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Exploring Differences between Cognition, Reading, Math, and Attention Scores in Students with Disabilities: A Systematic Review

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Title: Exploring Differences between Cognition, Reading, Math, and Attention Scores in Students with Disabilities: A Systematic Review
Language: English
Authors: Alicia A. Stewart (ORCID 0000-0001-6770-5046), Nancy Scammacca (ORCID 0000-0002-7484-5976), Young Ri Lee, Pierce Cappelli (ORCID 0000-0001-8426-8132)
Source: Learning Disability Quarterly. 2026 49(1):39-53.
Availability: SAGE Publications and Hammill Institute on Disabilities. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 15
Publication Date: 2026
Sponsoring Agency: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (DHHS/NIH)
Contract Number: 5P50HD05211712
Document Type: Journal Articles
Information Analyses
Descriptors: Learning Disabilities, Reading Difficulties, Mathematics Skills, Scores, Students with Disabilities, Attention Deficit Hyperactivity Disorder, Comorbidity, Outcomes of Education, Effect Size, Cognitive Ability, Short Term Memory, Language Skills
DOI: 10.1177/07319487251318352
ISSN: 0731-9487
2168-376X
Abstract: A large body of research documents underlying cognitive factors, many of which are shared, in students with reading disabilities (RDs), math disabilities (MDs), comorbid reading and math disabilities (RD + MD), as well as students with attention-deficit/hyperactivity disorder (ADHD) and students with RD and ADHD. In an effort to examine differences in reading, mathematics, and cognitive outcomes among these students, we investigated the outcomes between these groups across the published research literature by conducting a systematic review. A total of 16 studies met inclusion criteria. Data from a total of 1,910 participants across studies was used to answer our research questions. Mean effect size differences across studies highlight lower overall outcomes for students with RD + MD and students with RD + ADHD compared to students with RD, MD, or ADHD alone.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1495982
Database: ERIC
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  Value: <anid>AN0190688476;dyw01feb.26;2026Jan08.00:51;v2.2.500</anid> <title id="AN0190688476-1">Exploring Differences Between Cognition, Reading, Math, and Attention Scores in Students With Disabilities: A Systematic Review </title> <p>A large body of research documents underlying cognitive factors, many of which are shared, in students with reading disabilities (RDs), math disabilities (MDs), comorbid reading and math disabilities (RD + MD), as well as students with attention-deficit/hyperactivity disorder (ADHD) and students with RD and ADHD. In an effort to examine differences in reading, mathematics, and cognitive outcomes among these students, we investigated the outcomes between these groups across the published research literature by conducting a systematic review. A total of 16 studies met inclusion criteria. Data from a total of 1,910 participants across studies was used to answer our research questions. Mean effect size differences across studies highlight lower overall outcomes for students with RD + MD and students with RD + ADHD compared to students with RD, MD, or ADHD alone.</p> <p>Keywords: attention; reading; mathematics; attention-deficit disorders; learning disabilities; systematic review</p> <p>Learning disabilities (LD) and attention-deficit/hyperactivity disorder (ADHD) impact a substantial number of children ([<reflink idref="bib25" id="ref1">25</reflink>]; [<reflink idref="bib47" id="ref2">47</reflink>]). Evidence suggests 32% of students in U.S. schools from ages 3 to 21 years have a diagnosis of LD ([<reflink idref="bib25" id="ref3">25</reflink>]). Prevalence of ADHD diagnoses varies across sources. The Center for Disease Control and Prevention report 8.2% to 9.8% of children ages 3 to 17 years have either had (at some point) or currently have a diagnosis of ADHD ([<reflink idref="bib3" id="ref4">3</reflink>]). Some research suggests up to 19% of students ages 6 to 18 years have a diagnosis of ADHD ([<reflink idref="bib47" id="ref5">47</reflink>]). Variation in prevalence of ADHD diagnosis may occur due to the ways in which students are identified and served in schools. Not all students with ADHD receive special education services; however, some qualify under the category of Other Health Impairment (OHI). Others may receive support through the Office of Civil Rights via Section 504, but others may not hold a formal diagnosis or receive any legally documented supports in school. Both LD and ADHD are significantly related to lower academic outcomes (e.g., [<reflink idref="bib4" id="ref6">4</reflink>]; [<reflink idref="bib8" id="ref7">8</reflink>]). Students with LD have higher dropout rates than their typically developing peers ([<reflink idref="bib4" id="ref8">4</reflink>]). Students with ADHD are also more likely to drop out or be retained ([<reflink idref="bib2" id="ref9">2</reflink>]) and often score below average on achievement tests and other measures of academic outcomes ([<reflink idref="bib8" id="ref10">8</reflink>]).</p> <p>Many students with LD have specific needs in the area of reading (reading disability; RD) or in the area of math (math disability; MD), but some face challenges in both reading and math (RD + MD). Similarly, numerous students exhibit either LD or ADHD, but many embody both (i.e., LD + ADHD; e.g., [<reflink idref="bib8" id="ref11">8</reflink>]). This comorbidity is particularly concerning given that academic outcomes for students with LD + ADHD are significantly below those of students with LD or ADHD alone ([<reflink idref="bib46" id="ref12">46</reflink>]). Students with LD + ADHD can also experience long-term negative effects on social and behavior outcomes compared to students with LD or ADHD alone ([<reflink idref="bib46" id="ref13">46</reflink>]). High rates of comorbidity between LD and ADHD have prompted multiple investigations on the possible shared cognitive underpinnings of each disorder (e.g., [<reflink idref="bib26" id="ref14">26</reflink>]; [<reflink idref="bib29" id="ref15">29</reflink>]; [<reflink idref="bib49" id="ref16">49</reflink>]).</p> <hd id="AN0190688476-2">Theoretical Perspectives</hd> <p>Cognitive functioning is correlated to math and reading outcomes (e.g., [<reflink idref="bib24" id="ref17">24</reflink>]). Historically, a single deficit model explained difficulty in either reading or math. This model related unique needs to a specific cognitive process, or domain-general cognitive factor (e.g., processing speed is correlated with reading). Recent work highlights the importance of a multiple deficit framework, where each symptom dimension of a disorder can be predicted by several underlying cognitive factors ([<reflink idref="bib28" id="ref18">28</reflink>]). In addition, the correlated liabilities model, which suggests some cognitive factors associated with each disorder are shared by two or more disorders ([<reflink idref="bib28" id="ref19">28</reflink>]), has been used to explain similarities and differences across disorders. [<reflink idref="bib48" id="ref20">48</reflink>] expanded on this to provide a more nuanced interpretation. They suggested cognitive profiles across RD and MD may be so similar that identifying quantitative differences is more advantageous than focusing on minor qualitative distinctions across cognitive deficits. Identifying the extent of deficit across shared factors, specifically to domain-general cognitive deficits that are common across multiple disorders, may yield insights. They posit the importance of determining the intensity of the deficit among shared characteristics, as differing intensity may be one way to differentiate between shared factors. We situate our work within this multiple deficit framework ([<reflink idref="bib28" id="ref21">28</reflink>]) and the extent to which intensity may differentiate shared factors ([<reflink idref="bib48" id="ref22">48</reflink>]) to investigate the similarities and differences of reading, math, and cognitive outcomes in students with and without comorbid disorders.</p> <hd id="AN0190688476-3">RD Compared to MD</hd> <p>Previous research documents shared cognitive factors of RD and MD, such as verbal reasoning and processing speed deficits (Cirino et al., 2015; [<reflink idref="bib23" id="ref23">23</reflink>]; [<reflink idref="bib29" id="ref24">29</reflink>]; Willcutt et al., 2013, [<reflink idref="bib48" id="ref25">48</reflink>]). Studies consistently document verbal reasoning as a shared cognitive factor among students with RD and MD; however, findings surrounding processing speed differ. Although a number of studies document processing speed as a shared factor (e.g., Willcutt et al., 2013; [<reflink idref="bib48" id="ref26">48</reflink>]), some do not report a significant relationship (e.g., Cirino et al., 2015; [<reflink idref="bib23" id="ref27">23</reflink>]; [<reflink idref="bib29" id="ref28">29</reflink>]). Across multiple studies, processing speed difficulty is associated with RD, and general working memory difficulty is associated with MD. Interestingly, some research documents higher processing speed scores for students with RD + MD compared to MD alone; however, when examining RD compared to MD, working memory scores are significantly higher for RD than MD ([<reflink idref="bib40" id="ref29">40</reflink>]). Higher working memory scores in RD compared to MD are consistent with other studies, but higher outcomes on processing speed measures in RD compared to MD are not.