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

The American Statistician

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’ median27,607.0 · 94%
B · citations per work231.8 · 82%
N · works27 · 23%
P · academics3 · 4%
Formula result1.265

From this institution

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

Works27
Citations5,562 over 24 of 27 works
Citations per counted work231.8
Weighted citations—
Academics3
Departments3
First seen1991

Works placed per year

27 of these 27 works carry a year; an undated work is in no year. Works placed, under these filters.

Departments publishing there 3

DepartmentWorks
Columbia Political Science25
Penn Political Science1
Princeton Politics1

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

Works placed there 27

YearTitleAcademicsCitations
2006The difference between “significant” and “not significant” is not itself statistically significantAndrew Gelman1,701
2018R-squared for Bayesian regression modelsAndrew Gelman1,207
1998Markov chain Monte Carlo in practice: A roundtable discussionAndrew Gelman1,012
2009Splitting a predictor at the upper quarter or third and the lower quarter or thirdAndrew Gelman294
2002Let’s practice what we preach: Turning tables into graphsAndrew Gelman293
2017The Perils of Balance Testing in Experimental Design: Messy Analyses of Clean DataDiana C. Mutz256
2019Large scale replication projects in contemporary psychological researchAndrew Gelman149
2016The problems with p-values are not just with p-valuesAndrew Gelman145
1991A note on bivariate distributions that are conditionally normalAndrew Gelman83
2002You can load a die but you can’t bias a coinAndrew Gelman77
2006The boxer, the wrestler, and the coin flip: A paradox of robust Bayesian inference and belief functionsAndrew Gelman57
2013“Not only defended but also applied”: The perceived absurdity of Bayesian inference (with discussion and rejoinder)Andrew Gelman55
2019Multiple perspectives on inference for two simple statistical scenariosAndrew Gelman50
2005A course on teaching statistics at the university levelAndrew Gelman38
2022A proposal for informative default priors scaled by the standard error of estimatesAndrew Gelman36
2007‘Turning Points’ in Iraq: Reversible Jump Markov Chain Monte Carlo in Political ScienceArthur Spirling23
2024Causal quartets: Different ways to attain the same average treatment effectAndrew Gelman22
1998Student projects on statistical literacy and the mediaAndrew Gelman19
1998Some class-participation demonstrations for decision theory and Bayesian statisticsAndrew Gelman17
2008Teaching Bayesian applied statistics to graduate students in political science, sociology, public health, education, economics, . . .Andrew Gelman17
2023“Two truths and a lie” as a class-participation activityAndrew Gelman6
2015Moving forward in statistics education while avoiding overconfidence. Discussion of “Mere Renovation is Too Little Too Late: It’s Time to Rebuild the Undergraduate Curriculum from the Ground Up,” by George CobbAndrew Gelman3
2013Rejoinder: The Anti-Bayesian Moment and Its PassingAndrew Gelman2
2026The ladder of abstraction in statistical graphicsAndrew Gelman0
2009Discussion of “What is statistics,” by Emery Brown and Robert KassAndrew Gelman—
2006Gelman, A.(2006)," The Boxer, the Wrestler, and the Coin Flip: A Paradox of Robust Bayesian Inference and Belief Functions,"" The American Statistician," 60, 146-150: Comment …Andrew Gelman—
2006Gelman, A.(2006)," The boxer, the wrestler, and the coin flip: A paradox of robust Bayesian inference and belief functions," The American Statistician, 60, 146-150: ResponseAndrew Gelman—

Citations as counted, before any journal weight: 24 of these 27 works carry a count, and a work with no count is unknown, never a zero.