; 80 (4): 1150–67

  View details for DOI 10.1086/699246

  View details for Web of Science ID 000445660100009

• Obstacles to Estimating Voter ID Laws' Effect on Turnout JOURNAL OF POLITICS Grimmer, J., Hersh, E., Meredith, M., Mummolo, J., Nall, C. 2018; 80 (3): 1045–51

  View details for DOI 10.1086/696618

  View details for Web of Science ID 000436308500032

• Money in Exile: Campaign Contributions and Committee Access JOURNAL OF POLITICS Powell, E. N., Grimmer, J. 2016; 78 (4): 974-988

  View details for DOI 10.1086/686615

  View details for Web of Science ID 000384884200024

• Discovery of Treatments from Text Corpora Fong, C., Grimmer, J., Erk, K., Smith, N. A. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2016: 1600-1609

  View details for Web of Science ID 000493806800151

• We Are All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work Together PS-POLITICAL SCIENCE & POLITICS Grimmer, J. 2015; 48 (1): 80-83

  View details for DOI 10.1017/S1049096514001784

  View details for Web of Science ID 000347159500028

• Elevated threat levels and decreased expectations: How democracy handles terrorist threats POETICS Bonilla, T., Grimmer, J. 2013; 41 (6): 650-669

  View details for DOI 10.1016/j.poetic.2013.06.003

  View details for Web of Science ID 000329558200005

• Congressmen in Exile: The Politics and Consequences of Involuntary Committee Removal JOURNAL OF POLITICS Grimmer, J., Powell, E. N. 2013; 75 (4): 907-920

  View details for DOI 10.1017/S0022381613000704

  View details for Web of Science ID 000325771600017

• Multinomial Inverse Regression for Text Analysis Comment JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION Grimmer, J. 2013; 108 (503): 770-771

  View details for DOI 10.1080/01621459.2013.822383

  View details for Web of Science ID 000325782300002

• Appropriators not Position Takers: The Distorting Effects of Electoral Incentives on Congressional Representation AMERICAN JOURNAL OF POLITICAL SCIENCE Grimmer, J. 2013; 57 (3): 624-642

  View details for DOI 10.1111/ajps.12000

  View details for Web of Science ID 000321109300008

• Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts POLITICAL ANALYSIS Grimmer, J., Stewart, B. M. 2013; 21 (3): 267-297

  View details for DOI 10.1093/pan/mps028

  View details for Web of Science ID 000321825000001

• How Words and Money Cultivate a Personal Vote: The Effect of Legislator Credit Claiming on Constituent Credit Allocation AMERICAN POLITICAL SCIENCE REVIEW Grimmer, J., Messing, S., Westwood, S. J. 2012; 106 (4): 703-719

  View details for DOI 10.1017/S0003055412000457

  View details for Web of Science ID 000311519300002

• General purpose computer-assisted clustering and conceptualization PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Grimmer, J., King, G. 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

• An Introduction to Bayesian Inference via Variational Approximations POLITICAL ANALYSIS Grimmer, J. 2011; 19 (1): 32-47

  View details for DOI 10.1093/pan/mpq027

  View details for Web of Science ID 000286572100003

• Approval regulation and endogenous consumer confidence: Theory and analogies to licensing, safety, and financial regulation REGULATION & GOVERNANCE Carpenter, D., Grimmer, J., Lomazoff, E. 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