Demo corpus. Scores are computed on a select set of biomedical paper/datasets and may be inaccurate for papers outside this corpus — DataRank relies on network effects that improve with scale. We aim to expand this into a fully open resource pending additional funding.

The Measurement of Observer Agreement for Categorical Data

Biometrics(1977)10.2307/2529310Source: DataRank Database

The Measurement of Observer Agreement for Categorical Data is a research paper published in Biometrics (1977). On theSindex it has a DataRank of 1.7. It has been cited 77,165 times.

N/A

1.7DataRank · unranked

1.7

Open Access77165 citations · base score 11.3

Cite:

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

This paper presents a general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies. The procedure essentially involves the construction of functions of the observed proportions which are directed at the extent to which the observers agree among themselves and the construction of test statistics for hypotheses involving these functions. Tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interobserver agreement are developed as generalized kappa-type statistics. These procedures are illustrated with a clinical diagnosis example from the epidemiological literature.

›Data sources & pipeline

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Pending

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

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DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

1.7

From this paper's citation signal

Citation Network Contribution

Citation network not refreshed for this result

This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.

Learn more about DataRank methodology →

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.

Base score B(p): log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
Network N(p): Σ over citers of log1p(C_q) ÷ max(outdegree_q, 1). Being cited by a highly-cited paper with few references counts most.
Damping factor d = 0.85: DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
Self-citations excluded: Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.

Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.

Read the full methodology →