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

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.