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ARS Home » Southeast Area » Byron, Georgia » Fruit and Tree Nut Research » Research » Publications at this Location » Publication #381544

Research Project: Healthy, Sustainable Pecan Nut Production

Location: Fruit and Tree Nut Research

Title: Understanding the ramifications of quantitative ordinal scales on accuracy of estimates of disease severity and data analysis in plant pathology

item CHIANG, KUO-SZU - National Chung-Hsing University
item Bock, Clive

Submitted to: Tropical Plant Pathology
Publication Type: Review Article
Publication Acceptance Date: 6/7/2021
Publication Date: 7/13/2021
Citation: Chiang, K., Bock, C.H. 2021. Understanding the Ramifications of Quantitative Ordinal Scales on Accuracy of Estimates of Disease Severity and Data Analysis in Plant Pathology. Tropical Plant Pathology. Vol 47:58-73.

Interpretive Summary:

Technical Abstract: The severity of plant diseases, traditionally the proportion of the plant tissue exhibiting symptoms, is a key quantitative variable to know for many diseases but is prone to error. Plant pathologists face many situations in which the measurement by nearest percent estimates (NPEs) of disease severity is time-consuming or impractical. Moreover, rater NPEs of disease severity are notoriously variable. Therefore, NPEs of disease may be of questionable value if severity cannot be determined accurately and reliably. In such situations, researchers have often used a quantitative ordinal scale of measurement – often alleging the time saved, and the ease with which the scale can be learned. Because quantitative ordinal disease scales lack the resolution of the 0 to 100% scale, they are inherently less accurate. We contend that scale design and structure has ramifications for the resulting analysis of data from the ordinal scale data. To minimize inaccuracy and ensure that there is equivalent statistical power when using quantitative ordinal scale data, design of the scales can be optimized for use in the discipline of plant pathology. This review paper focuses on the nature of quantitative ordinal scales in plant disease assessment. Subsequently, their uses and effects will be discussed. Finally, we will review how to optimize quantitative ordinal scales design to allow sufficient accuracy of estimation while maximizing power for hypothesis testing.