Ch 14: Young
Diagnostic Features
Specific Learning Disorder (SLD) is the DSM-5 term; LD is an umbrella construct describing persistent low achievement not explained by other psychological disorders or environmental factors. Within DSM-5, LDs fall under neurodevelopmental disorders and are characterized by focal impairments in academic learning that limit acquisition and performance of academic skills.
Epidemiology: approximately of school-age children.
Reading disorder is the most researched and common LD variant; about of individuals with LD have primary reading deficits.
Core assumption across LD definitions: LD reflects unexpected underachievement (discrepancy between potential and actual achievement).
LD is both a clinical condition and an educational policy category; population makeup can vary with laws/regulations (e.g., different jurisdictions using different criteria).
DSM-5 subtypes corresponding to impairment in three domains:
Reading (word-reading accuracy, reading fluency, reading comprehension)
Written expression (spelling, grammar, clarity/organization of writing)
Mathematics (number sense, math facts, calculation, math reasoning)
DSM-5 requires that difficulties persist for at least months, despite interventions that target those difficulties. Severity is rated as mild, moderate, or severe.
Cognitive processing deficits are not required for DSM-5 diagnosis; individuals with SLD typically may show poor performance on cognitive tests, but it is unclear whether these cognitive abnormalities are causes, correlates, or consequences of learning difficulties.
Diagnostic criteria and assessment practices vary by jurisdiction; even with the same assessment method, operationalization differences can lead to different classifications across districts.
Historical perspective: LD definitions and diagnosis have evolved from earlier terms like “learning disturbance” to SLD; federal involvement began with PL 94-142 (Education for All Handicapped Act) in 1975, establishing a federal operational definition that continues to influence practice.
Assessment and Identification: Historical Context
Samuel Kirk is credited with origin of the term “learning disability.” His early work in the 1960s helped pave a path to federal involvement and the eventual definition of LD in practice and policy.
The Illinois Test of Psycholinguistic Abilities (ITPA) (Kirk, McCarthy & Kirk, 1961) was developed to tailor instruction, index aptitudes by treatment interactions, and guide intervention; though later critiques of the ITPA arose, the idea that psychoeducational assessment should inform instruction persisted.
Prefoundational roots trace to early 19th-century European case studies on language impairments after brain injury; the localization of function idea (testing cognitive skills to infer deficits) still informs many LD assessment methods today.
Early emphasis on IQ tests and intraindividual differences influenced a long period in which discrepancy approaches (ability–achievement gap) dominated, despite concerns about psychometrics and empirical support.
Bateman (1965) advanced a discrepancy-based definition: educationally significant discrepancy between estimated potential and actual achievement in basic learning processes (which may or may not involve CNS dysfunction). This reinforced the importance of cognitive testing for LD diagnosis.
The 1975 Education for All Handicapped Act (PL 94-142) provided a federal operational definition of SLD; subsequent federal guidelines refined how LD could be identified in educational settings.
The Isle of Wight studies (Rutter & Yule, 1975) used a regression-based approach to distinguish specific reading retardation from general reading backwardness but failed to achieve robust replication, fueling ongoing controversy over IQ–achievement discrepancy as a diagnostic anchor.
Five U.S. LD research institutes (UIC, Kansas, Minnesota, Virginia, Columbia) advanced curriculum-based assessment and intervention research; they improved identification and treatment approaches but could not converge on a single replacement for the discrepancy model.
The discrepancy model faced persistent criticisms (e.g., reliability, validity, and its status as a “wait to fail” model) and was heavily critiqued for not predicting treatment response.
In 2001, LD researchers convened at a Learning Disabilities Summit; evidence increasingly supported RTI as a viable alternative. The 2004 IDEA reauthorization allowed RTI as a method for LD identification and permitted states to adopt RTI or other research-based approaches.
RTI emphasizes prevention, progress-monitoring, and treatment utility; cognitive testing is deemphasized except to rule out intellectual disability. Critics argue RTI may not align perfectly with the federal operational definition of LD and might neglect cognitive processing deficits that influence learning.
Contemporary practice often blends elements from discrepancy, RTI, and PSW approaches (hybrid models), reflecting ongoing debates about what constitutes valid, reliable LD identification.
