Comparing the predictive power of two patterns of processing strengths and weaknesses models for the identification of specific learning disabilities
Jones, Alicia M
MetadataShow full item record
The reauthorization of the Individuals with Disabilities Education Improvement Act of 2004 presented regulations for identifying students with Specific Learning Disability (SLD), one of the thirteen eligibility categories for special education. Although these federal regulations prohibited the sole use of the aptitude-achievement discrepancy model across States, they allowed practitioners to use Response-to-Intervention models and research-based, alternative third-methods for identifying specific learning disabilities. There has been controversy and debate surrounding which methods are the most appropriate and accurate in part due to the ambiguity regarding alternative third-methods. One alternative method current in the research is the pattern of processing strengths and weaknesses (PSW) model for identifying specific learning disabilities. However, there has been limited research conducted regarding which of these PSW models is the most psychometrically sound and valid. The purpose of this current study was to determine and compare the accuracy of two PSW models by using clinical case data for individuals diagnosed with and without specific learning disabilities. Clinical data from an extant dataset of comprehensive evaluations for children and adolescents ages 8 to 17 were used in Flanagan and colleagues’ Dual-Discrepancy/Consistency Model for SLD identification (i.e., DD/C; Flanagan, Alfonso, & Ortiz, 2013a) and Hale and colleagues’ Concordance-Discordance Model of SLD (i.e., C-DM; Hale & Fiorello, 2004; Hale, Wycoff, & Fiorello, 2011). The resulting identification labels for each relevant case from both PSW models were statistically compared using cross-tabulation analysis, chi-square test for independence, and logistic regression. Results indicated that both PSW models were more accurate at identifying cases without a SLD profile, and the DD/C model operationalized by the Cross-Battery Assessment Software System had greater discriminate accuracy for predicting clinical cases with a SLD. The findings from this current study were meaningful for the fields of school psychology and special education in terms of the value and utility of PSW models.