</p> <p>Neuroimaging research documents brain activity when completing math and reading tasks in students with both RD and MD ([<reflink idref="bib18" id="ref30">18</reflink>]). In their meta-analysis, Martinez-Lincoln and colleagues (2023) investigated activity in domain-specific areas only (e.g., math-specific tasks for MD, reading-specific tasks for RD) when examining neuroimages for potential overlap or shared activity (or underactivity) in the brain. This suggests RD and MD have unique, domain-specific profiles, which confirms previous studies examining cognitive and domain-specific measures (e.g., Willcutt et al., 2013). They also documented underactivation in the regions of the brain associated with working memory, attention, and shifting tasks for people with MD (not RD). It is important to note, however, that the reading tasks included in the analysis were largely foundational in nature, such as word reading, with the most complex reading task at the sentence level (e.g., read a sentence and select the matching picture). As a result, there is a chance brain activity in regions associated with working memory, attention, and shifting may look similar when completing higher-level reading tasks for people with RD as well. This is certainly worth considering given the neuroimaging research documenting the use of executive function regions of the brain required for reading comprehension compared to word reading ([<reflink idref="bib1" id="ref31">1</reflink>]). Without additional research in this area, particularly for students with RD, MD, and comorbid RD + MD, we cannot be certain.</p> <hd id="AN0190688476-4">Comorbid RD + MD</hd> <p>Of students with LD, 30% to 70% have comorbid RD + MD ([<reflink idref="bib16" id="ref32">16</reflink>]; [<reflink idref="bib17" id="ref33">17</reflink>]; [<reflink idref="bib22" id="ref34">22</reflink>]). Students with comorbid RD + MD encounter greater overall academic impairment than students with RD or MD alone (Cirino et al., 2015; [<reflink idref="bib40" id="ref35">40</reflink>]; Willcutt et al., 2013, [<reflink idref="bib48" id="ref36">48</reflink>]).</p> <p>Although many researchers highlighted distinct differences between RD + MD and MD or RD alone due to unique cognitive profiles (e.g., Willcutt et al., 2013), others documented the additive profile of deficits in RD + MD ([<reflink idref="bib23" id="ref37">23</reflink>]). [<reflink idref="bib23" id="ref38">23</reflink>] did not find evidence of a significant interaction of RD by MD on any domain-general cognitive deficits, concluding a lack of uniqueness in cognitive profiles. Two previous meta-analyses ([<reflink idref="bib5" id="ref39">5</reflink>]; [<reflink idref="bib40" id="ref40">40</reflink>]) documented similar findings and suggested reading and math difficulties have common, domain-general risk factors, which may explain co-occurrence. Furthermore, a recent meta-analysis by [<reflink idref="bib45" id="ref41">45</reflink>] examined cognitive processes for students with math and reading difficulties and confirmed shared domain-general factors in many areas of executive functioning. They also documented unexpectedly similar findings in domain-specific tasks. For example, deficits in phonological processing were seen in both students with RD and MD. Many previous studies, including recent meta-analyses ([<reflink idref="bib27" id="ref42">27</reflink>]; [<reflink idref="bib50" id="ref43">50</reflink>]), did not identify phonological processing as a shared deficit.</p> <p>Students with RD + MD consistently score significantly below those with RD or MD alone on academic measures and cognitive measures (e.g., [<reflink idref="bib10" id="ref44">10</reflink>]; [<reflink idref="bib45" id="ref45">45</reflink>]; Willcutt et al., 2013). For example, when examining higher-order skills, such as word problem solving, students with MD consistently outperform those with RD + MD. These outcomes are likely related to oral language deficits, which are associated with RD in students with RD + MD ([<reflink idref="bib10" id="ref46">10</reflink>]). A recent systematic review examining the effects of math interventions on students with RD + MD and those with MD only provided additional evidence for the benefits of word-problem interventions for students with RD + MD ([<reflink idref="bib31" id="ref47">31</reflink>]). Authors documented larger effects for students with RD + MD who received word-problem intervention than students with MD only. Additional studies confirmed language-related needs in students with RD + MD by documenting greater deficits in phonological awareness and rapid naming in students with RD + MD as opposed to students with MD only (Cirino et al., 2015; [<reflink idref="bib24" id="ref48">24</reflink>]). Studies also consistently documented higher levels of impairment on cognitive measures in domain-general areas for students with comorbid RD + MD, such as working memory and processing speed (e.g., [<reflink idref="bib45" id="ref49">45</reflink>]). Interestingly, when ADHD was accounted for in the analysis, impairments in the RD + MD group compared to RD or MD alone were far less, suggesting a possible additive impairment when ADHD was present. Synthesizing the research in this area is key to determining how best to approach identification and intervention for students with one or both types of LD in addition to comorbid ADHD.</p> <hd id="AN0190688476-5">LD and ADHD</hd> <p>Any discussion of shared cognitive factors across MD and RD must account for the role of attention, given that ADHD is prevalent among students with LD. The ADHD consists of three unique symptom presentations: (a) inattentive, (b) hyperactive/impulsive, and (c) combined (both inattentive and hyperactive/impulsive). Of these, inattention (found both in inattentive and combined presentations) is consistently related to lower academic outcomes (e.g., [<reflink idref="bib20" id="ref50">20</reflink>]; [<reflink idref="bib42" id="ref51">42</reflink>]), whereas hyperactivity/impulsivity alone is not. This relation is documented in reading ([<reflink idref="bib30" id="ref52">30</reflink>]; [<reflink idref="bib33" id="ref53">33</reflink>]) and in mathematics ([<reflink idref="bib42" id="ref54">42</reflink>]). Because inattention is consistently correlated with lower reading and math outcomes (e.g., [<reflink idref="bib20" id="ref55">20</reflink>]; [<reflink idref="bib32" id="ref56">32</reflink>]; [<reflink idref="bib42" id="ref57">42</reflink>]), students with ADHD who have either inattentive or combined presentations may share more cognitive factors associated with LD than students with hyperactive/impulsive presentation alone.</p> <p>Numerous studies examined potential shared cognitive factors across ADHD and LD (e.g., [<reflink idref="bib21" id="ref58">21</reflink>]; [<reflink idref="bib26" id="ref59">26</reflink>]; [<reflink idref="bib29" id="ref60">29</reflink>]; [<reflink idref="bib41" id="ref61">41</reflink>]; [<reflink idref="bib43" id="ref62">43</reflink>]). [<reflink idref="bib43" id="ref63">43</reflink>] investigated the contributions of cognitive abilities to the relationship between ADHD symptoms and reading, math, and spelling outcomes. Early in their analysis, they identified a significant inverse relation between inattention scores and scores in reading, math, and spelling; however, when cognitive variables were added to the models, these associations did not persist. Their findings offered evidence that students with ADHD may share cognitive factors with students with MD and/or RD. Therefore, underlying cognitive factors associated with inattention may be responsible for lower math and reading outcomes.</p> <p>Although ADHD and LD appear to share many cognitive factors, evidence for some shared factors, such as working memory deficits, is mixed. [<reflink idref="bib29" id="ref64">29</reflink>] documented processing speed as a common predictor for RD + ADHD and MD + ADHD. They also found that verbal comprehension skills predicted ADHD + RD + MD but did not identify executive functions, in general, as a shared predictor. They noted working memory was a unique predictor of MD, and inhibition was a unique predictor of attention deficits. However, their reading measures consisted only of word reading and spelling tasks, with no higher-order skills included. Including these skills may yield different results, especially considering that working memory is utilized when comprehending text. Daucourt and colleagues (2020) examined shared genetic and environmental correlations between reading performance and ADHD as well as reading and math performance and found a stronger relationship between reading and math as it pertains to domain-general risk factors as compared to reading and ADHD. Both relationships were significant, but the math and reading correlations were much stronger. They recommended considering domain-specific risk factors when examining co-occurring cases of RD and ADHD symptoms. It is unclear how many measures of higher-level reading tasks were included in their analysis, which leaves room for the possibility of stronger relationships between shared domain-general factors across those with RD and ADHD.</p> <p>Other studies did highlight working memory as a shared, domain-general cognitive underpinning of ADHD and LD ([<reflink idref="bib21" id="ref65">21</reflink>]; [<reflink idref="bib26" id="ref66">26</reflink>]; [<reflink idref="bib41" id="ref67">41</reflink>]). [<reflink idref="bib26" id="ref68">26</reflink>] and [<reflink idref="bib41" id="ref69">41</reflink>] did not include academic measures in their work. Instead, they focused on cognitive factors associated with LD + ADHD. [<reflink idref="bib26" id="ref70">26</reflink>] saw significantly lower working memory and processing speed scores for students with RD + ADHD compared to students with ADHD alone. [<reflink idref="bib41" id="ref71">41</reflink>] also reported significantly lower working memory and processing speed scores for students with LD. They did not disaggregate findings based on type of LD. However, the majority of the sample consisted of students with either RD (42%) or RD + MD (35%). Similarities highlighted remain consistent with the correlated liabilities model. [<reflink idref="bib21" id="ref72">21</reflink>] examined the relationship between LD, executive function, and psychopathology in students with ADHD. Similar to previous studies, they also found that students with LD + ADHD performed significantly lower on working memory and processing speed tasks compared to students with ADHD alone. Much like Toffalini et al., they did not disaggregate findings by LD type. Findings represent a sample of students who had at least one LD in reading (39.1%), math (30.3%), or written expression (62.4%). They did not report the percentage with comorbid LD. Students also scored significantly lower on a composite score of executive functioning and on a measure of visual-motor integration.