Reference : Effects of rater bias and assessment method on disease severity estimation with regar...
Scientific journals : Article
Life sciences : Agriculture & agronomy
Effects of rater bias and assessment method on disease severity estimation with regard to hypothesis testing
Chiang, Kuo-Szu [> >]
Bock, Clive [> >]
El Jarroudi, Moussa mailto [Université de Liège > DER Sc. et gest. de l'environnement (Arlon Campus Environ.) > Eau, Environnement, Développement >]
Delfosse, Philippe [> >]
Lee, I.H. [> >]
Liu, H.I [> >]
Plant Pathology
Blackwell Publishing
Yes (verified by ORBi)
[en] phytopathometry ; plant disease quantification ; Rating scale
Researchers ; Professionals ; Students ; General public ; Others
The effects of bias (over and underestimates) in estimates of disease severityon hypothesis testing using different assessment methods wasexplored. Nearest percent estimates (NPE), the Horsfall-Barratt (H-B) scale, and two linear category scales (10% increments, with and without additional grades at low severity) were compared using simulation modeling to assess effects of bias. Type I and type II error rates were used to comparetwo treatment differences.The power of the H-B scale and the 10% scale wereleast for correctly testing an hypothesis compared withthe other methods, and the effects of rater bias on type II errors are greater over specific severity ranges. Apart from NPEs, the amended 10% category scale wasmost often superior to other methods at all severities tested for reducing the risk of type II errors. It should thus be a preferred method for raters who must use a category scale for disease assessments. Rater bias and assessment method had little effect on type I error rates.
The powerof the hypothesis test using unbiased estimateswasmost often greater compared
with biased estimates, regardless of assessment method. An unanticipated observationwas the greater impact of rater bias compared with assessment method on type II errors. Knowledge of the effects of rater bias and scale type onhypothesis testing can be used to improve accuracy and reliability of disease severity estimates and canprovide a logical framework for improvingaids to estimate severity visually, including standard area diagrams and rater training software.

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