Processing strengths and weaknesses (PSW) approaches to learning-disability identification

Research Method · All research methods
Evidence · 7 claims (1 mixed, 6 against) · 1 study (1 associational), q3 · 1 of 1 report an effect size · 7 claims rest on one study

Description

PSW methods operationalize a cognitive discrepancy approach to LD identification based on identifying an intraindividual pattern of processing strengths and weaknesses. The XBA approach "utilizes a series of normative comparisons to identify deficient and normal academic and cognitive abilities," requiring an achievement deficit, a normal cognitive profile, and a theoretically related cognitive deficit. The C/DM instead "calculates difference scores across a number of cognitive and achievement tests and compares these difference scores against computed critical values," requiring concordance between achievement and cognitive deficits and discordance with a cognitive strength. Both are aligned with CHC theory in this study.

Accounts

• Processing strengths and weaknesses (PSW) approaches to LD identification, operationalized by the XBA and C/DM methods: PSW methods operationalize a cognitive discrepancy approach to LD identification based on identifying an intraindividual pattern of processing strengths and weaknesses. The XBA approach "utilizes a series of normative comparisons to identify deficient and normal academic and cognitive abilities," requiring an achievement deficit, a normal cognitive profile, and a theoretically related cognitive deficit. The C/DM instead "calculates difference scores across a number of cognitive and achievement tests and compares these difference scores against computed critical values," requiring concordance between achievement and cognitive deficits and discordance with a cognitive strength. Both are aligned with CHC theory in this study. (Miciak Jeremy et al. (2016))

Claims

• The XBA and C/DM PSW methods show chance-level agreement (kappa = -.10) in identifying LD among fourth graders with reading comprehension deficits [-M]
• XBA LD status does not predict posttest reading outcomes beyond pretest performance in decoding, fluency, or comprehension [-M]
• Simulation evidence cited in the article indicates PSW methods have consistently low positive predictive values despite high negative predictive values [-W]
• Simulations indicate PSW cognitive assessment would improve classification of response by only about 5-10 students per 1,000 in the best case [-W]
• A few XBA inclusionary criteria modestly predict word reading only, with very small eta squared values (η2 range .008 to .018) [-W]
• C/DM LD status likewise does not predict posttest reading outcomes beyond pretest performance [-W]
• For the C/DM, only short-term memory deficit status significantly predicted posttest word reading [~W]

Related Research Methods

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Key Sources

• Miciak Jeremy, Williams Jacob L., Taylor W. Pat, Cirino Paul T., Fletcher Jack M., Vaughn Sharon. (2016). Do Processing Patterns of Strengths and Weaknesses Predict Differential Treatment Response? Journal of Educational Psychology, 108(6), 898-909. https://doi.org/10.1037/edu0000096

Page metadata

Field	Value
type	research-method
id	processing-strengths-and-weaknesses-psw-approaches-to-learning-disability-identification
title	Processing strengths and weaknesses (PSW) approaches to learning-disability identification
description	PSW methods operationalize a cognitive discrepancy approach to LD identification based on identifying an intraindividual pattern of processing strengths and weaknesses.
status	review
generated	by process:settle-candidates on 2026-10-25
trust	unverified — no one has confirmed this page's content

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