</p> <hd id="AN0190688476-6">This Study</hd> <p>Although numerous meta-analyses examine similar topics, such as brain structure and neuroimaging of individuals with RD and MD ([<reflink idref="bib18" id="ref73">18</reflink>]) and cognitive functioning of students with RD + MD ([<reflink idref="bib40" id="ref74">40</reflink>]; [<reflink idref="bib45" id="ref75">45</reflink>]), none have examined reading, math, and cognitive outcomes across students with LD (RD or MD), comorbid LD (RD + MD), ADHD, and comorbid LD + ADHD. One meta-analysis by Daucourt and colleagues (2020) examined genetic and environmental correlations between reading and ADHD symptoms and reading and math; however, they did not investigate the magnitude of differences across reading, math, and cognitive outcomes across individuals with LD, comorbid LD, ADHD, and comorbid LD + ADHD. We aim to synthesize data on this topic to (a) inform researchers as they design and test interventions, (b) provide additional insights for practitioners as they consider ways to best support these students, (c) support researchers and practitioners in considering ways of identifying students who may be at risk for learning difficulties, including comorbid disorders, and (d) engage in a more nuanced discussion of future directions, highlighting heavily researched outcomes and those that warrant additional study. We investigated the differences between five groups of students (those with RD only, MD only, ADHD only, RD + MD, and RD + ADHD) on measures of cognitive ability, reading, and mathematics across the published research literature by conducting a systematic review.</p> <p>We did not include students with MD + ADHD or RD + MD + ADHD due to a lack of studies examining these populations. Some studies provided descriptive information on these students; however, we could not include many in our review because they did not disaggregate results by LD type (MD and/or RD). Therefore, our inquiry sought to answer the following research questions:</p> <p></p> <ulist> <item> <bold> Research Question 1 (RQ1): </bold> To what extent do students identified with RD + MD exhibit more severe difficulty in reading, math, and cognitive ability compared to students with RD only and MD only?</item> <p></p> <item> <bold> Research Question 2 (RQ2): </bold> To what extent do students identified with RD + ADHD exhibit more severe difficulty in reading and cognitive ability compared to students with RD only or ADHD only?</item> </ulist> <hd id="AN0190688476-7">Method</hd> <p>We sought to conduct a systematic review and synthesis on studies that included one or more of the group comparisons relevant to our research questions (students with MD, RD, ADHD, MD + RD, or ADHD + RD) examining effect sizes within three domains: cognitive ability, reading skills, and math skills. In addition, we aimed to report and discuss effect sizes across cognitive outcomes to provide a detailed comparison across groups.</p> <hd id="AN0190688476-8">Literature Search</hd> <p>Our literature search was part of a larger project seeking to locate studies related to the identification of or interventions for students with reading or math disabilities or difficulties. Given that our interests focused on psychological and education research studies, we searched the PsycInfo database using the following terms: (reading difficult* OR learning disab* OR LD OR mild handi* OR mild disab* OR reading disab* OR RD OR reading delay* OR learning delay* OR struggling read* OR reading difficult* OR dyslex* OR comorbid* OR math disab* OR math delay OR struggling math OR math diff* OR dyscalc*) AND (read* OR English Language Arts OR math*) AND (identif* OR interven* OR program OR meta-analysis OR synthesis). We refined the search to include journals, academic journals, reviews, conference materials, and books published in English between 1889 (the earliest date for which literature is indexed in the PsycInfo database) and 2021. The initial search resulted in 8,628 abstracts. Raters trained by the second author to 98% agreement on a set of abstracts screened the remaining abstracts based on the inclusion criteria for this project and others within this area of inquiry. After reviewing the abstracts and full texts for the studies identified as possible matches for this project, we determined that 16 studies met the inclusion criteria. To locate additional data, we also conducted a hand search of all journals represented in our corpus of studies from 2019 to 2021; however, no additional studies qualified for inclusion in our review.</p> <hd id="AN0190688476-9">Inclusion Criteria</hd> <p>The 16 included studies met the following criteria:</p> <p></p> <ulist> <item> Published in English in 2021 or earlier.</item> <p></p> <item> Purpose of the study in whole or part was to contribute to the knowledge base on learning disabilities in reading, math, or both or similarities and differences between students with learning disabilities (RD, MD, or RD + MD) and those with ADHD.</item> <p></p> <item> Participants were English speakers in Grades K–12 or ages 5 to 21 years. If 50% or more were in the target age/grade range or if disaggregated data were provided, the study was included.</item> <p></p> <item> Quantitative data were reported for at least two of the following groups: (a) students identified as low achieving in reading or as having RD based on state, school, or researcher-specified criteria; (b) students identified as low achieving in math or as having MD based on state, school, or researcher-specified criteria; (c) students identified as having ADHD based on medical, psychological, or researcher-specified criteria; and (d) students identified as having comorbid disabilities in two or more of the above categories. Studies that included two or more of these groups but also included students with other types of disabilities were retained, along with those in which some students were English learners. Studies focused exclusively on English learners were not included.</item> <p></p> <item> The reported data were sufficient for computing a standardized mean difference effect size from a measure of reading, math, or cognitive ability.</item> <p></p> <item> The measures for which data were reported were not used to determine whether the participants had RD, MD, ADHD, or more than one disability. When studies reported data used to identify participants as having one or more disabilities and additional data not used in this manner, we calculated effect sizes only for measures not used for identification.</item> </ulist> <p>Of the 48 studies that were screened but did not meet the inclusion criteria, most (32 studies) were dropped because they did not present data that compared at least two of the groups that were the focus of our research questions. The remaining studies were eliminated because they did not contain the data needed to compute standardized mean difference effect sizes (<emph>k</emph> = 8); all students in the study were non-English speakers (<emph>k</emph> = 5); the only data presented were the data used to group students as having RD, MD, RD + MD, ADHD, or RD + ADHD (<emph>k</emph> = 2); or the study did not include students in the target age or grade range (<emph>k</emph> = 1).</p> <hd id="AN0190688476-10">Coding Procedures</hd> <p>The codesheet and coding procedures manual were similar to those developed by [<reflink idref="bib44" id="ref76">44</reflink>] and used in many prior meta-analyses and systematic reviews (e.g., [<reflink idref="bib36" id="ref77">36</reflink>]). For this project, the codesheet was adapted to be appropriate for studies that primarily focused on the identification of students with MD, RD, ADHD, or the comparison of students with more than one disability. The codesheet captured data including: (a) participant characteristics (e.g., gender, socioeconomic status [SES], age/grade); (b) description of types of participants included (e.g., identified as RD/MD/ADHD, struggling in reading or math); (c) description of the method for determining that students have RD, MD, or ADHD; (d) measures administered; (e) study findings; and (f) data for computing effect sizes. Our coding manual provided operational definitions of all information to be coded as well as our procedures for establishing reliability across coders.</p> <p>All 16 studies were coded by two independent members of the research team who were part of a group of seven raters with the second author being the gold standard ([<reflink idref="bib12" id="ref78">12</reflink>]). To establish initial inter-rater reliability before coding, all coders coded an article not included in our review and met or surpassed.95 reliability (calculated as the number of agreements minus the number of disagreements divided by the total number of items coded) across all coding items. During coding, when raters did not agree, the second author decided the correct code.</p> <hd id="AN0190688476-11">Data Analysis</hd> <p>We calculated effect sizes for each academic domain measure (reading, cognitive, and math) in each study. The standardized mean difference effect size comparing students with comorbid and singular disabilities was calculated using Hedges' <emph>g</emph>, which is an unbiased effect size estimate ([<reflink idref="bib13" id="ref79">13</reflink>]).</p> <hd id="AN0190688476-12">Results</hd> <p>A summary of characteristics of all 16 studies is presented in a Supplemental File (see Table S1). Sample sizes range from 38 to 260 participants, resulting in a total sample of 1,910 participants. In addition, some studies reported whether or not participants received special education services (i.e., students had a classification of RD and/or MD); however, some studies included students who were classified as having <emph>difficulty</emph> with reading and/or math based on various criteria described in each paper. We included these students in our review due to their documented areas of need in reading and/or math. There is also a chance these students may have received special education services under a formal LD classification, but this information was not reported. As a result, we included them in our sample. Below, we describe results from the synthesis pertaining to effect sizes across student populations. Average effect sizes across studies by comparison are presented in Tables 1–4. For ease of summarizing findings, tables include mean effect sizes within the three major categories of interest; however, Supplemental Tables S2–S6 provide a detailed overview of effect sizes by comparison. These tables provide insight on the constructs measured within each category, such as working memory and processing speed within cognitive outcomes and word problem solving within math outcomes. In addition, we provided a detailed summary of the average cognitive effect sizes by domain-general cognitive factors in Table 4. We interpreted effect sizes less than 0.05 in either direction as an indication of similar performance across groups (no relative differences between groups).