Use of the Extreme Groups Approach: A Critical Reexamination and New Recommendations.

Analysis of continuous variables sometimes proceeds by selecting individuals on the basis of extreme scores of a sample distribution and submitting only those extreme scores to further analysis. This sampling method is known as the extreme groups approach (EGA). EGA is often used to achieve greater statistical power in subsequent hypothesis tests. However, there are several largely unrecognized costs associated with EGA that must be considered. The authors illustrate the effects EGA can have on power, standardized effect size, reliability, model specification, and the interpretability of results. Finally, the authors discuss alternative procedures, as well as possible legitimate uses of EGA. The authors urge researchers, editors, reviewers, and consumers to carefully assess the extent to which EGA is an appropriate tool in their own research and in that of others. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

累计储量模型的非线性最小二乘估计

确定累计储量模型中的待定参数需要求解一个三维数值优化问题,常规算法难以给出合适的迭代初始值,并且迭代过程容易收敛于局部极小点。将三维优化问题转换成一元非线性方程的求解,给出了该一元方程的解区间搜索算法和二分法求解算法。实际算例表明．本文算法不仅计算过程稳定、计算速度快,而且计算误差小。本文算法可应用于石油地质储量、油田产量预测等一系列实际问题。     

On the practice of dichotomization of quantitative variables.

The authors examine the practice of dichotomization of quantitative measures, wherein relationships among variables are examined after 1 or more variables have been converted to dichotomous variables by splitting the sample at some point on the scale(s) of measurement. A common form of dichotomization is the median split, where the independent variable is split at the median to form high and low groups, which are then compared with respect to their means on the dependent variable. The consequences of dichotomization for measurement and statistical analyses are illustrated and discussed. The use of dichotomization in practice is described, and justifications that are offered for such usage are examined. The authors present the case that dichotomization is rarely defensible and often will yield misleading results. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

A conceptual and empirical examination of justifications for dichotomization.

Despite many articles reporting the problems of dichotomizing continuous measures, researchers still commonly use this practice. The authors’ purpose in this article was to understand the reasons that people still dichotomize and to determine whether any of these reasons are valid. They contacted 66 researchers who had published articles using dichotomized variables and obtained their justifications for dichotomization. They also contacted 53 authors of articles published in Psychological Methods and asked them to identify any situations in which they believed dichotomized indicators could perform better. Justifications provided by these two groups fell into three broad categories, which the authors explored both logically and with Monte Carlo simulations. Continuous indicators were superior in the majority of circumstances and never performed substantially worse than the dichotomized indicators, but the simulations did reveal specific situations in which dichotomized indicators performed as well as or better than the original continuous indictors. The authors also considered several justifications for dichotomization that did not lend themselves to simulation, but in each case they found compelling arguments to address these situations using techniques other than dichotomization. (PsycINFO Database Record (c) 2010 APA, all rights reserved)