The objective of this article is to demonstrate with examples that the two-sided tie correction does not work well. This correction was developed by Cureton so that Kendall’s tau-type and Spearman’s rho-type formula...The objective of this article is to demonstrate with examples that the two-sided tie correction does not work well. This correction was developed by Cureton so that Kendall’s tau-type and Spearman’s rho-type formulas for rank-biserial correlation yield the same result when ties are present. However, a correction based on the bracket ties achieves the desired goal, which is demonstrated algebraically and checked with three examples. On the one hand, the 10-element random sample given by Cureton, in which the two-sided tie correction performs well, is taken up. On the other hand, two other examples are given, one with a 7-element random sample and the other with a clinical random sample of 31 participants, in which the two-sided tie correction does not work, but the new correction does. It is concluded that the new corrected formulas coincide with Goodman-Kruskal’s gamma as compared to Glass’ formula that matches Somers’ d<sub>Y</sub><sub>|X</sub> or asymmetric measure of association of Y ranking with respect to X dichotomy. The use of this underreported coefficient is suggested, which is very easy to calculate from its equivalence with Kruskal-Wallis’ gamma and Somers’ d<sub>Y</sub><sub>|X</sub>.展开更多
We studied woodland vegetation in broad-leaved deciduous woodlands of Metema in northwestern Amhara regional state, Ethiopia to determine plant community types and species distribution patterns and their relationships...We studied woodland vegetation in broad-leaved deciduous woodlands of Metema in northwestern Amhara regional state, Ethiopia to determine plant community types and species distribution patterns and their relationships with environmental variables, including altitude, pH, cation exchange capacity, electrical conductivity （EC）, and moisture. We used a selective approach with a systematic sampling design. A total of 74 quadrats, each 25m × 25m at intervals of 150-200 m were sampled along the established transect lines. For herbaceous vegetation and soil data collection, five subquadrats each lm x lm were established at the four corners and the center of each quadrat. Three community types were identified using TWINSPAN analysis. All three community types showed high diversity （Shannon-Weiner index）, the highest in community type II at 3.55. The highest similarity coefficient was 0.49 （49%） between community types II and III, reflecting 0.51 （51%） dissimilarity in their species richness. The canonical correspondence ordination diagram revealed that the distribution pattern of community type I was explained by moisture while that of community types III and II was explained by EC and altitude and moisture, respectively. Altitude was the most statistically significant environmental variable, followed by moisture and EC in determining the total variation in species composition and distribution patterns while pH and cation exchange capacity were non significant. In conclusion, we recommend that any intervention should take into account these three discrete community types and their environmental settings to make the intervention more successful.展开更多
文摘The objective of this article is to demonstrate with examples that the two-sided tie correction does not work well. This correction was developed by Cureton so that Kendall’s tau-type and Spearman’s rho-type formulas for rank-biserial correlation yield the same result when ties are present. However, a correction based on the bracket ties achieves the desired goal, which is demonstrated algebraically and checked with three examples. On the one hand, the 10-element random sample given by Cureton, in which the two-sided tie correction performs well, is taken up. On the other hand, two other examples are given, one with a 7-element random sample and the other with a clinical random sample of 31 participants, in which the two-sided tie correction does not work, but the new correction does. It is concluded that the new corrected formulas coincide with Goodman-Kruskal’s gamma as compared to Glass’ formula that matches Somers’ d<sub>Y</sub><sub>|X</sub> or asymmetric measure of association of Y ranking with respect to X dichotomy. The use of this underreported coefficient is suggested, which is very easy to calculate from its equivalence with Kruskal-Wallis’ gamma and Somers’ d<sub>Y</sub><sub>|X</sub>.
基金supported by Special Fund for Public Welfare Technology Research of Agricultural Industry (200903014)
文摘We studied woodland vegetation in broad-leaved deciduous woodlands of Metema in northwestern Amhara regional state, Ethiopia to determine plant community types and species distribution patterns and their relationships with environmental variables, including altitude, pH, cation exchange capacity, electrical conductivity （EC）, and moisture. We used a selective approach with a systematic sampling design. A total of 74 quadrats, each 25m × 25m at intervals of 150-200 m were sampled along the established transect lines. For herbaceous vegetation and soil data collection, five subquadrats each lm x lm were established at the four corners and the center of each quadrat. Three community types were identified using TWINSPAN analysis. All three community types showed high diversity （Shannon-Weiner index）, the highest in community type II at 3.55. The highest similarity coefficient was 0.49 （49%） between community types II and III, reflecting 0.51 （51%） dissimilarity in their species richness. The canonical correspondence ordination diagram revealed that the distribution pattern of community type I was explained by moisture while that of community types III and II was explained by EC and altitude and moisture, respectively. Altitude was the most statistically significant environmental variable, followed by moisture and EC in determining the total variation in species composition and distribution patterns while pH and cation exchange capacity were non significant. In conclusion, we recommend that any intervention should take into account these three discrete community types and their environmental settings to make the intervention more successful.