</p> <p>Table 1. Effect Size Between Reading Disability (RD) and Mathematics Disability (MD) by Domain and Study.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="center">Domain</th><th align="center">Study</th><th align="center">Year</th><th align="center">Mean ES</th><th align="center">Lowest ES</th><th align="center">Highest ES</th><th align="center"><italic>m</italic></th></tr></thead><tbody><tr><td>Reading</td><td>D'Angiulli & Siegel</td><td>2003</td><td>−1.514</td><td>−3.019</td><td>−0.400</td><td>3</td></tr><tr><td /><td>Snowling et al.</td><td>2021</td><td>0.435</td><td>0.340</td><td>0.529</td><td>2</td></tr><tr><td /><td>Willcutt et al.</td><td>2013</td><td>−0.682</td><td>−0.682</td><td>−0.682</td><td>1</td></tr><tr><td>Math</td><td>Cirino et al.</td><td>2015</td><td>0.244</td><td>−0.040</td><td>0.548</td><td>8</td></tr><tr><td>Cognitive</td><td>Cirino et al.</td><td>2015</td><td>0.027</td><td>−0.348</td><td>0.418</td><td>8</td></tr><tr><td /><td>D'Angiulli & Siegel</td><td>2003</td><td>−0.304</td><td>−0.613</td><td>0.037</td><td>16</td></tr><tr><td /><td>Davis</td><td>1997</td><td>0.154</td><td>−0.202</td><td>0.648</td><td>13</td></tr><tr><td /><td>Geary et al.</td><td>1999</td><td>0.378</td><td>0.144</td><td>0.683</td><td>4</td></tr><tr><td /><td>Geary et al.</td><td>2000</td><td>−0.025</td><td>−0.566</td><td>0.666</td><td>10</td></tr><tr><td /><td>Snowling et al.</td><td>2021</td><td>0.384</td><td>−1.250</td><td>1.069</td><td>7</td></tr><tr><td /><td>Willcutt et al.</td><td>2013</td><td>−0.070</td><td>−0.497</td><td>0.209</td><td>6</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note</emph>. Negative ES indicates the achievement of students with RD is lower than that of students with MD. <emph>m</emph> = total number of effect sizes used to calculate a mean effect.</p> <p>Table 2. Effect Size Between Comorbid Learning Disability and Mathematics Disability/Reading Disability by Domain and Study.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="center">Comparison</th><th align="center">Domain</th><th align="center">Study</th><th align="center">Year</th><th align="center">Mean ES</th><th align="center">Lowest ES</th><th align="center">Highest ES</th><th align="center"><italic>m</italic></th></tr></thead><tbody><tr><td colspan="8">RD + MD and MD</td></tr><tr><td /><td>Reading</td><td>Snowling et al.</td><td>2021</td><td>−0.504</td><td>−0.595</td><td>−0.412</td><td>2</td></tr><tr><td /><td /><td>Willcutt et al.</td><td>2013</td><td>−1.230</td><td>−1.230</td><td>−1.230</td><td>1</td></tr><tr><td /><td>Math</td><td>Cirino et al.</td><td>2015</td><td>−0.244</td><td>−0.329</td><td>−0.149</td><td>8</td></tr><tr><td /><td /><td>Jitendra et al.</td><td>2016</td><td>−0.476</td><td>−0.934</td><td>−0.275</td><td>4</td></tr><tr><td /><td /><td>Vukovic</td><td>2012</td><td>−0.418</td><td>−0.783</td><td>−0.117</td><td>4</td></tr><tr><td /><td>Cognitive</td><td>Cirino et al.</td><td>2015</td><td>−0.256</td><td>−0.507</td><td>−0.080</td><td>8</td></tr><tr><td /><td /><td>Geary et al.</td><td>1999</td><td>0.189</td><td>−0.570</td><td>0.842</td><td>4</td></tr><tr><td /><td /><td>Geary et al.</td><td>2000</td><td>−0.133</td><td>−0.717</td><td>0.555</td><td>10</td></tr><tr><td /><td /><td>Snowling et al.</td><td>2021</td><td>−0.157</td><td>−0.684</td><td>0.287</td><td>7</td></tr><tr><td /><td /><td>Vukovic</td><td>2012</td><td>−0.348</td><td>−0.744</td><td>−0.069</td><td>6</td></tr><tr><td /><td /><td>Willcutt et al.</td><td>2013</td><td>−0.503</td><td>−0.883</td><td>−0.081</td><td>6</td></tr><tr><td colspan="8">RD + MD and RD</td></tr><tr><td /><td>Reading</td><td>Snowling et al.</td><td>2021</td><td>−0.936</td><td>−1.009</td><td>−0.862</td><td>2</td></tr><tr><td /><td /><td>Willcutt et al.</td><td>2013</td><td>−0.615</td><td>−0.615</td><td>−0.615</td><td>1</td></tr><tr><td /><td>Math</td><td>Cirino et al.</td><td>2015</td><td>−0.489</td><td>−0.716</td><td>−0.279</td><td>8</td></tr><tr><td /><td>Cognitive</td><td>Cirino et al.</td><td>2015</td><td>−0.318</td><td>−0.497</td><td>0.080</td><td>8</td></tr><tr><td /><td /><td>Geary et al.</td><td>1999</td><td>−0.223</td><td>−0.689</td><td>0.075</td><td>4</td></tr><tr><td /><td /><td>Geary et al.</td><td>2000</td><td>−0.121</td><td>−0.649</td><td>0.278</td><td>10</td></tr><tr><td /><td /><td>Snowlling et al.</td><td>2019</td><td>−0.494</td><td>−0.946</td><td>0.755</td><td>7</td></tr><tr><td /><td /><td>Swanson & Howard</td><td>2005</td><td>0.522</td><td>0.336</td><td>0.637</td><td>4</td></tr><tr><td /><td /><td>Willcutt et al.</td><td>2013</td><td>−0.456</td><td>−0.877</td><td>0.061</td><td>6</td></tr></tbody></table> </ephtml> </p> <p>2 <emph>Note</emph>. Negative ES indicates the achievement of students with MD/RD is lower than that of students with MD or RD. <emph>m</emph> = total number of effect sizes used to calculate a mean effect.</p> <p>Table 3. Effect Size Between Reading Disability (RD) and Attention-Deficit/Hyperactivity Disorder (ADHD) by Domain and Study.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="center">Comparison</th><th align="center">Domain</th><th align="center">Study</th><th align="center">Year</th><th align="center">Mean ES</th><th align="center">Lowest ES</th><th align="center">Highest ES</th><th align="center"><italic>m</italic></th></tr></thead><tbody><tr><td colspan="8">RD and ADHD</td></tr><tr><td /><td>Reading</td><td>Aaron et al.</td><td>2002</td><td>−0.185</td><td>−1.810</td><td>1.670</td><td>6</td></tr><tr><td /><td /><td>Breier et al.</td><td>2001</td><td>−1.778</td><td>−2.260</td><td>−0.862</td><td>5</td></tr><tr><td /><td /><td>Dalby</td><td>1985</td><td>−1.534</td><td>−1.650</td><td>−1.417</td><td>2</td></tr><tr><td /><td /><td>Willcutt et al.</td><td>2007</td><td>−1.199</td><td>−1.563</td><td>−0.835</td><td>2</td></tr><tr><td /><td>Math</td><td>Dalby</td><td>1985</td><td>−1.254</td><td>−1.254</td><td>−1.254</td><td>1</td></tr><tr><td /><td>Cognitive</td><td>Breier et al.</td><td>2001</td><td>−0.826</td><td>−0.921</td><td>−0.730</td><td>2</td></tr><tr><td /><td /><td>Dalby</td><td>1985</td><td>1.298</td><td>−0.607</td><td>4.604</td><td>5</td></tr><tr><td /><td /><td>Dewey et al.</td><td>2003</td><td>−0.536</td><td>−0.594</td><td>−0.477</td><td>2</td></tr><tr><td /><td /><td>Rucklidge & Tannock</td><td>2002</td><td>−0.033</td><td>−0.468</td><td>0.838</td><td>8</td></tr><tr><td colspan="8">RD + ADHD and ADHD</td></tr><tr><td /><td>Reading</td><td>Aaron et al.</td><td>2002</td><td>−0.959</td><td>−2.336</td><td>0.026</td><td>6</td></tr><tr><td /><td /><td>Breier et al.</td><td>2001</td><td>−1.524</td><td>−2.130</td><td>−0.468</td><td>5</td></tr><tr><td /><td /><td>Willcutt et al.</td><td>2007</td><td>−1.759</td><td>−2.414</td><td>−1.104</td><td>2</td></tr><tr><td /><td>Cognitive</td><td>Breier et al.</td><td>2001</td><td>−0.818</td><td>−0.856</td><td>0.781</td><td>2</td></tr><tr><td /><td /><td>Dewey et al.</td><td>2003</td><td>−0.411</td><td>−0.414</td><td>−0.408</td><td>2</td></tr><tr><td /><td /><td>Rucklidge & Tannock</td><td>2002</td><td>−0.583</td><td>−1.253</td><td>0.204</td><td>8</td></tr></tbody></table> </ephtml> </p> <p>3 <emph>Note</emph>. Negative ES indicates the achievement of students with RD and RD + ADHD are lower than that of students with ADHD. <emph>m</emph> = total number of effect sizes used to calculate a mean effect.</p> <p>Table 4. Mean Cognitive ES Across Studies.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="center">Comparison</th><th align="center">Executive function</th><th align="center">Attention</th><th align="center">Working memory</th><th align="center">Short-term memory</th><th align="center">Long-term memory</th><th align="center">General memory</th><th align="center">Phonological memory</th><th align="center">Processing speed</th><th align="center">Perceptual reasoning</th><th align="center">Verbal comprehension</th><th align="center">Full-scale IQ</th><th align="center">Phonological processing</th><th align="center">VMI</th></tr></thead><tbody><tr><td>RD vs. MD</td><td>0.916</td><td>0.046</td><td>−0.078</td><td>n/a</td><td>0.197</td><td>n/a</td><td>n/a</td><td>−0.195</td><td>0.342</td><td>−0.232</td><td>−0.379</td><td>−0.348</td><td>−0.390</td></tr><tr><td>RD + MD vs. MD</td><td>0.195</td><td>−0.482</td><td>−0.366</td><td>−0.725</td><td>0.246</td><td>n/a</td><td>n/a</td><td>−0.441</td><td>0.175</td><td>−0.445</td><td>n/a</td><td>−0.587</td><td>n/a</td></tr><tr><td>RD + MD vs. RD</td><td>−0.634</td><td>−0.389</td><td>−0.128</td><td>n/a</td><td>0.019</td><td>n/a</td><td>n/a</td><td>−0.354</td><td>−0.512</td><td>−0.399</td><td>n/a</td><td>−0.497</td><td>n/a</td></tr><tr><td>RD vs. ADHD</td><td>n/a</td><td>2.222</td><td>−0.403</td><td>n/a</td><td>n/a</td><td>−0.594</td><td>−0.730</td><td>0.033</td><td>0.229</td><td>−0.607</td><td>−0.373</td><td>n/a</td><td>n/a</td></tr><tr><td>RD + ADHD vs. ADHD</td><td>n/a</td><td>−1.253</td><td>−0.996</td><td>n/a</td><td>n/a</td><td>−0.408</td><td>−0.781</td><td>−0.072</td><td>n/a</td><td>n/a</td><td>−0.414</td><td>n/a</td><td>n/a</td></tr></tbody></table> </ephtml> </p> <p>4 <emph>Note.</emph> Negative ES means lower performance by the first group listed in the comparison; VMI = visual-motor integration; RD = reading disability; MD = mathematics disability; ADHD = attention-deficit/hyperactivity disability.