Overall, there is no single universally accepted method for LD identification; the field continues to “course correct” in light of new evidence, with current emphasis on DSM-5-aligned approaches and parsimonious assessment strategies.
Contemporary Classification Approaches
Three major classification approaches have dominated the field, each with distinct assumptions about LD and distinct assessment procedures:
Ability–Achievement Discrepancy (Discrepancy Model)
Response to Intervention (RTI)
Patterns of Strengths and Weaknesses (PSW) processing-based models
Acknowledgement across approaches: a comprehensive evaluation is essential; exclusionary factors (e.g., ADHD, ID, sensory deficits, cultural and linguistic diversity, opportunity to learn) must be considered, and multiple data sources are recommended.
Dual-deficit functional academic impairment (DDFAI) model and hybrid models (Fletcher et al., 2013) emphasize dual criteria: a normative deficit in achievement plus a functional impairment; they align with DSM-5 and IDEA requirements while reducing reliance on a single cognitive measure.
The field remains unsettled on which model best identifies LD and on the best ways to combine cognitive data with academic data to guide intervention.
Ability–Achievement Discrepancy Model
Traditional logic: IQ is used as a benchmark for achievement. A significant deviation of achievement from IQ indicates underachievement that is considered unexpected.
Typical operationalizations include: simple-difference cutoff (e.g., a gap of 1–2 standard deviations), expectancy formulas (based on age), and regression-based formulas controlling for regression to the mean.
Strengths: straightforward, easy to implement, aligns with the idea of unexpected underachievement, relatively actuarial (less reliance on clinical judgment).
Weaknesses: scores treated as dichotomous (cutoffs) reduce continuous data to a binary decision, measurement error around cutoffs is common, correlation between IQ and achievement makes discrepancy less reliable, poor stability over time, limited predictive validity for treatment response, and bias toward higher-IQ individuals due to regression to the mean.
Practical implications: prevalence estimates, stability, and decisions can vary across grades and jurisdictions; many jurisdictions still relied on discrepancy in practice into the 2000s.
Processing Strengths and Weaknesses (PSW) Models
Core idea: LD subtypes can be identified by patterns of cognitive weaknesses that are theoretically linked to specific academic deficits; focus on intraindividual scatter across cognitive abilities and how that relates to academic performance.
Variants include:
Concordance/Discordance Model (C/DM) – requires a pattern of concordant or discordant scores that significantly exceed the standard error of the difference (SEd) to indicate a PSW.
Dual-Discrepancy/Consistency (DD/C) – uses CHC-based cognitive measures and achievement to identify a cognitive weakness that is concordant with an academic weakness; emphasizes cross-battery data and CHC theory.
Discrepancy/Consistency Model (D/CM) – similar to C/DM but uses normative and ipsative criteria for weaknesses
Strengths: aims to identify subtypes and tailor instruction; emphasizes early intervention planning and prevention; can be used in RTI contexts as a hybrid approach.
Weaknesses: empirical support for diagnostic utility is limited; diagnostic efficiency is often low; reliability of cognitive pattern interpretations is questionable; test–retest stability of PSWs is variable; substantial overlap with non-LD groups; potential biases due to test selection and cutoffs; risk of over-interpretation due to multiple comparisons and analytic flexibility.
Current stance: PSW methods are debated; many researchers caution against relying solely on PSW for LD diagnosis; some advocates view PSW as a complement to RTI rather than a stand-alone gold standard.
RTI (Response to Intervention)
RTI is an intervention framework with three tiers of increasingly intensive instruction and ongoing progress monitoring (GOMs like CBM).
Tiers:
Tier 1: high-quality universal instruction in general education.
Tier 2: targeted supplemental instruction for those not benefiting from Tier 1.
Tier 3: intensive, individualized intervention; sometimes followed by comprehensive evaluation.
Diagnostic use: RTI is used to identify learning difficulties based on response to evidence-based instruction; it is not a stand-alone diagnostic model but until recently has been positioned as a central criterion for LD identification in some jurisdictions.
Strengths: focuses on prevention, early identification, and data-driven instruction; can reduce wait-to-fail delays; has practical appeal for schools.
Weaknesses: potential for self-congratulatory rhetoric; concerns about whether RTI alone can identify cognitive deficits that underlie LD; risk of mislabeling or delaying formal diagnosis; concerns about the quality and fidelity of interventions across settings; some large-scale studies found limited treatment impact from RTI-based identification and intervention when implemented under real-world conditions.
