Trackademia

Welcome to Trackademia

The idea behind Trackademia is to build on existing academic databases like Google Scholar and OpenAlex so that you can trust the data and use it for whatever you want. That comes down to two things.

More data, and more ways to use it. We aggregate publication data from every major source into a single dataset and add information pulled from faculty CVs. Then we let you slice it however you want: choose which sources to build from, write your own formulas, weight journals, adjust for department size.

More accurate, and honest about where it isn’t. We focus on a smaller set of schools so we can verify everything carefully. Faculty lists come from department websites rather than being inferred from publication records, so we know who should be in the data before we go looking. When we can’t find data on someone, we record the gap instead of dropping them. Every number comes with a coverage rate you can check against ground truth.

Everything in Trackademia is built from seven units. AI merges duplicate records across sources and tags each publication so these categories hold up:

  • Publication — citation counts, authors, journal, peer review status, and more
  • Publication group — versions of the same work combined into one entity, such as a working paper and the article it became
  • Academic
  • Department
  • University
  • Field
  • Journal

There are two modes. In Database mode you browse the underlying records and filter by any of the seven units. In Analytics mode you get graphs and comparisons, and you switch views to compare two departments, compare one department against the average of the others, or see them all at once. Tabs let you focus on a particular level of the data.

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Journal

Political Analysis

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Citation weight

TierA
Weight in force
Weightingoff

Weight inputs

Each figure with its percentile among the ranked journals. A is the median, over the academics with a work here, of that academic’s citations to the works this site counts for them - never a Google Scholar profile total. Over every academic of this field, all years, whatever the filters select.

A · academics’ median6,583.5 · 56%
B · citations per work378.8 · 94%
N · works141 · 87%
P · academics50 · 83%
Formula result1.107

From this institution

Under these filters. A work with no citation count is unknown here, never a zero.

Works139
Citations50,760 over 134 of 139 works
Citations per counted work378.8
Weighted citations
Academics50
Departments8
First seen1991

Works placed per year

138 of these 139 works carry a year; an undated work is in no year. Works placed, under these filters.

Departments publishing there 8

DepartmentWorks
Harvard Government54
Columbia Political Science27
Princeton Politics23
Yale Political Science23
Penn Political Science12
Cornell Government5
Dartmouth Government4
Brown Political Science3

A work held by two departments counts once in each, so these add up to more than the works above.