</p> <hd id="AN0190688476-13">Differences Between Students With RD, MD, and RD + MD</hd> <p></p> <hd id="AN0190688476-14">Effect Sizes by Study for Students With RD Versus MD</hd> <p>Table 1 and Supplemental Table S2 compare students with RD and those with MD on measures of reading, math, and cognitive ability. In the reading domain, the average effect size between students with RD and MD consistently indicated higher reading scores for students with MD (<emph>g</emph> = −1.514; D'Angiulli & Siegel, 2003; <emph>g</emph> = −0.682; Willcutt et al., 2013), but one study documented higher outcomes on measures of language and phonemic awareness for students with RD (mean effect size: <emph>g</emph> = 0.435; Snowling et al., 2021). The results were similar in the math domain, where students with MD had lower scores than students with RD across all but two math measures, which did not indicate notable differences in either direction between groups (i.e., numeration: <emph>g</emph> = 0.040; number line: <emph>g</emph> = −0.020; Cirino et al., 2015). Mean effect sizes for studies comparing measures of cognitive abilities ranged from −0.304 to 0.384. Examination of cognitive effect sizes (see Table 4) showed mean effects that favor MD in all areas except one measure of executive functioning (<emph>g</emph> = 0.916; Snowling et al., 2021), long-term memory (<emph>g</emph> = 0.197), and perceptual reasoning (<emph>g</emph> = 0.342). Mean effects for attention indicate similar performance across groups (<emph>g</emph> = 0.046).</p> <hd id="AN0190688476-15">Effect Sizes by Study for Students With RD + MD Versus MD</hd> <p>Differences between students with comorbid RD + MD and students with MD are shown in Table 2 and Supplemental Table S3. On measures of reading, all effect sizes showed higher levels of need in the RD + MD group, with means ranging from −1.230 (Willcutt et al., 2013) to −0.504 (Snowling et al., 2021). In math, students with RD + MD also demonstrated lower skill levels on average than students with MD across all studies, with mean effect sizes ranging from −0.476 to −0.244.</p> <p>Findings for the cognitive domain were less consistent. Mean effect sizes across all but one study (i.e., Geary et al., 1999) were negative (see Table 2), suggesting higher overall impairment in the RD + MD group. Upon further inspection of individual effects (see Supplemental Table S3), there are a few instances in which the RD + MD group outperformed the MD group. Specifically, the comorbid group outperformed the MD group on measures of long-term memory that included words and numbers (effect sizes ranging from 0.95 to 0.842; Geary et al., 1999, 2000). In addition, the comorbid group outperformed the MD group on one measure of executive functioning (<emph>g</emph> = 0.195; Snowling et al., 2021) and two measures of perceptual reasoning (<emph>g</emph> = 0.216 and 0.287; Snowling et al., 2021), including a composite score combining the two subtests (<emph>g</emph> = 0.285; Snowling et al., 2021). One measure of long-term memory indicated similar performance across groups with an effect size of 0.000 (Geary et al., 2000). Interestingly, both instances in which the MD group outperformed the comorbid group on long-term memory tasks included nonwords. All other long-term memory tasks in which the RD + MD group outperformed the MD group contained real words and numbers. Effect sizes for perceptual reasoning consistently favored the RD + MD group except for one matrix task (<emph>g</emph> = −0.090; Cirino et al., 2015). Overall, the majority of effect sizes across reading, math, and cognition showed higher levels of need in the RD + MD with few exceptions.</p> <hd id="AN0190688476-16">Effect Sizes by Study for Students With RD + MD Versus RD</hd> <p>Effect sizes comparing students with RD + MD to those with RD are shown in Table 2 and Supplemental Table S4. In reading, all studies yielded negative mean effects. Cirino et al.'s study (2015) was the only study to compare these groups on math measures. They found that students with RD + MD had lower scores than students with RD across eight measures, as expected, with an average effect size of −0.489.</p> <p>On measures of cognitive ability, average effect sizes across all studies were negative with one exception (<emph>g</emph> = 0.522; Swanson & Howard, 2005). Table 4 indicates higher levels of need across all cognitive domains except for long-term memory, which was relatively similar across groups (<emph>g</emph> = 0.019). Closer inspection of cognitive effects across studies (see Supplemental Table S4) suggests higher levels of impairment in the RD + MD condition with few exceptions. Similar to the RD + MD and MD comparison, the comorbid group outperformed the RD group on multiple long-term memory tasks except for two nonword tasks, in which the RD group earned a higher score (<emph>g</emph> = −0.276; Geary et al., 1999; <emph>g</emph> = −0.266; Geary et al., 2000). In addition, there was one instance in which the RD group had a higher score on a number task (<emph>g</emph> = −0.150). One other measure of long-term memory (number task) indicated similar performance across groups with an effect size of 0.000 (Geary et al., 1999). On measures of working memory, studies consistently indicated higher levels of impairment for the RD + MD group compared to the RD group, except for Swanson and Howard (2005), who documented higher scores in the comorbid group than the RD group on four measures. Still, the average effect size across all working memory measures was negative (<emph>g</emph> = −0.128), suggesting lower overall performance by students in the comorbid group. Attention measures favor the RD group on all but one measure of hyperactivity/impulsivity (<emph>g</emph> = 0.061; Willcutt et al., 2013). All measures of inattention documented higher scores in the RD group compared to the RD + MD group.</p> <hd id="AN0190688476-17">Differences Between Students With RD, ADHD, and RD + ADHD</hd> <p></p> <hd id="AN0190688476-18">Effect Sizes by Study for Students With RD Versus ADHD</hd> <p>Studies in Table 3 and Supplemental Table S5 compare students with RD and with ADHD. Effect sizes indicated lower reading scores on average for students with RD in all four studies (Aaron et al., 2002; Breier et al., 2001; Dalby, 1985; Willcutt et al., 2013), with average effect sizes ranging from −1.778 to −0.185. Further examination of individual effects across studies (see Supplemental Table S5) documents some instances in which students with RD outperformed students with ADHD. Of the four studies that reported reading outcomes, Aaron et al.'s study (2002) was the only study to indicate three positive effect sizes, suggesting the RD group outperformed the ADHD group on measures of listening comprehension, listening vocabulary, and reading comprehension (<emph>g</emph> = 1.671, 0.856, 0.151, respectively). All other studies documented negative effect sizes on reading outcomes for students with RD, including another reading comprehension outcome (<emph>g</emph> = −0.835; Willcutt et al., 2007). One study reported a math outcome for students with RD and students with ADHD (Dalby, 1985). The effect size for arithmetic indicated higher levels of need in the RD group compared to the ADHD group (<emph>g</emph> = −1.254).</p> <p>For cognitive ability, two of the four studies had negative average effects ranging from −0.826 to −0.536 (Breier et al., 2001; Dewey et al., 2003). One study did not yield average effects that indicate differences between groups (<emph>g</emph> = −0.033; Rucklidge & Tannock, 2002). Dalby (1985) had a positive average effect (<emph>g</emph> = 1.298). However, two large positive effects were reported for measures of attention (see Supplemental Table S5). Students with RD had much lower scores on parent and teacher hyperactivity and impulsivity rating scales than students with ADHD (<emph>g</emph> = 4.604, 2.532, respectively). Interestingly, one effect size from an attention measure in Rucklidge and Tannock (2002) was negative (<emph>g</emph> = −0.468), suggesting greater deficits for students with RD compared to those with ADHD. Still, average attention effects were positive, suggesting higher levels of attention difficulties in the ADHD group compared to the RD group. Table 4 highlights average cognitive effect sizes. Students with RD averaged lower scores on measures of working memory (<emph>g</emph> = −0.403), general memory, phonological memory (<emph>g</emph> = −0.594), verbal comprehension (<emph>g</emph> = −0.607), phonological memory (<emph>g</emph> = −0.730), and full-scale IQ (<emph>g</emph> = −0.373) compared to the ADHD group. The ADHD group earned lower average scores than the RD group on measures of attention (<emph>g</emph> = 2.222) and perceptual reasoning (<emph>g</emph> = 0.229). The average mean effect on measures of processing speed did not indicate differences between groups (<emph>g</emph> = 0.033). Further inspection of individual measures of processing speed indicates mixed results. One study reports higher levels of need in the RD group on measures of processing speed (<emph>g</emph> = 0.921; Breier et al., 2000); however, another study indicates higher levels of need in the ADHD group on two measures of processing speed (coding: <emph>g</emph> = 0.838; symbol search: <emph>g</emph> = 0.323; Rucklidge & Tannock, 2002) and the corresponding composite score of those two measures (processing speed composite: <emph>g</emph> = 0.654).</p> <hd id="AN0190688476-19">Effect Sizes by Study for Students With RD + ADHD Versus ADHD</hd> <p>Results in Table 3 and Supplemental Table S6 show differences between students with RD + ADHD and ADHD. Effect sizes on reading measures across all studies (see Supplemental Table S6) were negative except for one reading comprehension measure in Aaron et al. (2002), in which no notable differences were observed between groups (<emph>g</emph> = 0.026). No math outcomes were reported across studies.