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Simulation of LD Identification Accuracy Using a Pattern of Processing Strengths and Weaknesses Method With Multiple Measures

机译：LD识别精度的多尺度加工强弱方法模拟

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摘要

We investigated the classification accuracy of learning disability (LD) identification methods premised on the identification of an intraindividual pattern of processing strengths and weaknesses (PSW) method using multiple indicators for all latent constructs. Known LD status was derived from latent scores; values at the observed level identified LD status for individual cases according to the concordance/discordance method. Agreement with latent status was evaluated using (a) a single indicator, (b) two indicators as part of a test–retest “confirmation” model, and (c) a mean score. Specificity and negative predictive value (NPV) were generally high for single indicators (median specificity = 98.8%, range = 93.4%−99.7%; median NPV = 94.2%, range = 85.6%−98.7%), but low for sensitivity (median sensitivity = 49.1%, range = 20.3%−77.1%) and positive predictive value (PPV; median PPV = 48.8%, range = 23.5%−69.6%). A test–retest procedure produced inconsistent and small improvements in classification accuracy, primarily in “not LD” decisions. Use of a mean score produced small improvements in classifications (mean improvement = 2.0%, range = 0.3%−2.8%). The modest gains in agreement do not justify the additional testing burdens associated with incorporating multiple tests of all constructs.
机译：我们研究了学习障碍（LD）识别方法的分类准确性，该方法的前提是使用针对所有潜在构造的多个指标来识别加工优势和劣势（PSW）方法的个性化模式。已知的LD状态是从潜在得分中得出的；观察水平的值根据一致性/不一致方法确定了个别案例的LD状态。使用（a）单个指标，（b）作为重测“确认”模型的一部分的两个指标和（c）平均评分来评估具有潜在身份的协议。单一指标的特异性和阴性预测值（NPV）通常较高（中位特异性= 98.8％，范围= 93.4％−99.7％;中位数NPV = 94.2％，范围= 85.6％−98.7％），但敏感性较低（中位数敏感性= 49.1％，范围= 20.3％−77.1％）和阳性预测值（PPV； PPV中位数= 48.8％，范围= 23.5％−69.6％）。重测程序主要在“非LD”决策中对分类准确性产生了不一致的微小改进。平均得分的使用在分类方面产生了很小的改善（平均改善= 2.0％，范围= 0.3％-2.8％）。一致的收益并不能证明与对所有结构进行多次测试相关的额外测试负担。

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