Evidence map›Paper›PMID 39511613›Full record

ArticlePopulation health metrics2024

Harmonizing measurements: establishing a common metric via shared items across instruments.

Iris Eekhout, Ann M Weber, Stef van Buuren

Abstract read
In one paragraph

Article in Population health metrics, 2024. 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
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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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.

Iris EekhoutDepartment Child Health, Netherlands Organization for Applied Scientific Research TNO, Sylviusweg 71, Leiden, the Netherlands. iris.eekhout@tno.nl.
Ann M WeberSchool of Public Health, University of Nevada Reno, Reno, NV, USA.
Stef van BuurenDepartment Child Health, Netherlands Organization for Applied Scientific Research TNO, Sylviusweg 71, Leiden, the Netherlands.

Funding

Bill and Melinda Gates Foundation OPP1138517
6 · The paper itself

Abstract

backgroundThe proliferation of instruments that define instrument-specific metrics impedes progress in comparative assessment across populations. This paper explores a method to extract a common metric from related but different instruments and transform the original measurements into scores with a standard unit of measurement.

methodsExisting data from four assessment instruments of child development, collected from three different samples of children, were used to create "equate clusters" of items that measure the same behaviour in (slightly) different ways. A probability model was formulated to identify best items and groups to serve as anchors linking the instruments, assuming that items in an anchoring or "active" equate cluster are psychometrically equivalent. Quantification and inspection of item characteristic curves were used to resolve which equate clusters should be active. We simulated the impact of various analytic choices.

resultsSimulation confirmed the feasibility of creating a common metric from data collected with different instruments from respondent samples with different abilities. The method performed as expected in an application in early childhood development.

conclusionsThe use of equate clusters is an intuitive and flexible way to establish a common metric across instruments and facilitates the transformation of measurements obtained to a standardized scale. Standardizing instrument scores to a common metric allows for population-level comparisons on a global scale.

Indexed as

Child DevelopmentPsychometricsChildChild, PreschoolCluster AnalysisComputer SimulationHumansConcurrent calibrationEarly childhood developmentGlobal metricMeta-analysesRasch model

Identifiers

PMID39511613
PMCPMC11546590

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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.