</p> <p>On cognitive measures, students with RD + ADHD had lower average scores than those with ADHD across all studies (Brier et al., 2001; Dewey, 2003; Willcutt et al., 2007). Average effect sizes ranged from −0.818 to −0.411. Individual effects (see Supplemental Table S6) were all negative with the exception of three measures of processing speed (composite: <emph>g</emph> = 0.204; coding: <emph>g</emph> = 0.184; symbol search: <emph>g</emph> = 0.182; Rucklidge & Tannock, 2002). Average effect sizes across all studies (see Table 4) were also negative on measures of attention (<emph>g</emph> = 1.253), working memory (<emph>g</emph> = −0.996), general memory (<emph>g</emph> = −0.408), phonological memory (<emph>g</emph> = −0.781), processing speed (<emph>g</emph> = −0.072), and full-scale IQ (<emph>g</emph> = −0.414). Effects suggested higher levels of need in these areas for students with comorbid RD + ADHD.</p> <hd id="AN0190688476-20">Discussion</hd> <p>We aimed to investigate differences in cognitive ability and reading and mathematics skills between students with RD, MD, RD + MD, ADHD, and RD + ADHD. Findings from our systematic review documented lower average scores in areas of working memory, processing speed, verbal comprehension, full-scale IQ, phonological processing, and visual-motor integration for students with RD. All effect sizes indicated higher reading scores for students with MD as compared to students with RD, except for two in the areas of language comprehension and phonemic awareness. Higher performance for the MD group was not surprising given that language skills likely would not be hindered in students with MD, resulting in higher reading outcomes. This finding is also consistent with previous studies documenting higher reading scores for students with MD as compared to those with RD (e.g., Cirino et al., 2015; [<reflink idref="bib24" id="ref80">24</reflink>]). The two scores indicating a higher performance in the area of language comprehension and phonemic awareness reported by Snowling and colleagues (2021), however, were unexpected. Also not surprising, scores on all math assessments were lower for students with MD than students with RD (Cirino et al., 2015). The magnitude of differences was substantial between students with MD and those with RD on reading and math measures. Cognitive assessments indicated lower scores for students with RD compared to students with MD in roughly half of the studies. Furthermore, the average working memory effect size slightly favored the MD group compared to the RD group (<emph>g</emph> = −0.078). This was unexpected as working memory is typically seen as a difficulty associated with MD rather than RD (e.g., [<reflink idref="bib18" id="ref81">18</reflink>]; [<reflink idref="bib40" id="ref82">40</reflink>]). However, two large negative effects (showing greater need in the RD group; see Supplemental Table S2) impacted the overall mean effect. Most other effect sizes either were close to zero or showed greater need in the MD group.</p> <p>Average effect sizes across studies suggested the RD + MD group scored lower than students with RD or MD on all reading and math measures. All but two mean cognitive effect sizes (Geary et al., 1999; Swanson & Howard, 2005) also indicate greater deficits for the RD + MD group. Closer inspection of individual effect sizes and average effect sizes across domain-general cognitive domains reveals some instances in which the RD + MD group outperformed the RD group and the MD group. For example, students in the RD + MD group outperformed those in the MD group and the RD group on some long-term memory tasks, mainly those that included real words and numbers (Geary et al., 1999, 2000). The RD + MD group earned higher scores on measures of processing speed (Snowling et al., 2021) and working memory (Swanson & Howard, 2005) compared to the RD group in some instances. These exceptions were unexpected, as previous literature consistently documents lower scores for students with RD + MD compared to those with RD or MD alone (e.g., [<reflink idref="bib10" id="ref83">10</reflink>]; Willcutt et al., 2013). Still, the overwhelming majority of results documented lower scores for students with RD + MD across cognitive, reading, and mathematics measures, suggesting students with RD + MD may experience more difficulty compared to students with RD or MD.</p> <p>Findings from our review also allowed us to consider differences in reading and cognitive scores for students with RD compared to students with ADHD. Average effect sizes across all studies but one (Dalby, 1985) indicated lower scores for students with RD compared to students with ADHD. Although many students with ADHD, specifically those with high levels of inattention, tended to earn lower scores than their typically developing peers on measures of fluency, vocabulary, and reading comprehension ([<reflink idref="bib11" id="ref84">11</reflink>]; [<reflink idref="bib19" id="ref85">19</reflink>]), some students with high levels of inattention earned average scores on word reading measures. As a result, it is important to consider the types of measures included in the studies reported in Table 3. All but two studies (Aaron et al., 2002; Willcutt et al., 2007) used measures of word reading, spelling, or phonemic awareness. As a result, students with RD would be expected to earn lower scores on these measures compared to students with ADHD.</p> <p>Aaron et al. (2002) reported three effect sizes in which students with RD outperformed those with ADHD. Two of these required listening tasks (listening comprehension and listening vocabulary). Because some students with RD may not have specific needs related to executive functioning, they may perform better on listening tasks compared to students with attention deficits who experience challenges with executive functioning. However, many students with RD also experience language deficits that may impact listening tasks. Still, many students with RD specifically struggle at the word reading level (e.g., dyslexia) and experience little difficulty with listening tasks. The other exception included paragraph comprehension. Although students with RD performed slightly higher than those with inattention, the mean score differed by just over one point, with an average score of 83.44 for students with RD compared to 82.11 for students with inattention. Because students with RD often face challenges at the word reading level, they may struggle with comprehending text that requires both word reading and language comprehension. Many students with inattention tend to read words successfully ([<reflink idref="bib11" id="ref86">11</reflink>]); however, a breakdown can occur when putting information together to make sense of it. Both student profiles may face unique challenges comprehending text, making similar scores on these measures expected. Findings reported by Aaron et al. (2002) confirmed this expectation.</p> <p>Unexpectedly, some cognitive effect sizes showed higher levels of need for students with ADHD than those with RD. Although it is not surprising that students with ADHD had higher levels of impairment on measures of attention, it is surprising that students with ADHD had somewhat greater levels of impairment on measures of processing speed and perceptual reasoning. These findings are not consistent with previous research documenting significantly lower scores on processing speed measures for students with LD compared to those with ADHD ([<reflink idref="bib41" id="ref87">41</reflink>]). Our findings surrounding working memory suggest students with RD showed greater need on all working memory measures compared to students with ADHD. This is not only consistent with previous literature documenting lower working memory scores for students with LD, but it also expands previous work by highlighting differing scores for students with RD, specifically. Effects ranged from −0.147 to −0.86 with a moderate mean effect size of −0.403 across working memory measures in favor of the ADHD group.</p> <p>When comparing scores of students with RD to those with RD + ADHD, findings suggested those with RD + ADHD consistently scored below those with ADHD on all reading and cognition measures. This pattern was consistent with previous studies (e.g., [<reflink idref="bib21" id="ref88">21</reflink>]; [<reflink idref="bib41" id="ref89">41</reflink>]; [<reflink idref="bib48" id="ref90">48</reflink>]). Examining mean effect sizes also allows for interpretation of the magnitude of effects. Overall, average effects on reading measures were large (<emph>g</emph> = −1.759 to −0.959) and documented lower achievement for students with RD + ADHD compared to students with ADHD. Cognitive scores also favored students with ADHD, although average effect sizes were lower (<emph>g</emph> = −0.818 to −0.411). Our findings confirmed previous research documenting greater need for students with LD + ADHD compared to students with ADHD alone (e.g., [<reflink idref="bib21" id="ref91">21</reflink>]; [<reflink idref="bib41" id="ref92">41</reflink>]). They also highlighted the magnitude of differences for students with RD, specifically. Results were consistent with the multiple deficit hypothesis and further supported Willcutt et al.'s notion that students with LD and ADHD (particularly those with inattentive and combined presentations) share underlying cognitive factors, yet the intensity of deficit may differ. Our findings suggested a larger intensity of deficit for students with RD + ADHD, particularly as it manifests on reading tasks.</p> <p>Because inattention is consistently related to lower student outcomes, we examined studies comparing students with RD to those with ADHD (<emph>k</emph> = 6) to determine how many students had high levels of inattention (i.e., inattentive or combined ADHD presentations). Three of six studies (Aaron et al., 2002; Breier et al., 2001; Rucklidge & Tannock, 2002) reported ADHD presentation. Of those that did, most or all students had inattentive or combined ADHD presentations. Willcutt et al. (2007) examined levels of ADHD impairment across time for different ADHD presentations but did not disaggregate the overall sample by presentation. As a result, it is impossible to determine the number of students with inattention or combined presentations. Dalby (1985) and Dewey et al. (2003) did not disaggregate by ADHD presentation within their sample of students with ADHD. Given consistent documentation of lower outcomes for students with inattention, we expected all six studies would either (a) include primarily students with inattention in their ADHD samples or (b) report specifics on presentation within the sample. Although we expected all studies to report ADHD presentation, some may have been published before the field identified inattention as a common factor of deficits.