Works placed there 139

YearTitleAcademicsCitations
2001Logistic Regression in Rare Events DataGary King6,552
2007Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal InferenceGary King · Kosuke Imai6,021
2012Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical TurkGregory Alain Huber5,882
2011Causal Inference Without Balance Checking: Coarsened Exact MatchingGary King5,362
2019Why Propensity Scores Should Not Be Used for MatchingGary King2,813
2014Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference ExperimentsDaniel J. Hopkins2,707
2019How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice.Jonathan Mummolo1,458
2021On the Use of Two-way Fixed Effects Regression Models for Causal Inference with Panel DataKosuke Imai1,104
2012Statistical Analysis of List ExperimentsKosuke Imai923
2018Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About ItArthur Spirling913
2015Computer Assisted Text Analysis for Comparative PoliticsDustin Tingley833
2006The Dangers of Extreme CounterfactualsGary King822
2015Geographic Boundaries as Regression DiscontinuitiesRocío Titiunik679
2004Bayesian multilevel estimation with poststratification: state-level estimates from national pollsAndrew Gelman663
2014Does Survey Mode Still Matter? Findings from a 2010 Multi-Mode ComparisonStephen Daniel Ansolabehere662
2013Identification and Sensitivity Analysis for Multiple Causal Mechanisms: Revisiting Evidence from Framing ExperimentsKosuke Imai601
2007Comparing Incomparable Survey Responses: New Tools for Anchoring VignettesGary King594
2012Validation: What Big Data Reveals About Survey Misreporting and the Real ElectorateStephen Daniel Ansolabehere568
2015How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About ItGary King484
2019A Note on Dropping Experimental Subjects who Fail a Manipulation CheckP. M. Aronow419
2016Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration RecordKosuke Imai374
2018The Number of Choice Tasks and Survey Satisficing in Conjoint ExperimentsDaniel J. Hopkins373
2005Practical issues in implementing and understanding Bayesian ideal point estimationAndrew Gelman352
2014Measuring TransparencyJames Raymond Vreeland350
2018A Note on Listwise Deletion versus Multiple ImputationThomas B. Pepinsky281
2002Estimating Dynamic Panel Models in Political ScienceGregory J. Wawro268
1999No Evidence on Directional vs. Proximity VotingGary King262
2005Institutional context, cognitive resources and party attachments across democraciesJohn D. Huber255
2017Estimating heterogeneous treatment effects and the effects of heterogeneous treatments with ensemble methodsSean J. Westwood252
2010Bayesian Model Averaging: Theoretical Developments and Practical ApplicationsBrendan Nyhan246
2023Using Conjoint Experiments to Analyze Elections: The Essential Role of the Average Marginal Component EffectDaniel J. Hopkins236
2011Statistical Analysis of Endorsement Experiments: Measuring Support for Militant Groups in PakistanKosuke Imai235
2021Improving the External Validity of Conjoint Analysis: The Essential Role of Profile DistributionKosuke Imai230
2000Estimating Legislators’ Preferred Points.John Benedict Londregan229
2006Comparing Experimental and Matching Methods using a Large-Scale Voter Mobilization ExperimentAlan S. Gerber · Donald P. Green218
2011Estimation of Heterogeneous Treatment Effects from Randomized Experiments, with Application to the Optimal Planning of the Get-out-the-vote CampaignKosuke Imai200
2015Cluster-Robust Variance Estimation for Dyadic DataP. M. Aronow200
2009The Microfoundations of Polarization.Matthew Levendusky199
2007UK OC OK? Interpreting Optimal Classification Scores for the U.K. House of CommonsArthur Spirling192
2002The Experimental Method in Political ScienceROSE McDERMOTT191
2015Fuzzy Sets on Shaky Ground? Parametric and Specification Sensitivity in fsQCA.Donghyun Danny Choi187
2018Classification Accuracy as a Substantive Quantity of Interest: Measuring Polarization in Westminster SystemsArthur Spirling185
2016The Influence of Emotion on TrustDustin Tingley182
2019A Theory of Statistical Inference for Matching Methods in Causal ResearchGary King178
2001Testing for Publication Bias in Political ScienceAlan S. Gerber · Donald P. Green176
2018Measuring Voters’ Multidimensional Policy Preferences with Conjoint Analysis: Application to Japan’s 2014 ElectionDaniel M. Smith171
2008Analysis of Cluster-Randomized Experiments: A Comparison of Alternative Estimation ApproachesDonald P. Green162
