Comparing Three Patterns of Strengths and Weaknesses Models for the Identification of Specific Learning Disabilities

IF 0.5 Q4 EDUCATION, SPECIAL
Daniel C. Miller, D. Maricle, Alicia M. Jones
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引用次数: 2

Abstract

Processing Strengths and Weaknesses (PSW) models have been proposed as a method for identifying specific learning disabilities. Three PSW models were examined for their ability to predict expert identified specific learning disabilities cases. The Dual Discrepancy/Consistency Model (DD/C; Flanagan, Ortiz, & Alfonso, 2013) as operationalized by the Cross Battery Assessment Software (X-BASS; Ortiz, Flanagan & Alfonso, 2015), the Concordance-Discordance Model (C-DM; Hale & Fiorello, 2004), and the Psychological Processing Analyzer software (PPA v3.1; Dehn, 2015b) were evaluated. The DD/C approach as represented with the X-BASS system had a 100% agreement with the expert panel in the identification of specific learning disabilities and non-specific learning disabilities cases. The C-DM model was more conservative, identifying only 45% of the specific learning disabilities cases. The PPA software was too limited to be used in the study and is not recommended for use in identifying specific learning disabilities via a PSW approach. Although more research is needed, the results of this study would suggest that the DD/C and X-BASS provide the greatest utility for a PSW approach to identifying specific learning disabilities.
三种识别特殊学习障碍的优势与劣势模型模式之比较
加工优势和劣势(PSW)模型被提出作为一种识别特定学习障碍的方法。三个PSW模型对专家确定的特定学习障碍案例的预测能力进行了检验。双差异/一致性模型(DD/C)Flanagan, Ortiz, & Alfonso, 2013)通过跨电池评估软件(X-BASS;Ortiz, Flanagan & Alfonso, 2015),一致性-不一致性模型(C-DM;Hale & Fiorello, 2004),以及心理处理分析软件(PPA v3.1;Dehn, 2015b)进行评估。以X-BASS系统为代表的DD/C方法在识别特定学习障碍和非特定学习障碍案例方面与专家组达成了100%的一致。C-DM模型更为保守,仅识别出45%的特殊学习障碍病例。PPA软件在研究中使用的局限性太大,不建议通过PSW方法用于识别特定的学习障碍。虽然还需要更多的研究,但本研究的结果表明,DD/C和X-BASS为PSW方法识别特定学习障碍提供了最大的效用。
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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6.20%
发文量
4
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