</p> <p>Originally, we hoped to examine students with MD, ADHD, and MD + ADHD. However, we did not find any studies that reported effect sizes across groups of students with MD or ADHD alone compared to students with MD + ADHD or MD, RD, or ADHD alone compared to students with MD + RD + ADHD. As a result, we were unable to examine or discuss outcomes for these students. We view this as a result and a limitation. Our search highlights a need for additional work in these areas. Multiple studies suggested shared deficits across students with MD and ADHD (e.g., [<reflink idref="bib29" id="ref93">29</reflink>]; [<reflink idref="bib43" id="ref94">43</reflink>]; [<reflink idref="bib48" id="ref95">48</reflink>]); therefore, additional work examining the magnitude of differences in scores of students with MD, ADHD, and MD + ADHD may provide additional insight into differences in intensity of needs for these students. Emphasizing a need for work in these areas is an important finding of our review, and future work examining outcomes across groups is essential to investigate possible shared factors.</p> <hd id="AN0190688476-21">Limitations</hd> <p>Findings in this review are limited by the information reported in the primary studies that met search criteria. In addition, we did not perform an ancestral search, which may have yielded additional studies. However, a hand search of all journals represented by included studies did not locate additional studies. Our findings are also limited by the lack of consensus about the correct criteria to use to classify students as having RD or MD or both. Studies varied somewhat in their identification criteria, with some using state guidelines and others using researcher-defined criteria. We could not examine the extent to which the selection criteria affected the observed differences between groups given the limited corpus of studies. Similarly, we did report specific criteria used in each study to determine reading, math, and attention difficulties (e.g., normative reading and math scores, specific attention score criteria). Although studies typically followed similar criteria (e.g., standard reading or math scores at least 1 <emph>SD</emph> below the mean, formal ADHD diagnosis, or specific criteria used to determine elevated levels of inattention and/or hyperactivity/impulsivity), considering potential differences in demographic details is important, particularly when thinking of how findings may represent the larger population. In addition, ADHD is not always identified or reported at the school level. For example, some students receive formal services under the category of OHI, but many do not. In addition, due to the common comorbidity of LD and ADHD, there is a chance some LD samples in our review may have also had unreported ADHD. Possible instances of comorbidity should be considered when contextualizing findings.</p> <p>The majority of work examining shared cognitive factors and outcomes for students with LD and ADHD measured lower-order skills (e.g., word ID, basic arithmetic). We not only see this as a limitation but also as an important finding of our review. It calls for additional work including measures of more complex skills (e.g., reading comprehension, math word problems), which may yield different outcomes, particularly for students with LD + ADHD. High levels of inattention may impact higher-order skills more readily. In addition, the literature is focused on areas of difficulty for students with LD and ADHD. It is important to note that these students possess areas of strength and resilience that are understudied. The lack of research on assets limited our synthesis to focusing on deficits. Future research aimed at identifying assets is needed. Similarly, we were limited in our ability to compare and discuss math difficulties with ADHD due to a lack of studies that met our inclusion criteria. We hope our review highlights a need for more work in this area.</p> <hd id="AN0190688476-22">Implications for Practice</hd> <p>Although our systematic review examined differences in outcomes across various groups of students, we cannot speak specifically to ways to support these students in classrooms. We shed light on areas of need for students with various diagnoses, which is useful when considering supports. Because intervention outcomes were outside the scope of this review, we cannot recommend specific instructional practices. We can, however, connect them to instructional practices already documented in the field and highlight a need for future research investigating these practices specifically for students with comorbid LD and for students with LD + ADHD compared to students with RD, MD, or ADHD alone. Because our findings consistently identified students with RD + ADHD as having shared difficulties with working memory and processing speed, and research documents students with inattention having difficulties with reading comprehension, instructional practices that target working memory to support reading comprehension may be particularly beneficial for students with RD + ADHD (inattentive or combined presentations). For example, explicitly teaching students to generate main idea statements while reading provides them with opportunities to stop and consider chunks of text (e.g., [<reflink idref="bib15" id="ref96">15</reflink>]; [<reflink idref="bib39" id="ref97">39</reflink>]). In addition, using graphic organizers while reading to document main ideas about chunks of text can scaffold working memory demands associated with reading comprehension ([<reflink idref="bib6" id="ref98">6</reflink>]; [<reflink idref="bib7" id="ref99">7</reflink>]). Main ideas composed during reading can then be used after reading to help summarize an entire text. Summary composition is well documented in the literature to support students with reading difficulties ([<reflink idref="bib37" id="ref100">37</reflink>]), students with ADHD (e.g., [<reflink idref="bib9" id="ref101">9</reflink>]; [<reflink idref="bib34" id="ref102">34</reflink>]; [<reflink idref="bib35" id="ref103">35</reflink>]; [<reflink idref="bib38" id="ref104">38</reflink>]), and specifically students with inattention ([<reflink idref="bib39" id="ref105">39</reflink>]).</p> <p>Our findings also shed light on considerations surrounding classroom accommodations. We were interested in listening comprehension scores across comparisons due to the common accommodation of reading items aloud to students with LD during assessments. Upon further inspection of effect sizes across studies, results were mixed in that students with RD outperformed those with MD and ADHD on some listening and language comprehension measures; however, students with comorbid RD + MD earned lower scores on these measures compared to students with RD or MD alone. Although we cannot confidently state whether this accommodation is beneficial for students with LD or not, we do wonder about this accommodation, particularly for students with comorbid RD + MD. This is amplified when considering the average working memory effects observed for both students with RD and students with comorbid RD + MD, which were consistently lower than students with MD or ADHD alone. Working memory is often required to (a) successfully follow multistep verbal directions and (b) comprehend multicomponent items on exams. As a result, students with LD may benefit from additional accommodations to support these areas of need (e.g., posing test items after smaller chunks of text rather than posing all items after a multiparagraph passage).</p> <p>Studies in our review consistently documented working memory as a shared cognitive factor of students with LD and ADHD. It may be worthwhile to consider this during the identification process, particularly because students with inattention, especially girls, may not be easily identified ([<reflink idref="bib14" id="ref106">14</reflink>]). If a clear working memory need is documented during initial cognitive testing, additional follow-up assessments to examine potential inattentive needs may be warranted. Furthermore, sharing detailed ADHD presentation information with practitioners is helpful. If assessment results suggest a student has high levels of inattention, for example, this provides insight on <emph>possible</emph> strengths (e.g., word reading) and areas of need (e.g., reading comprehension); it can also provide information about which instructional practices may be helpful when supporting working memory during specific tasks, such as reading comprehension. Although information about ADHD presentation may provide some context, additional data, such as progress monitoring data, behavioral observations, and formative assessment data, are also essential when determining unique student needs and appropriate intervention. In addition, working memory scores were consistently lower in students with RD compared to both MD and ADHD in our review. This differs from previous research and highlights the importance of considering working memory as a potential indicator of LD during the identification process (along with several other indicators, such as reading performance, verbal comprehension, and processing speed).</p> <hd id="AN0190688476-23">Conclusion</hd> <p>Our findings are consistent with the multiple deficit framework and documented similar cognitive abilities across students with RD + MD and RD or MD as well as students with RD + ADHD or ADHD. Examining mean effect sizes across domains allowed for additional interpretation of the magnitude of differences. Most average effect sizes across measures of reading, mathematics, and cognitive ability indicated higher levels of need for students with RD + MD compared to students with RD or MD. Similarly, students with RD + ADHD have greater needs on reading measures compared to those with ADHD alone with few exceptions. Differences in mean effect sizes were consistent with previous literature showing the intensity of deficits for students with comorbid conditions compared to those with single disabilities. In addition to documenting findings, we identified areas of need in the literature base, such as a need to examine outcomes using assessments that include higher-order tasks and a need for additional research examining outcomes for students with MD or ADHD compared to students with MD + ADHD as well as students with MD, RD, or ADHD compared to students with RD + MD + ADHD.