Commentary on Meehl’s theoretical risks and tabular asterisks: Sir Karl, Sir Ronald, and the slow progress of soft psychology

Robyn M Dawes
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引用次数: 1

Abstract

Meehl points out that in “soft” psychology we do not have quantitatively measured variables that allow generalization outside the context of study to other contexts involving these same variables (perhaps combined with others to yield prediction and control). But as illustrated by this own work comparing actuarial versus clinical combination of variables to predict important human outcomes, we do have consistency across qualitatively diverse contexts. Here, actuarial combination is almost always superior. That result might not be what we would wish to have (e.g., a very good prediction of who will succeed in graduate school or on parole—as opposed to knowing that an actuarial prediction is superior to a clinical one), but it is what we have. It is not nothing, in fact far from nothing. Lacking a statistical theory (or even definition) of “qualitative diversity” and how to access it, we often rely on null hypothesis testing—not to be taken literally, but to access a consistent direction of results across qualitatively diverse contexts.

评论米尔的理论风险和表格星号:卡尔爵士,罗纳德爵士,以及软心理学的缓慢进展
Meehl指出,在“软”心理学中,我们没有定量测量的变量,这些变量不能在研究背景之外推广到涉及这些变量的其他背景(可能与其他变量结合起来产生预测和控制)。但是,正如我自己的工作所说明的那样,通过比较精算和临床变量组合来预测重要的人类结果,我们确实在质量不同的情况下具有一致性。在这里,精算组合几乎总是更好。这个结果可能不是我们所希望的(例如,对谁将在研究生院或假释中取得成功的非常好的预测,而不是知道精算预测优于临床预测),但它是我们所拥有的。它不是一无所有,实际上远非一无所有。由于缺乏“定性多样性”的统计理论(甚至定义)以及如何获得它,我们经常依赖于零假设检验——不是字面上的,而是在定性不同的背景下获得一致的结果方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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