Setting health research priorities using the CHNRI method

V. Quantitative properties of human collective knowledge

Bibliographic Data

ID19551065
AuthorsIgor Rudan (0000-0001-6993-6884, University of Edinburgh, corresponding author), Sachiyo Yoshida (0000-0002-1101-8535, World Health Organization), Kerri Wazny (0000-0002-4891-3891, University of Edinburgh), Kit Y Chan (0000-0002-3465-8383, University of Edinburgh), Simon Cousen (0000-0001-8970-2305, London School of Hygiene & Tropical Medicine), Simon Cousens
Year2016
Volume6
Issue1
Pages010502-010502
Publication date2016-06-01
Open AccessYes
TypeARTICLE
VenueJournal of Global Health (JOURNAL)
Journal identifiersISSN: 2047-2978 • E-ISSN: 2047-2986
PublisherInternational Society of Global Health (PUBLISHER • GB)
DOI10.7189/jogh.06.010502
PMID27350873
OpenAlexW2463862931
LanguageEN
Citations received24
References cited14

INTRODUCTION: Crowdsourcing has become an increasingly important tool to address many problems - from government elections in democracies, stock market prices, to modern online tools such as TripAdvisor or Internet Movie Database (IMDB). The CHNRI method (the acronym for the Child Health and Nutrition Research Initiative) for setting health research priorities has crowdsourcing as the major component, which it uses to generate, assess and prioritize between many competing health research ideas. METHODS: We conducted a series of analyses using data from a group of 91 scorers to explore the quantitative properties of their collective opinion. We were interested in the stability of their collective opinion as the sample size increases from 15 to 90. From a pool of 91 scorers who took part in a previous CHNRI exercise, we used sampling with replacement to generate multiple random samples of different size. First, for each sample generated, we identified the top 20 ranked research ideas, among 205 that were proposed and scored, and calculated the concordance with the ranking generated by the 91 original scorers. Second, we used rank correlation coefficients to compare the ranks assigned to all 205 proposed research ideas when samples of different size are used. We also analysed the original pool of 91 scorers to to look for evidence of scoring variations based on scorers' characteristics. RESULTS: The sample sizes investigated ranged from 15 to 90. The concordance for the top 20 scored research ideas increased with sample sizes up to about 55 experts. At this point, the median level of concordance stabilized at 15/20 top ranked questions (75%), with the interquartile range also generally stable (14-16). There was little further increase in overlap when the sample size increased from 55 to 90. When analysing the ranking of all 205 ideas, the rank correlation coefficient increased as the sample size increased, with a median correlation of 0.95 reached at the sample size of 45 experts (median of the rank correlation coefficient = 0.95; IQR 0.94-0.96). CONCLUSIONS: Our analyses suggest that the collective opinion of an expert group on a large number of research ideas, expressed through categorical variables (Yes/No/Not Sure/Don't know), stabilises relatively quickly in terms of identifying the ideas that have most support. In the exercise we found a high degree of reproducibility of the identified research priorities was achieved with as few as 45-55 experts

Concordance · Crowdsourcing · Data science · Information retrieval · Sample size determination · Statistics · The Internet · World Wide Web · Computer Science · Mathematics · Medicine · Mobile Crowdsensing and Crowdsourcing · Open Source Software Innovations · Psychology · Reliability and Agreement in Measurement

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