</p> <hd id="AN0190688476-24">Supplemental Material</hd> <p>Graph: Supplemental material, sj-docx-1-ldq-10.1177_07319487251318352 for Exploring Differences Between Cognition, Reading, Math, and Attention Scores in Students With Disabilities: A Systematic Review by Alicia A. 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(2005). Neuropsychological analyses of comorbidity between reading disability and attention deficit hyperactivity disorder: In search of the common deficit. Developmental Neuropsychology, 27(1), 35–78. https://doi.org/10.1207/s15326942dn2701_3</bibtext> </blist> <blist> <bibtext> Yang X., Yan M., Ruan Y., Ku S. Y. Y., Ming Lo J. C., Peng P., McBride C. (2021). Relations among phonological processing skills and mathematics in children: A meta-analysis. Journal of Educational Psychology, 114(2), 289–307. https://doi.org/10.1037/edu0000710</bibtext> </blist> </ref> <ref id="AN0190688476-26"> <title> Footnotes </title> <blist> <bibtext> The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> This research was supported in part by the 5P50 HD052117-12 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Eunice Kennedy Shriver National Institute of Child Health and Human Development or the National Institutes of Health.</bibtext> </blist> <blist> <bibtext> Alicia A. Stewart</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0001-6770-5046 Nancy Scammacca</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0002-7484-5976 Pierce Cappelli</bibtext> </blist> <blist> <bibtext>Graph https://orcid.org/0000-0001-8426-8132</bibtext> </blist> <blist> <bibtext> Supplemental material is available at sj-docx-1-ldq-10.1177_07319487251318352.docx.</bibtext> </blist> </ref> <aug> <p>By Alicia A. Stewart; Nancy Scammacca; Young Ri Lee and Pierce Cappelli</p> <p>Reported by Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib25" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib47" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib46" firstref="ref12"></nolink> <nolink nlid="nl4" bibid="bib26" firstref="ref14"></nolink> <nolink nlid="nl5" bibid="bib29" firstref="ref15"></nolink> <nolink nlid="nl6" bibid="bib49" firstref="ref16"></nolink> <nolink nlid="nl7" bibid="bib24" firstref="ref17"></nolink> <nolink nlid="nl8" bibid="bib28" firstref="ref18"></nolink> <nolink nlid="nl9" bibid="bib48" firstref="ref20"></nolink> <nolink nlid="nl10" bibid="bib23" firstref="ref23"></nolink> <nolink nlid="nl11" bibid="bib40" firstref="ref29"></nolink> <nolink nlid="nl12" bibid="bib18" firstref="ref30"></nolink> <nolink nlid="nl13" bibid="bib16" firstref="ref32"></nolink> <nolink nlid="nl14" bibid="bib17" firstref="ref33"></nolink> <nolink nlid="nl15" bibid="bib22" firstref="ref34"></nolink> <nolink nlid="nl16" bibid="bib45" firstref="ref41"></nolink> <nolink nlid="nl17" bibid="bib27" firstref="ref42"></nolink> <nolink nlid="nl18" bibid="bib50" firstref="ref43"></nolink> <nolink nlid="nl19" bibid="bib10" firstref="ref44"></nolink> <nolink nlid="nl20" bibid="bib31" firstref="ref47"></nolink> <nolink nlid="nl21" bibid="bib20" firstref="ref50"></nolink> <nolink nlid="nl22" bibid="bib42" firstref="ref51"></nolink> <nolink nlid="nl23" bibid="bib30" firstref="ref52"></nolink> <nolink nlid="nl24" bibid="bib33" firstref="ref53"></nolink> <nolink nlid="nl25" bibid="bib32" firstref="ref56"></nolink> <nolink nlid="nl26" bibid="bib21" firstref="ref58"></nolink> <nolink nlid="nl27" bibid="bib41" firstref="ref61"></nolink> <nolink nlid="nl28" bibid="bib43" firstref="ref62"></nolink> <nolink nlid="nl29" bibid="bib44" firstref="ref76"></nolink> <nolink nlid="nl30" bibid="bib36" firstref="ref77"></nolink> <nolink nlid="nl31" bibid="bib12" firstref="ref78"></nolink> <nolink nlid="nl32" bibid="bib13" firstref="ref79"></nolink> <nolink nlid="nl33" bibid="bib11" firstref="ref84"></nolink> <nolink nlid="nl34" bibid="bib19" firstref="ref85"></nolink> <nolink nlid="nl35" bibid="bib15" firstref="ref96"></nolink> <nolink nlid="nl36" bibid="bib39" firstref="ref97"></nolink> <nolink nlid="nl37" bibid="bib37" firstref="ref100"></nolink> <nolink nlid="nl38" bibid="bib34" firstref="ref102"></nolink> <nolink nlid="nl39" bibid="bib35" firstref="ref103"></nolink> <nolink nlid="nl40" bibid="bib38" firstref="ref104"></nolink> <nolink nlid="nl41" bibid="bib14" firstref="ref106"></nolink>
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  Data: Exploring Differences between Cognition, Reading, Math, and Attention Scores in Students with Disabilities: A Systematic Review
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  Data: English
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  Data: <searchLink fieldCode="AR" term="%22Alicia+A%2E+Stewart%22">Alicia A. Stewart</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6770-5046">0000-0001-6770-5046</externalLink>)<br /><searchLink fieldCode="AR" term="%22Nancy+Scammacca%22">Nancy Scammacca</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7484-5976">0000-0002-7484-5976</externalLink>)<br /><searchLink fieldCode="AR" term="%22Young+Ri+Lee%22">Young Ri Lee</searchLink><br /><searchLink fieldCode="AR" term="%22Pierce+Cappelli%22">Pierce Cappelli</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8426-8132">0000-0001-8426-8132</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Learning+Disability+Quarterly%22"><i>Learning Disability Quarterly</i></searchLink>. 2026 49(1):39-53.
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  Data: SAGE Publications and Hammill Institute on Disabilities. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
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  Label: Page Count
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  Data: 15
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  Label: Publication Date
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  Data: 2026
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  Label: Sponsoring Agency
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  Data: Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (DHHS/NIH)
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  Data: 5P50HD05211712
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  Data: Journal Articles<br />Information Analyses
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Learning+Disabilities%22">Learning Disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Reading+Difficulties%22">Reading Difficulties</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematics+Skills%22">Mathematics Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Scores%22">Scores</searchLink><br /><searchLink fieldCode="DE" term="%22Students+with+Disabilities%22">Students with Disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Attention+Deficit+Hyperactivity+Disorder%22">Attention Deficit Hyperactivity Disorder</searchLink><br /><searchLink fieldCode="DE" term="%22Comorbidity%22">Comorbidity</searchLink><br /><searchLink fieldCode="DE" term="%22Outcomes+of+Education%22">Outcomes of Education</searchLink><br /><searchLink fieldCode="DE" term="%22Effect+Size%22">Effect Size</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Ability%22">Cognitive Ability</searchLink><br /><searchLink fieldCode="DE" term="%22Short+Term+Memory%22">Short Term Memory</searchLink><br /><searchLink fieldCode="DE" term="%22Language+Skills%22">Language Skills</searchLink>
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  Data: 10.1177/07319487251318352
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  Data: 0731-9487<br />2168-376X
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: A large body of research documents underlying cognitive factors, many of which are shared, in students with reading disabilities (RDs), math disabilities (MDs), comorbid reading and math disabilities (RD + MD), as well as students with attention-deficit/hyperactivity disorder (ADHD) and students with RD and ADHD. In an effort to examine differences in reading, mathematics, and cognitive outcomes among these students, we investigated the outcomes between these groups across the published research literature by conducting a systematic review. A total of 16 studies met inclusion criteria. Data from a total of 1,910 participants across studies was used to answer our research questions. Mean effect size differences across studies highlight lower overall outcomes for students with RD + MD and students with RD + ADHD compared to students with RD, MD, or ADHD alone.
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  Data: 2026
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  Data: EJ1495982
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        Value: 10.1177/07319487251318352
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 39
    Subjects:
      – SubjectFull: Learning Disabilities
        Type: general
      – SubjectFull: Reading Difficulties
        Type: general
      – SubjectFull: Mathematics Skills
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      – SubjectFull: Scores
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      – SubjectFull: Students with Disabilities
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      – SubjectFull: Attention Deficit Hyperactivity Disorder
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      – SubjectFull: Comorbidity
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      – SubjectFull: Outcomes of Education
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      – SubjectFull: Effect Size
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      – SubjectFull: Cognitive Ability
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      – SubjectFull: Short Term Memory
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      – SubjectFull: Language Skills
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      – TitleFull: Exploring Differences between Cognition, Reading, Math, and Attention Scores in Students with Disabilities: A Systematic Review
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