Evidence map›Paper›PMID 40518614›Full record

ArticlePsychometrika2025

Bayesian Rank-Clustering.

Michael Pearce, Elena A Erosheva

Abstract read
In one paragraph

Article in Psychometrika, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Michael PearceDepartment of Mathematics and Statistics, https://ror.org/00cvxb145Reed College, Portland, OR, USA.ORCID https://orcid.org/0000-0002-9313-271X
Elena A EroshevaDepartment of Statistics, School of Social Work, and the Center for Statistics and the Social Sciences, https://ror.org/00a6ram87University of Washington, Seattle, WA, USA.ORCID https://orcid.org/0000-0003-2162-0017

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article proposes a new statistical model to infer interpretable population-level preferences from ordinal comparison data. Such data is ubiquitous, e.g., ranked choice votes, top-10 movie lists, and pairwise sports outcomes. Traditional statistical inference on ordinal comparison data results in an overall ranking of objects, e.g., from best to worst, with each object having a unique rank. However, the ranks of some objects may not be statistically distinguishable. This could happen due to insufficient data or to the true underlying object qualities being equal. Because uncertainty communication in estimates of overall rankings is notoriously difficult, we take a different approach and allow groups of objects to have equal ranks or be

Indexed as

Bradley–Terryfusion priorsitem indifferencePlackett–Lucerank aggregationspike-and-slab

Identifiers

PMID40518614
PMCPMC12483714

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.