2004Political Learning from Rare Events: Poisson Inference, Fiscal Constraints, and the Lifetime of BureausDaniel Paul Carpenter156
2009Testing the Accuracy of Regression Discontinuity Analysis Using Experimental BenchmarksAlan S. Gerber · Donald P. Green153
2014Multiple imputation for continuous and categorical data: Comparing joint and conditional ap- proachesAndrew Gelman151
2017The Design of Field Experiments With Survey Outcomes: A Framework for Selecting More Efficient, Robust, and Ethical DesignsJoshua Kalla140
2015Measure for Measure: An Experimental Test of Online Political Media ExposureAndrew M. Guess140
2003Roll Calls, Party Labels, and ElectionsJames M. Snyder, Jr. · Michael M. Ting130
1991On Political MethodologyGary King125
2018Emotional Arousal Predicts Voting on the U.S. Supreme CourtRyan D. Enos122
2013Beyond LATE: Estimation of the Average Treatment Effect with an Instrumental VariableAllison Carnegie · P. M. Aronow122
2001Aggregation Among Binary, Count, and Duration Models: Estimating the Same Quantities from Different Levels of DataGary King118
2001Agenda constrained legislator ideal Points and the spatial voting modelAdam Meirowitz116
2013Asking About Numbers: Why and HowMarc Meredith · Stephen Daniel Ansolabehere115
2017Sparse Estimation and Uncertainty with Application to Subgroup AnalysisDustin Tingley107
2015Using the Predicted Responses from List Experiments as Explanatory Variables in Regression ModelsKosuke Imai94
2019List Experiments with Measurement ErrorKosuke Imai93
2003Integrating roll call analysis and voting theory: A frameworkAdam Meirowitz88
2016Cause or Effect? Turnout in Hispanic Majority-Minority DistrictsRocío Titiunik87
2020Measuring the Competitiveness of ElectionsDaniel M. Smith86
2014Experiments to Reduce the Over-reporting of Voting: A Pipeline to the TruthIsmail K. White84
2002The Downstream Benefits of ExperimentationAlan S. Gerber · Donald P. Green81
2020Active Learning Approaches for Labeling Text: Review and Assessment of the Performance of Active Learning ApproachesBlake Miller79
2002A Fast, Easy, and Efficient Estimator for Multiparty Electoral DataGary King78
2010Bayesian combination of state polls and election forecastsAndrew Gelman78
2005Two-stage regression and multilevel modeling: a commentaryAndrew Gelman75
2016Coarsening bias: How instrumenting for coarsened treatments upwardly biases instrumental variable estimatesJohn Marshall72
2002A Statistical Model of Bilateral CooperationJames Raymond Vreeland71
1998An Empirical Spatial Model of Congressional CampaignsNolan McCarty68
2023Statistically Valid Inferences from Differentially Private Data Releases, with Application to the Facebook URLs DatasetGary King66
2003A Consensus on Second Stage Analyses in Ecological Inference ModelsGary King62
2018Estimating Spatial Preferences from Votes and TextJohn Benedict Londregan61
2008Bayesian and Likelihood Ecological Inference for 2 × 2 Tables: An Incomplete Data ApproachKosuke Imai60
2002Reconciling Individual and Aggregate Evidence Concerning Partisan Stability: Applying Time-Series Models to Panel Survey DataDonald P. Green59
2002Another Look at the Measurement of Political KnowledgeJason Barabas51
2010Baseline, Placebo, and Treatment: Efficient Estimation for Three-Group ExperimentsAlan S. Gerber · Donald P. Green50
2007Bayesian Approaches for Limited Dependent Variable Change Point ProblemsArthur Spirling49
2016Retrospective Causal Inference with Machine Learning Ensembles: An Application to Anti-Recidivism Policies in ColombiaSarah Zukerman Daly47
2021Spikes and Variance: Using Google Trends to Detect and Forecast Protests.Erik Wibbels45
2020Voter registration databases and MRP: Toward the use of large scale databases in public opinion researchAndrew Gelman43
2022An Improved Method of Automated Nonparametric Content Analysis for Social ScienceGary King41
2021Placebo Selection In Survey Experiments: An Agnostic Approach.Yamil Ricardo Velez41
2013Preregistration of studies and mock reportsAndrew Gelman39
2024Sensitivity analysis for survey weightsMelody Huang38
2009Apportionment Cycles as Natural ExperimentsMarc Meredith37
2023Automated Coding of Political Campaign Advertisement Videos: An Empirical Validation StudyKosuke Imai36
2024Using Machine Learning to Test Causal Hypotheses in Conjoint AnalysisKosuke Imai35
2000The Time to Give: PAC Motivations and Electoral TimingNolan McCarty34
1993Issues and the Dynamics of Party Identification: A Methodological CritiqueDonald P. Green33