Hybrid approaches (DDFAI and Fletcher et al., 2013)
Emphasize dual criteria: a normative deficit in achievement plus a functional impairment in academics; may or may not require cognitive testing depending on the model.
Include elements of RTI (response to instruction) with cognitive data, balancing prevention with diagnostic clarity.
Aim to be DSM-5 aligned and IDEA-consistent while addressing concerns about reliance on a single diagnostic criterion.
Assessments Commonly Used in LD Classification
Norm-referenced intelligence tests and achievement tests, plus curriculum-based measurement (CBM) are most commonly used; comprehensive evaluations typically incorporate multiple data sources (records, interviews, observations).
Wechsler Intelligence Scale for Children—Fifth Edition (WISC‑V)
16 subtests; 5 primary index scores: Verbal Comprehension, Visual–Spatial, Fluid Reasoning, Working Memory, Processing Speed.
Full Scale IQ (FSIQ) is the hierarchical index; ancillary index scores can be formed, but interpretation cautions apply (not strictly factor-analytic).
Revisions from prior editions included discontinuation of some subtests and introduction of new ones (e.g., Picture Span for visuospatial working memory; Visual Puzzles and Figure Weights for visual-spatial and fluid reasoning).
Primary interpretation focus: use patterns in the major index scores to infer processing strengths and weaknesses (PSWs) and potential learning profiles; FSIQ can be used to identify severe discrepancy in some contexts, but emphasis is on lower-order scores for individual differences.
Kaufman Assessment Battery for Children—Second Edition (KABC-II)
Processing and cognitive abilities for ages 3–18; dual theoretical foundations: CHC and Luria’s neuropsychological theory.
Offers CHC-based scores (Gsm, Glr, Gv, Gf, Gc) and a second-order full-scale Fluid-Crystallized Index (FCI) representing g; optional Luria-based interpretation (Sequential, Learning, Simultaneous, Planning) though CHC is preferred for interpretation.
Flexibility to select theoretical model; useful for culturally and linguistically diverse examinees; cautions exist about interpreting Luria-based language of scores as measuring two distinct constructs simultaneously.
Kaufman Test of Educational Achievement—3rd Edition (KTEA-3)
Norm-referenced achievement test for ages 4–25; 19 subtests forming Reading, Math, Writing, and an omnibus Academic Skills Battery (ASB).
Provides reading-related composites (Sound–Symbol, Decoding, Reading Fluency, Reading Understanding), oral language composites (Oral Language, Oral Fluency), and cross-domain composites (Comprehension, Expression, Orthographic Processing, Academic Fluency).
Includes error analysis on 10 of 19 subtests (e.g., Math Computation) with standardized error-analysis protocols; base rates for error patterns are provided by the publisher.
Woodcock–Johnson IV (WJ-IV)
Comprehensive battery for ages 2–90; 47 subtests across three batteries to assess cognitive ability, achievement, and oral language.
Designed to measure CHC factors; supports cross-battery assessment (XBA) framework; scores include broad and narrow cognitive–achievement clusters, with more than 20 cognitive–achievement profiles possible.
Interpretable via CHC theory with core (and auxiliary) subtests; CP, precision and interpretation guided by XBA handbooks; online scoring platform for standardized scores.
WJ-IV is central to several CHC-based interpretation approaches and has led to developments such as C-SEP (Core-Selective Evaluation Process), which aligns with core WJ-IV test sets.
Cross-Battery Assessment Software System (X-BASE, X-BASE-PSW integration)
Software systems like X-BASS support PSW-based decision making by aligning multiple CHC broad abilities with achievement data; requires scores across CHC domains for interpretation.
Curriculum-Based Measurement (CBM)
Probes of core academic skills that are short (1–3 minutes), timed, and yield measures of accuracy and response rate.
Probes are brief, repeatable, and sensitive to small increments; used for screening, progress monitoring, and decision making across subskills; considered General Outcome Measures (GOMs) for decision-making across instructional targets.
CBM data can inform intervention planning and predict year-end high-stakes outcomes; CBM is widely used in RTI frameworks as well.
