Effects of Quantitative Ordinal Scale Design on the Accuracy of Estimates of Mean Disease Severity

Liu, Hung I.; Tsai, Jia Ren; Chung, Wen Hsin; Bock, Clive H.; Chiang, Kuo-Szu

doi:10.3390/agronomy9090565

Cited by 13 publications

(5 citation statements)

References 30 publications

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“…Moreover, due to the simplicity of analysis and categorisation, an ordinal scale can be effectively used in surveys, polls, and questionnaires. The collected responses are easily compared to draw impactful conclusions about the target audience [92]. This feature was convenient for this in-depth interview, making it simple and quick to obtain opinions from the specialists.…”

Section: Discussionmentioning

confidence: 99%

A Relevance-Based Technology–Organisation–Environment Model of Critical Success Factors for Digital Procurement Adoption in Chinese Construction Companies

Luo,

Serrão,

Liang

et al. 2023

Sustainability

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With the emergence of digital transformation, there is an increasing need for Chinese construction companies to adopt digital procurement (D-procurement). However, there is a lack of theoretical foundation to guide and support the adoption practices. This study aims to fill the research gap through the provision of a model by grouping a set of relevance-based critical success factors (CSFs) into the Technology–Organisation–Environment (TOE) framework for D-procurement adoption success (DAS). A case study approach is applied in the research. We selected H Group as it is one of the most representative D-procurement cases in China. The study includes two parts. In the first part, a systematic literature review was conducted, and 17 CSFs were identified from 12 selected studies. By grouping the 17 CSFs into the TOE framework, we put forward a basic CSF–TOE model. In the second part, an in-depth interview was carried out in H Group, where the 17 selected experts were asked to rank the previously identified CSF. Based on their order of relevance, the 17 CSFs were re-organised in the basic CSF–TOE model, and a relevance-based CSF–TOE model was finally proposed. This study is vital for D-procurement adoption because most existing CSF studies are based on the literature and questionnaire surveys, and there is a lack of actual case studies. In addition, this study significantly contributes to the field of D-procurement adoption for construction companies by providing a theoretical framework for practice and a relevance-based CSF–TOE model for research.

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Section: Discussionmentioning

confidence: 99%

A Relevance-Based Technology–Organisation–Environment Model of Critical Success Factors for Digital Procurement Adoption in Chinese Construction Companies

Luo,

Serrão,

Liang

et al. 2023

Sustainability

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“…Modelling in other disease states has shown that distinction is greater when seven or more classes are used, particularly at the lower range of disease severity. 28 This spectrum, from the absence of infection to death, enables the scale to be used across a broad range of studies. Clinical and virological absence of infection is suggestive of a cure for patients who are initially infected or suggestive of a misdiagnosis for those individuals included in a trial.…”

Section: Uses Of the Who Clinical Progression Scalementioning

confidence: 99%

High-Dose Convalescent Plasma for Treatment of Severe COVID-19

Santis¹,

Oliveira²,

Garibaldi³

et al. 2022

Emerg. Infect. Dis.

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Clinical research is necessary for an effective response to an emerging infectious disease outbreak. However, research efforts are often hastily organised and done using various research tools, with the result that pooling data across studies is challenging. In response to the needs of the rapidly evolving COVID-19 outbreak, the Clinical Characterisation and Management Working Group of the WHO Research and Development Blueprint programme, the International Forum for Acute Care Trialists, and the International Severe Acute Respiratory and Emerging Infections Consortium have developed a minimum set of common outcome measures for studies of COVID-19. This set includes three elements: a measure of viral burden (quantitative PCR or cycle threshold), a measure of patient survival (mortality at hospital discharge or at 60 days), and a measure of patient progression through the health-care system by use of the WHO Clinical Progression Scale, which reflects patient trajectory and resource use over the course of clinical illness. We urge investigators to include these key data elements in ongoing and future studies to expedite the pooling of data during this immediate threat, and to hone a tool for future needs.

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“…It will be best if the general conclusion for characteristics of quantitative ordinal scales and their design can be obtained by simulation. To this end, Chiang et al ( 2014 ) and Liu et al ( 2019 ) determined that a 10% interval scale with additional classes at <10% maximized accuracy of estimates of mean disease severity (Table 1 ). Unlike many other ordinal scales, this scale has a linear relationship with percentage area diseased at severities >10% (class 6 on the scale) (Fig.…”

Section: An Overview Of Quantitative Ordinal Scalesmentioning

confidence: 99%

“…Recently, Liu et al ( 2019 ) used simulation and actual data of mean severity of pear scab to compare mean estimates based on various quantitative ordinal scale designs to those based on NPEs, and to investigate the effects of the number of classes in an ordinal scale on the accuracy of that mean. The results indicated that scales with seven or more classes are preferable when actual mean disease severities of ≤50% are involved.…”

Section: Applications and Effects Of Quantitative Ordinal Scalesmentioning

confidence: 99%

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

Chiang

Bock

2021

Trop. plant pathol.

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The severity of plant diseases, traditionally defined as 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 have 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. In this review, we focus on the nature of quantitative ordinal scales used in plant disease assessment. Subsequently, their application 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.

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Effects of Quantitative Ordinal Scale Design on the Accuracy of Estimates of Mean Disease Severity

Cited by 13 publications

References 30 publications

A Relevance-Based Technology–Organisation–Environment Model of Critical Success Factors for Digital Procurement Adoption in Chinese Construction Companies

A Relevance-Based Technology–Organisation–Environment Model of Critical Success Factors for Digital Procurement Adoption in Chinese Construction Companies

High-Dose Convalescent Plasma for Treatment of Severe COVID-19

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

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