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.
Trackademia
Back to the list
Journal

Political Analysis

close

Citation weight

TierA
Weight in force1×
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.

Works51
Citations9,929 over 48 of 51 works
Citations per counted work206.9
Weighted citations—
Academics26
Departments7
First seen2017

Works placed per year

50 of these 51 works carry a year; an undated work is in no year. Works placed, under these filters.

Departments publishing there 7

DepartmentWorks
Harvard Government18
Princeton Politics9
Yale Political Science9
Penn Political Science8
Cornell Government5
Columbia Political Science4
Dartmouth Government1

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

Works placed there 51

YearTitleAcademicsCitations
2019Why Propensity Scores Should Not Be Used for MatchingGary King2,813
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
2018Text Preprocessing For Unsupervised Learning: Why It Matters, When It Misleads, And What To Do About ItArthur Spirling913
2019A Note on Dropping Experimental Subjects who Fail a Manipulation CheckP. M. Aronow419
2018The Number of Choice Tasks and Survey Satisficing in Conjoint ExperimentsDaniel J. Hopkins373
2018A Note on Listwise Deletion versus Multiple ImputationThomas B. Pepinsky281
2017Estimating heterogeneous treatment effects and the effects of heterogeneous treatments with ensemble methodsSean J. Westwood252
2023Using Conjoint Experiments to Analyze Elections: The Essential Role of the Average Marginal Component EffectDaniel J. Hopkins236
2021Improving the External Validity of Conjoint Analysis: The Essential Role of Profile DistributionKosuke Imai230
2018Classification Accuracy as a Substantive Quantity of Interest: Measuring Polarization in Westminster SystemsArthur Spirling185
2019A Theory of Statistical Inference for Matching Methods in Causal ResearchGary King178
2018Measuring Voters’ Multidimensional Policy Preferences with Conjoint Analysis: Application to Japan’s 2014 ElectionDaniel M. Smith171
2017The Design of Field Experiments With Survey Outcomes: A Framework for Selecting More Efficient, Robust, and Ethical DesignsJoshua Kalla140
2018Emotional Arousal Predicts Voting on the U.S. Supreme CourtRyan D. Enos122
2017Sparse Estimation and Uncertainty with Application to Subgroup AnalysisDustin Tingley107
2019List Experiments with Measurement ErrorKosuke Imai93
2020Measuring the Competitiveness of ElectionsDaniel M. Smith86
2020Active Learning Approaches for Labeling Text: Review and Assessment of the Performance of Active Learning ApproachesBlake Miller79
2023Statistically Valid Inferences from Differentially Private Data Releases, with Application to the Facebook URLs DatasetGary King66
2018Estimating Spatial Preferences from Votes and TextJohn Benedict Londregan61
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
2024Sensitivity analysis for survey weightsMelody Huang38
2023Automated Coding of Political Campaign Advertisement Videos: An Empirical Validation StudyKosuke Imai36
2024Using Machine Learning to Test Causal Hypotheses in Conjoint AnalysisKosuke Imai35
2022Listwise Deletion in High DimensionsP. M. Aronow31
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
2023Blocks as Geographic Discontinuities: The Effect of Polling Place Assignment on VotingMarc Meredith20
2024Multilanguage Word Embeddings For Social Science: Estimation, Inference and Validation Resources for 157 LanguagesArthur Spirling18
—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
2025Categorizing topics versus inferring attitudes: a theory and method for analyzing open-ended survey responses.William R. Hobbs11
2025Crowdsourced Adaptive SurveysYamil Ricardo Velez9
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
2018Estimating ideal points from votes and textJohn Benedict Londregan3
2025Meaning Beyond Numbers: Introducing the Plot Staircase to Measure Graphical PreferencesMarkus Prior · Talbot M. Andrews2
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—
2018Comments on Single-Blind Reviewing from the Editorial StaffDaniel J. Hopkins—

Citations as counted, before any journal weight: 48 of these 51 works carry a count, and a work with no count is unknown, never a zero.