The Comprehensive Test of Phonological Processing, Second Edition (CTOPP-2)
Phonological processing assessment with three composites: Phonological Awareness, Phonological Memory, Rapid Naming.
Phonological Awareness and Rapid Naming often show deficits in LD with reading problems; Phonological Memory may be in the average or higher range.
Reynolds Intellectual Assessment Scale—Second Edition (RIAS-2)
Measures cognitive ability with Verbal Intelligence Index (VIX; verbal reasoning/crystallized abilities) and Nonverbal Intelligence Index (NIX; nonverbal reasoning/spatial).
Provides a Composite Memory Index (CMX) for working memory assessment; used to rule out intellectual disability and to contextualize cognitive functioning.
Brief behavioral and emotional measures
Behavior Assessment System for Children—III (BASC-3) for social–emotional functioning; ADHD Rating Scale‑5 for ADHD symptomatology (DSM-5 criteria); teacher and parent reports are common.
These measures help rule out or identify comorbidity (e.g., ADHD) and provide context for academic performance and classroom behavior.
Other considerations
Exclusionary factors: ensure impairment is not primarily attributable to ID, sensory deficits, emotional disturbance, or cultural/linguistic mismatch; consider opportunity to learn and adequate instruction.
Case-specific considerations include records review, diagnostic interviews, observations, and differential diagnosis for comorbid conditions.
In practice, many practitioners use a combination of the above tools, with emphasis on DSM-5-aligned criteria and local/state educational regulations.
CHC (Cattell–Horn–Carroll) broad abilities (Table 14.1, summarized)
Gc: Comprehension–Knowledge — depth and breadth of knowledge and culturally valued skills.
Gf: Fluid Reasoning — flexible control of attention and novel problem solving.
Gsm: Short-Term Memory — encoding, maintenance, and manipulation of information in working memory.
Glr: Long-Term Storage and Retrieval — consolidation and retrieval over periods of time.
Gv: Visual Processing — use of mental imagery to solve problems; spatial abilities.
Ga: Auditory Processing — detecting and processing meaningful nonverbal information in sound.
Gs: Processing Speed — quick, simple cognitive tasks.
Grw: Reading and Writing — written language knowledge and skills.
Gq: Quantitative Reasoning — mathematics-related knowledge and reasoning.
Other notes on assessment approaches
Cross-battery assessment (XBA) integrates subtests from multiple batteries to map CHC broad abilities to achievement; this supports targeted interpretation and reduces over-reliance on any single instrument.
The Core-Selective Evaluation Process (C-SEP) aligns testing with core WJ-IV batteries, aiming to optimize efficiency and interpretive validity in PSW-based approaches.
Clinicians should interpret subtest patterns with caution due to psychometric limitations, measurement error, and base-rate effects affecting the interpretation of pattern-based conclusions.
Evaluation Tools in Practice: Practical Implications
When using cognitive data to identify LD, practitioners should avoid overreliance on any single score; integration of cognitive data, achievement data, and progress-monitoring data is essential.
The choice of model affects who is identified as LD; discrepancies in model criteria can lead to different subsets of children being classified as LD.
There is no universally accepted gold standard for LD identification; the best practice emphasizes DSM-5-aligned, evidence-based, and defensible approaches, with clear documentation of rationale and considerations for exclusionary factors.
In practice, clinicians are urged to disclose limitations of various methods and to consider cost–benefit trade-offs when adopting newer assessment practices.
Appendix 14.1: Clinical Resources for Practitioners
LD Assessment and Identification Resources
National Center for Learning Disabilities (www.ncld.org)
Texas Center for Learning Disabilities (www.texasldcenter.org)
Essentials of Specific Learning Disability Identification (Alfonso & Flanagan, 2018)
Learning Disabilities: From Identification to Intervention (Fletcher, Lyon, Fuchs, & Barnes, 2019)
CBM (RTI) Assessment
Evaluating Educational Interventions: Single-Case Design for Measuring RTI (Riley-Tillman & Burns, 2009)
Dynamic Indicators of Basic Early Literacy Skills (DIBELS; dibels.uoregon.edu)
easyCBM (www.easycbm.com)
The ABCs of CBM: A Practical Guide to Curriculum-Based Measurement (Hosp, Hosp, & Howell, 2016)
Appendix 14.2: LD Identification Assessment Case Study
Case study background: Matthew Smith, 8-year-old, 2nd grade; significant reading and behavioral difficulties; born prematurely at 34 weeks; ADHD symptoms suspected; multiple sources of data collected (cognitive, achievement, language, behavior, interviews, classroom observations).
