Median split, k-group split, and optimality in continuous populations

Knüppel, Lothar; Hermsen, Oliver

doi:10.1007/s10182-010-0122-5

Cited by 14 publications

(10 citation statements)

References 26 publications

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“…Continuous variables were transformed into categorical variables based on recognized cutoff values (for age) or median number (for blood loss volume, number of obtained lymph nodes, and lymph node station). 13 To examine the generalizability of the model, an external validation cohort was provided by the IASLC lung cancer database. The cohort is composed of 2,148 patients with stage I to III NSCLC diagnosed between 1999 and 2010 in China, Europe, and North America.…”

Section: Patient Population and Data Processingmentioning

confidence: 99%

Development and Validation of a Nomogram for Predicting Survival in Patients With Resected Non–Small-Cell Lung Cancer

Liang

Zhang

Jiang

et al. 2015

JCO

545

466

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Section: Patient Population and Data Processingmentioning

confidence: 99%

Development and Validation of a Nomogram for Predicting Survival in Patients With Resected Non–Small-Cell Lung Cancer

Liang

Zhang

Jiang

et al. 2015

JCO

545

466

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show abstract

“…We set the cut-point value according to the preliminary analysis [77] at the 50 th percentile (i.e. the median) of the number of records per sampling unit [78] (see further details on Appendix S1). Thereby, units with higher number of sampling records than the median were classified as well sampled and those below as poorly sampled.…”

Section: Methodsmentioning

confidence: 99%

A Novel Method to Handle the Effect of Uneven Sampling Effort in Biodiversity Databases

et al. 2013

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How reliable are results on spatial distribution of biodiversity based on databases? Many studies have evidenced the uncertainty related to this kind of analysis due to sampling effort bias and the need for its quantification. Despite that a number of methods are available for that, little is known about their statistical limitations and discrimination capability, which could seriously constrain their use. We assess for the first time the discrimination capacity of two widely used methods and a proposed new one (FIDEGAM), all based on species accumulation curves, under different scenarios of sampling exhaustiveness using Receiver Operating Characteristic (ROC) analyses. Additionally, we examine to what extent the output of each method represents the sampling completeness in a simulated scenario where the true species richness is known. Finally, we apply FIDEGAM to a real situation and explore the spatial patterns of plant diversity in a National Park. FIDEGAM showed an excellent discrimination capability to distinguish between well and poorly sampled areas regardless of sampling exhaustiveness, whereas the other methods failed. Accordingly, FIDEGAM values were strongly correlated with the true percentage of species detected in a simulated scenario, whereas sampling completeness estimated with other methods showed no relationship due to null discrimination capability. Quantifying sampling effort is necessary to account for the uncertainty in biodiversity analyses, however, not all proposed methods are equally reliable. Our comparative analysis demonstrated that FIDEGAM was the most accurate discriminator method in all scenarios of sampling exhaustiveness, and therefore, it can be efficiently applied to most databases in order to enhance the reliability of biodiversity analyses.

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“…Analogous to previous research employing the instrument (e.g., Gaylon & Wann, 2012; Wann, Zapalac, Grieve, Patridge, & Lanter, 2015), the measure was transformed into a blocking variable via a three-way split that categorized participants as either low ( n = 50; M = 2.30, SD = 1.05), medium ( n = 52; M = 5.76, SD = 0.96), or high ( n = 47; M = 9.47, SD = 1.06) in fanship. A three-way split was preferred to a median-split to more optimally reflect between-groups differences (Knüppel & Hermsen, 2010). An analysis of variance (ANOVA) comparing mean sport fanship as a function of group assignment verified effectiveness of the grouping, F (2, 146) = 593.51, p < .001, η p 2 = .89.…”

Section: Methodsmentioning

confidence: 99%

Differentiating Objective Sport Knowledge Versus Subjective Sport Fanship via a Test of Exemplification in Sport Media

Hahn

Cummins

2017

Communication & Sport

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Recognizing the need for a more nuanced measure of sport fanship, this study utilizes an experiment to better understand the construct of sport fanship, comparing a common sport fanship measure and an adapted, objective measure of sport knowledge. This experiment also extends exemplification theory into the novel context of sports media by examining how one individual difference—domain-specific knowledge and interest in the subject—influences perceptions formed through a common message type, a sports highlight reel. Exemplification effects in the form of exaggerated football plays did indeed alter perceptions of the athlete despite concrete sport statistics depicting the athlete modestly. Interestingly, while the two concepts were positively correlated, they did not act uniformly. High knowledge sports fans were less swayed by exaggerated sports highlight exemplars when compared to their low knowledge counterparts while high and low fans categorized by self-identified interest did not affect the perceptions of the on-screen athlete. Thus, this study contributes to current research by expanding exemplification theory and furthering the multidimensional operational understanding of sport fanship. Additional findings and future research directions are discussed.

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Median split, k-group split, and optimality in continuous populations

Abstract: Categorization, Median split, Continuous distributions, Numerical methods,

Cited by 14 publications

References 26 publications

Development and Validation of a Nomogram for Predicting Survival in Patients With Resected Non–Small-Cell Lung Cancer

Development and Validation of a Nomogram for Predicting Survival in Patients With Resected Non–Small-Cell Lung Cancer

A Novel Method to Handle the Effect of Uneven Sampling Effort in Biodiversity Databases

Differentiating Objective Sport Knowledge Versus Subjective Sport Fanship via a Test of Exemplification in Sport Media

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