On the use of ridit analysis

Beder, Jay H.; Heim, Robert C.

doi:10.1007/bf02294665

Cited by 6 publications

(5 citation statements)

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“…As noted above, ridit analysis is based on an observed distribution rather than a normal distribution (Beder & Heim, 1990; Bross, 1958, 1978; Fleiss, 1981). For this example, we focus only on the reported frequency of the symptoms, scored as “never,” “rarely,” “sometimes,” “often,” and “always.” The mean value for frequency of each symptom reported by the participants before lung transplant (baseline) was used as the initial observation.…”

Section: Methodsmentioning

confidence: 99%

“…For a more detailed explanation of this study, please see Lanuza and colleagues (2012). As noted above, ridit analysis is based on an observed distribution rather than a normal distribution (Beder & Heim, 1990;Bross, 1958Bross, , 1978Fleiss, 1981). For this example, we focus only on the reported frequency of the symptoms, scored as "never," "rarely," "sometimes," "often," and "always."…”

Section: Study Examplementioning

confidence: 99%

“…As ordinal data are not considered normally distributed, analyses using the parametric approach (e.g., t tests) may not be appropriate (Gibbons, 1993) and chi-square based approaches are less efficient. Ridit analysis, an acronym for Relative to an Identified Distribution, is an approach used to compare two or more sets of ordered quantitative data that takes into consideration the inherent ordering of ordinal data (Beder & Heim, 1990;Bross, 1958Bross, , 1978Fleiss, 1981;Fleiss, Chilton, & Wallenstein, 1979). Ridit analysis has been suggested as a method for assessment of change in ordinal data because it is based on the observed distribution rather than the normal distribution.…”

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confidence: 99%

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Depicting Changes in Multiple Symptoms Over Time

2014

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Ridit analysis, an acronym for Relative to an Identified Distribution, is a method for assessing change in ordinal data and can be used to show how individual symptoms change or remain the same over time. The purposes of this article are to (a) describe how to use ridit analysis to assess change in a symptom measure using data from a longitudinal study, (b) give a step-by-step example of ridit analysis, (c) show the clinical relevance of applying ridit analysis, and (d) display results in an innovative graphic. Mean ridit effect sizes were calculated for the frequency and distress of 64 symptoms in lung transplant patients before and after transplant. Results were displayed in a bubble graph. Ridit analysis allowed us to maintain the specificity of individual symptoms and to show how each symptom changed or remained the same over time. The bubble graph provides an efficient way for clinicians to identify changes in symptom frequency and distress over time.

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Section: Methodsmentioning

confidence: 99%

Section: Study Examplementioning

confidence: 99%

mentioning

confidence: 99%

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Depicting Changes in Multiple Symptoms Over Time

2014

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“…Because the ridit theory is adequately documented, 10,20,21 were selected according to Fan et al 14 : 0 -1.9, 2-9, 10 -19, 20 -39, and more than 40 km. Referring formulas in the Appendix, the mean ridit of the VR observations in 1981 (r ϭ 0.66) is obtained.…”

Section: Methodsmentioning

confidence: 99%

“…K VR categories were selected. Referring to Beder and Heim, 21 let and denote the column vectors (q 1 ,…,q K )Ј and (p 1 ,…,p K )Ј by q and p, respectively, which are probability distributions on {1,…,K}. Such vectors are probability distributions on {1,…,K}.…”

Section: Appendixmentioning

confidence: 99%

Visual Range Trends in the Yangtze River Delta Region of China, 1981–2005

Gao

Jia

Zhang

et al. 2011

Journal of the Air & Waste Management Association

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Visual range (VR) data from 1981 to 2005 were examined for 20 meteorological monitoring sites in the Yangtze River Delta Region of China. Cumulative percentile analysis was used to construct VR trend. The 25-yr average domain-average 50% VR was approximately 21.9 Ϯ1.9 km. Domain-average 50% VR decreased from 1981 to 2005 with a trend of Ϫ2.41 km/decade. The worst 20% and 50% and best 20% VR and variation rates for the 20 sites were analyzed. The 50% VR of the town, county-level city, and prefecture-level city sites were 24.1, 21.5, and 19.4 km, respectively. The best 20% VR decreased fastest with a rate of Ϫ3.5 km/decade. Regional median VR decreased from the coastal sites to the inland sites. Ridit analysis and cumulative percentile were adopted to study the VR variation properties between economically developed areas (e.g., Nanjing and Hangzhou) and remote areas (e.g., Lvsi). The two analyses show that VR decreased in Nanjing and Hangzhou but remained constant in Lvsi from 1981 to 2005. IMPLICATIONSDecreasing VR corresponds to greater industrialization and particulate concentrations. Moving pollution sources from the coastal to inland locations only moves the decreasing visibility elsewhere. This work offered a good opportunity to comprehend the VR variation in the most developed region of China over the last 2 decades.

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Fixed-width confidence interval for two-stage response-adaptive designs in ridit analysis

Bandyopadhyay

Biswas

2016

Statistics & Probability Letters

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On the use of ridit analysis

Cited by 6 publications

References 16 publications

Depicting Changes in Multiple Symptoms Over Time

Depicting Changes in Multiple Symptoms Over Time

Visual Range Trends in the Yangtze River Delta Region of China, 1981–2005

Fixed-width confidence interval for two-stage response-adaptive designs in ridit analysis

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