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关于神经心理学中的百分位常模:测试分数百分位等级不确定性量化的建议报告标准和方法。

On percentile norms in neuropsychology: proposed reporting standards and methods for quantifying the uncertainty over the percentile ranks of test scores.

作者信息

Crawford John R, Garthwaite Paul H, Slick Daniel J

机构信息

School of Psychology, University of Aberdeen, Aberdeen AB24 2UB, UK.

出版信息

Clin Neuropsychol. 2009 Sep;23(7):1173-95. doi: 10.1080/13854040902795018. Epub 2009 Mar 26.

Abstract

Normative data for neuropsychological tests are often presented in the form of percentiles. One problem when using percentile norms stems from uncertainty over the definitional formula for a percentile. (There are three co-existing definitions and these can produce substantially different results.) A second uncertainty stems from the use of a normative sample to estimate the standing of a raw score in the normative population. This uncertainty is unavoidable but its extent can be captured using methods developed in the present paper. A set of reporting standards for the presentation of percentile norms in neuropsychology is proposed. An accompanying computer program (available to download) implements these standards and generates tables of point and interval estimates of percentile ranks for new or existing normative data.

摘要

神经心理学测试的常模数据通常以百分位数的形式呈现。使用百分位数常模时存在的一个问题源于百分位数定义公式的不确定性。(存在三种并存的定义,它们可能产生截然不同的结果。)第二个不确定性源于使用常模样本估计原始分数在常模总体中的位置。这种不确定性是不可避免的,但可以使用本文开发的方法来把握其程度。本文提出了一套神经心理学中百分位数常模呈现的报告标准。一个配套的计算机程序(可供下载)实施这些标准,并为新的或现有的常模数据生成百分位排名的点估计和区间估计表。

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