Evidence map›Paper›PMID 41185548›Full record

ArticlePsychometrika2025

The Interval Consensus Model: Aggregating Continuous Bounded Interval Responses.

Matthias Kloft, Björn S Siepe, Daniel W Heck

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. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Matthias KloftDepartment of Psychology, https://ror.org/01rdrb571Philipps-Universität Marburg, Germany.ORCID https://orcid.org/0000-0003-1845-6957
Björn S SiepeDepartment of Psychology, https://ror.org/01rdrb571Philipps-Universität Marburg, Germany.
Daniel W HeckDepartment of Psychology, https://ror.org/01rdrb571Philipps-Universität Marburg, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cultural consensus theory (CCT) leverages shared knowledge between individuals to optimally aggregate answers to questions for which the underlying truth is unknown. Existing CCT models have predominantly focused on unidimensional point truths using dichotomous, polytomous, or continuous response formats. However, certain domains, such as risk assessment or interpretation of verbal quantifiers, may require a consensus focused on intervals, capturing a range of relevant values. We introduce the interval consensus model (ICM), a novel extension of CCT designed to estimate consensus intervals from continuous bounded interval responses. We use a Bayesian hierarchical modeling approach to estimate latent consensus intervals. In a simulation study, we show that, under the conditions studied, the ICM performs better than using simple means and medians of the responses. We then apply the model to empirical judgments of verbal quantifiers.

Indexed as

Bayesian modelingcontinuous bounded responsescultural consensus theoryinterval responses

Identifiers

PMID41185548
PMCPMC13122415

What OpenQuestion holds

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Registered trials

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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.