Peña Elizabeth D, Spaulding Tammie J, Plante Elena
University of Texas at Austin, Austin, TX 78712, USA.
Am J Speech Lang Pathol. 2006 Aug;15(3):247-54. doi: 10.1044/1058-0360(2006/023).
The normative group of a norm-referenced test is intended to provide a basis for interpreting test scores. However, the composition of the normative group may facilitate or impede different types of diagnostic interpretations. This article considers who should be included in a normative sample and how this decision must be made relative to the purpose for which a test is intended.
The way in which the composition of the normative sample affects classification accuracy is demonstrated through a test review followed by a simulation study. The test review examined the descriptions of the normative group in a sample of 32 child language tests. The mean performance reported in the test manual for the sample of language impaired children was compared with the sample's norms, which either included or excluded children with language impairment. For the simulation, 2 contrasting normative procedures were modeled. The first procedure included a mixed group of representative cases (language impaired and normal cases). The second procedure excluded the language impaired cases from the norm.
Both the data obtained from test manuals and the data simulation based on population characteristics supported our claim that use of mixed normative groups decreases the ability to accurately identify language impairment. Tests that used mixed norms had smaller differences between the normative and language impaired groups in comparison with tests that excluded children with impairment within the normative sample. The simulation demonstrated mixed norms that lowered the group mean and increased the standard deviation, resulting in decreased classification accuracy.
When the purpose of testing is to identify children with impaired language skills, including children with language impairment in the normative sample can reduce identification accuracy.
常模参照测验的常模组旨在为解释测验分数提供依据。然而,常模组的构成可能会促进或阻碍不同类型的诊断性解释。本文探讨了常模样本中应包括哪些人,以及如何根据测验的目的做出这一决定。
通过一项测验综述和随后的模拟研究,展示了常模样本的构成对分类准确性的影响方式。测验综述考察了32项儿童语言测验样本中常模组的描述。将语言障碍儿童样本在测验手册中报告的平均表现与该样本的常模进行比较,常模要么包括有语言障碍的儿童,要么排除有语言障碍的儿童。对于模拟,构建了两种对比的常模程序。第一种程序包括一组有代表性的混合案例(语言障碍和正常案例)。第二种程序在常模中排除语言障碍案例。
从测验手册获得的数据以及基于总体特征的数据模拟均支持我们的观点,即使用混合常模组会降低准确识别语言障碍的能力。与在常模样本中排除有语言障碍儿童的测验相比,使用混合常模的测验在常模组和语言障碍组之间的差异较小。模拟表明,混合常模会降低组均值并增加标准差,从而导致分类准确性下降。
当测验的目的是识别语言技能受损的儿童时,在常模样本中纳入有语言障碍的儿童会降低识别准确性。