Abstract
Procedures for identifying specific learning disabilities (SLD) have been controversial, if not contentious, for many decades. Over this period, researchers and policymakers have sought to replace the IQ-achievement discrepancy (IAD) method, the original method used to identify SLD, with alternative research-based approaches. Patterns of strengths and weaknesses (PSW) is advocated as overcoming the limitations of the IAD method, is allowed under federal special education regulations, and has been adopted by at least 14 states. Questions remain, however, regarding whether PSW is evidence-based as an identification procedure. This study sought to understand the evidentiary basis of PSW for SLD identification through a systematic review of the diagnostic accuracy evidence. Review results showed that PSW identifies SLD at the level of chance (e.g., a coin flip) regardless of PSW method used, instrument employed, and whether real or simulated data are used. The evidence to date suggests that PSW may not be worth the time or effort for SLD identification, and therefore, psychologists are encouraged to consider alternative SLD identification methods.
Impact Statement
This study reviews the diagnostic accuracy research surrounding patterns of strengths and weaknesses (PSW) for specific learning disabilities (SLD) identification. The results indicated that no matter what PSW method is used, the procedure is only about as accurate as a coin flip when used to identify SLD. School districts and school psychology practitioners should look to alternative procedures to PSW when seeking a method for SLD identification.
ASSOCIATE EDITOR:
Disclosure statement
The authors have no conflicts of interest to report.
The search plan was preregistered on the Open Science Framework website.
Ethical Approval
All procedures were conducted in accordance with the ethical standards in publishing.
Author Contribution Statement
All authors were responsible for the design, data analysis, conceptualization, and writing of the manuscript. All authors have been actively contributing to the literature in school psychology and allied disciplines.
Open Scholarship
This article has earned the Center for Open Science badges for Preregistered. The materials are openly accessible at https://osf.io/2gqvt/?view_only=bc6df62026454bd4a553c37aee924f2c. To obtain the author’s disclosure form, please contact the Editor.
Notes
1 The most common PSW procedure used was the DD/C method (74%) with approximately 14% and 7% using the discrepancy—consistency and concordance—discordance methods, respectively (Maki & Adams, Citation2019).
2 There is a perspective that SLD is a socially constructed rather than scientifically derived construct (e.g., Sleeter, Citation1986).
3 Please see Table 2 for a definition of each abbreviation.
4 Flanagan and Schneider (Citation2016) criticized Kranzler et al. (Citation2016) for using the term XBA instead of PSW. It is noted that PSW was initially referenced as XBA, so it is understandable that both the Roman (Citation2016) thesis, directed by a PSW proponent, and the Kranzler et al. article applied this terminology.
5 A diagnostic accuracy analysis of the CSEP has never been undertaken so it is unknown whether it would produce similar findings. One of its authors has indicated via personal correspondence (E. Schultz, 4/25/23) that the CSEP has now moved toward a low achievement approach to SLD identification.
Additional information
Funding
Notes on contributors
Stefan C. Dombrowski
Stefan C. Dombrowski is a professor at Rider University. Please see https://www.rider.edu/about/faculty-staff-directory/stefan-dombrowski for more information.
Nicholas F. Benson
Nicholas F. Benson is an associate professor of School Psychology in the Department of Educational Psychology at Baylor University. Please see https://soefaculty.baylor.edu/nicholas-benson/ for more information.
Kathrin E. Maki
Kathrin E. Maki is an assistant professor of School Psychology in the School of Special Education, School Psychology, and Early Childhood Studies at the University of Florida. Her research centers on the conceptual, psychometric, and decision-making issues related to Specific Learning Disabilities (SLD) identification, and the use of assessment data to drive academic intervention implementation for students with SLD and other academic difficulties.