2022Listwise Deletion in High DimensionsP. M. Aronow31
2001Nonseparable Preferences, Measurement Error, and Unstable Survey ResponsesDean Lacy30
2017Combining Double Sampling and Bounds to Address Non-ignorable Missing Outcomes in Randomized ExperimentsAlan S. Gerber · Donald P. Green30
2025Priming bias versus post-treatment bias in experimental designsKosuke Imai28
2023Dyadic Clustering in International RelationsP. M. Aronow28
2023Validating the Applicability of Bayesian Inference with Surname and Geocoding to Congressional RedistrictingKevin DeLuca28
2003Analyzing Second Stage Ecological RegressionsGary King26
2015Estimating the Severity of the WikiLeaks US Diplomatic Cables DisclosureArthur Spirling26
1996The Generalization in the Generalized Event Count Model, With Comments on Achen, Amato, and LondreganGary King24
2002Interpreting the Coefficient of Party Influence: Comment on KrehbielJames M. Snyder, Jr.24
1991The Effects of Measurement Error on Two-Stage, Least Squares EstimatesDonald P. Green21
2016Research Note: A More Powerful Test Statistic for Reasoning about Interference between UnitsP. M. Aronow20
2023Blocks as Geographic Discontinuities: The Effect of Polling Place Assignment on VotingMarc Meredith20
1995A Correction for an Underdispersed Event Count Probability DistributionGary King18
2024Multilanguage Word Embeddings For Social Science: Estimation, Inference and Validation Resources for 157 LanguagesArthur Spirling18
2001Party Labels, Roll-Call Votes, and ElectionsJames M. Snyder, Jr.17
G. Porro (2012)“Causal Inference Without Balance Checking: Coarsened Exact Matching.”Gary King16
2020A General Model of Author “Style” with Application to the UK House of Commons, 1935–2018Arthur Spirling16
2024A Partisan Solution to Partisan Gerrymandering: The Define Combine ProcedureKevin DeLuca15
2005Parties in elections, parties in government, and partisan biasAdam Meirowitz15
2002Isolating Spatial Autocorrelation, Aggregation Bias, and Distributional Violations in Ecological InferenceGary King13
2011Introduction to the Virtual Issue: Past and Future Research Agenda on Causal InferenceKosuke Imai12
2025Categorizing topics versus inferring attitudes: a theory and method for analyzing open-ended survey responses.William R. Hobbs11
2025Crowdsourced Adaptive SurveysYamil Ricardo Velez9
2012Vetoes, Bargaining, and Boundary ConditionsCharles M. Cameron8
2023The Essential Role of Statistical Inference in Evaluating Electoral Systems: A Response to DeFord et al.Gary King7
2025Measuring Distances in High Dimensional Spaces Why Average Group Vector Comparisons Exhibit Bias, And What to Do About itArthur Spirling · William R. Hobbs7
2025Nationally Representative, Locally Misaligned: The Biases of Generative Artificial Intelligence in Neighborhood PerceptionMelissa Lee Sands7
2025A Statistical Model of Bipartite Networks: Application to Cosponsorship in the United States SenateKosuke Imai6
2015Explaining Systematic Bias and Nontransparency in US Social Security Administration ForecastsGary King6
2002A Practical Statistical Model for Multiparty Electoral DataGary King6
1996Some Remarks on the ‘Generalized Event Count’ Distribution.John Benedict Londregan4
2018Estimating ideal points from votes and textJohn Benedict Londregan3
2009A Comment on Diagnostic Tools for Counterfactual InferenceNicholas Sambanis2
2006Ancillary Materials in Volume 14Alan S. Gerber · Donald P. Green2
2025Meaning Beyond Numbers: Introducing the Plot Staircase to Measure Graphical PreferencesMarkus Prior · Talbot M. Andrews2
2006Institutional context, cognitive resources and party attachments across democracies (vol 13, pg 365, 2006)John D. Huber2
2009Empirical versus Theoretical Claims about Extreme Counterfactuals: A ResponseGary King1
2026On the Foundations of the Design-Based Ap- proachP. M. Aronow1
2022Rejoinder: Concluding Remarks on Scholarly CommunicationsGary King0
2022Experimental Design and Statistical Inference for Conjoint Analysis: The Essential Role of Population DistributionKosuke Imai
2018EmotionalArousalPredictsVotingontheSupreme CourtRyan D. Enos
2009The Society for Political MethodologyGary King
2018Comments on Single-Blind Reviewing from the Editorial StaffDaniel J. Hopkins
2002Experimental Methodology in Political ScienceROSE McDERMOTT

Citations as counted, before any journal weight: 134 of these 139 works carry a count, and a work with no count is unknown, never a zero.