University of Texas at Austin, Population Research Center, Austin, TX 78712, USA.
J Learn Disabil. 2011 May-Jun;44(3):246-57. doi: 10.1177/0022219410374236. Epub 2010 Jun 29.
The disproportionate identification of learning disabilities among certain sociodemographic subgroups, typically groups that are already disadvantaged, is perceived as a persistent problem within the education system. The academic and social experiences of students who are misidentified with a learning disability may be severely restricted, whereas students with a learning disability who are never identified are less likely to receive the accommodations and modifications necessary to learn at their maximum potential. The authors use the Education Longitudinal Study of 2002 to describe national patterns in learning disability identification. Results indicate that sociodemographic characteristics are predictive of identification with a learning disability. Although some conventional areas of disproportionality are confirmed (males and language minorities), differences in socioeconomic status entirely account for African American and Hispanic disproportionality. The discrepancy between the results of bivariate and multivariate analyses confirms the importance of employing multivariate multilevel models in the investigation of disproportionality.
在教育系统中,某些社会人口统计学亚组(通常是已经处于不利地位的群体)学习障碍的识别比例过高,这被认为是一个持续存在的问题。被错误识别为学习障碍的学生的学术和社交体验可能会受到严重限制,而从未被识别为学习障碍的学生则不太可能获得充分发挥潜力所需的适应和调整。作者使用 2002 年教育纵向研究来描述学习障碍识别的全国模式。结果表明,社会人口统计学特征是学习障碍识别的预测因素。尽管某些传统的不均衡领域得到了证实(男性和语言少数群体),但社会经济地位的差异完全解释了非裔美国人和西班牙裔的不均衡现象。二元和多元分析结果之间的差异证实了在不均衡性研究中采用多元多层模型的重要性。