Data sources and instruments used:
RIAS-2 (cognitive): Verbal IQ (VIQ), Nonverbal IQ (NIQ), Composite Intelligence Index (CIX), Memory Index (CMX).
WJ-IV (achievement): Reading, Writing, Mathematics broad composites; subtests for reading and reading-related skills.
CTOPP-2 (phonological processing): Phonological Awareness, Phonological Memory, Rapid Naming.
BASC-3 (behavioral functioning) and ADHD Rating Scale-5 (teacher and parent reports).
Classroom observations and interviews (parent, teacher, child) and review of academic records.
Key results:
RIAS-2: CIX = 84 (low average; percentile ~14%), VIQ = 79 (8th percentile, below average), NIQ = 100 (50th percentile), CMX = 97 (average); implies cognitive ability is not the primary source of reading difficulties.
WJ-IV: Broad Reading composite below average; Broad Written Language low average; Broad Mathematics average.
CTOPP-2: Phonological Awareness 79 (below average, ~7th percentile); Phonological Memory 100 (average); Rapid Naming 78 (below average, ~8th percentile).
CBM and other academic data indicate reading difficulties persist despite some instructional support.
BASC-3 and ADHD Rating Scale: clinically significant elevations in inattentive/hyperactive-impulsive behavior; ADHD Combined Type diagnosed (DSM-5 314.01).
Diagnostic impressions:
Specific Learning Disorder (SLD) with impairment in reading (DSM-5; 315.0).
Co-occurring ADHD, combined presentation (DSM-5; 314.01).
Implications and recommendations:
The district should consider specialized instruction and accommodations for reading difficulties and ADHD; develop an IEP and/or 504 plan.
Intervention recommendations targeted to reading (phonological awareness, decoding, fluency, and comprehension) and behavior management strategies at home and school.
Specific academic strategies discussed include phonological awareness development, repeated reading for fluency, prereading/organization strategies for comprehension, and explicit instruction with progress monitoring.
Behavior management: direct contingency management (home/school), parent training, seating, clear instructions, frequent breaks, behavior report cards, and social skills training.
Consideration of pharmacological evaluation for ADHD as part of an integrated treatment plan.
Connections to Foundational Principles and Real-World Relevance
The LD assessment landscape reflects foundational tensions between cognitive theory, educational policy, and practical constraints in schools. The field balances principles of evidence-based practice with regulatory requirements and ecological validity across settings.
Core concepts connect to broader topics in psychology and education:
Validity and reliability of cognitive and achievement tests; factor structure and incremental validity of cognitive measures; impact on clinical decision-making.
The role of data integration across multiple sources (records, interviews, observations, rating scales) in forming robust clinical judgments.
Ethical considerations and potential conflicts of interest in the dissemination of LD assessment methods and test products.
Real-world implications include determining eligibility for special education services, the design of individualized education programs (IEPs), Section 504 accommodations, and alignment with RTI frameworks in schools.
The ongoing debate about using IQ-achievement discrepancy vs. RTI vs. PSW reflects broader questions about diagnosis versus intervention and the best balance between identification and education-focused remediation.
Summary
LD identification remains a contested area with no single gold standard; multiple models (Discrepancy, RTI, PSW) offer different lenses on how LD is defined and diagnosed.
DSM-5 emphasizes persistent academic dysfunction resistant to remediation; the role of cognitive processing deficits is less central than in prior eras, though cognitive data can be informative for ruling out other conditions.
Modern practice favors integrative approaches (hybrid models) that combine DSM-5 criteria, RTI data, and cautious use of cognitive processing information, with a strong emphasis on ecological validity and exclusionary factors.
Comprehensive assessment relies on a battery of tools (intelligence, achievement, phonological processing, and behavior) plus records, interviews, and observations; interpretation should be guided by evidence-based practices and local/state regulations.
The Case Study (Appendix 14.2) illustrates how an LD diagnosis (SLD with reading impairment) can co-occur with ADHD and how a detailed, multimethod assessment informs a plan for IEP/504 accommodations and targeted interventions.