Bio


Justin Grimmer is an associate professor of political science at Stanford University. His research examines how representation occurs in American politics using new statistical methods. His first book Representational Style in Congress: What Legislators Say and Why It Matters (Cambridge University Press, 2013) shows how senators define the type of representation they provide constituents and how this affects constituents' evaluations. His second book The Impression of Influence: How Legislator Communication and Government Spending Cultivate a Personal Vote (Under Review, with Sean J. Westwood and Solomon Messing) demonstrates how legislators ensure they receive credit for government actions. His work has appeared in the American Political Science Review, American Journal of Political Science, Journal of Politics, Political Analysis, Proceedings of the National Academy of Sciences, Regulation and Governance, and Poetics. During the 2013-2014 academic year he was a National Fellow at the Hoover Institute.

Academic Appointments


2019-20 Courses


Stanford Advisees


  • Doctoral Dissertation Advisor (AC)
    Chloe Lim, Matthew Tyler
  • Doctoral Dissertation Co-Advisor (AC)
    Annie Franco
  • Doctoral (Program)
    Katie Clayton, Sandy Handan-Nader, Shadie Khubba

All Publications


  • 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

  • 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
  • Elevated threat levels and decreased expectations: How democracy handles terrorist threats POETICS Bonilla, T., Grimmer, J. 2013; 41 (6): 650-669
  • Congressmen in Exile: The Politics and Consequences of Involuntary Committee Removal JOURNAL OF POLITICS Grimmer, J., Powell, E. N. 2013; 75 (4): 907-920
  • 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
  • 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