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A Meta-Analysis of Interventions to Improve Reading Comprehension Outcomes for Adolescents with Reading Difficulties

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Title: A Meta-Analysis of Interventions to Improve Reading Comprehension Outcomes for Adolescents with Reading Difficulties
Language: English
Authors: Sohn, Hyojong (ORCID 0000-0002-0300-2437), Acosta, Kelly, Brownell, Mary T., Gage, Nicholas A., Tompson, Eilish, Pudvah, Carolyn
Source: Learning Disabilities Research & Practice. May 2023 38(2):85-103.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 19
Publication Date: 2023
Document Type: Journal Articles
Information Analyses
Education Level: Junior High Schools
Middle Schools
Secondary Education
Elementary Education
Grade 6
Intermediate Grades
Grade 7
Grade 8
Grade 9
High Schools
Grade 10
Grade 11
Grade 12
Descriptors: Middle School Students, Grade 6, Grade 7, Grade 8, Grade 9, Grade 10, Grade 11, Grade 12, High School Students, Reading Difficulties, Intervention, Reading Comprehension, Reading Improvement, Instructional Effectiveness
DOI: 10.1111/ldrp.12307
ISSN: 0938-8982
1540-5826
Abstract: This meta-analysis synthesized 97 effect sizes extracted from 37 intervention studies for students with reading difficulties (RDs) in Grades 6 to 12 published between 1982 and 2021 to identify the overall impact of reading interventions and the moderating effects of intervention characteristics and study design characteristics. Random-effects robust variance estimation (RVE) was used to account for dependencies within studies. Overall, interventions designed to improve reading comprehension outcomes for adolescents with RDs were effective (g = 0.63). Meta-regression analyses identified several significant moderators that were associated with intervention efficacy, such as text content, duration of intervention, agent of intervention, status of student, type of dependent measure, and study quality. We provide study limitations as well as implications for research and practice.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1379410
Database: ERIC
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  Value: <anid>AN0164007724;7mj01may.23;2023Jun02.06:18;v2.2.500</anid> <title id="AN0164007724-1">A Meta‐Analysis of Interventions to Improve Reading Comprehension Outcomes for Adolescents with Reading Difficulties </title> <p>This meta‐analysis synthesized 97 effect sizes extracted from 37 intervention studies for students with reading difficulties (RDs) in Grades 6 to 12 published between 1982 and 2021 to identify the overall impact of reading interventions and the moderating effects of intervention characteristics and study design characteristics. Random‐effects robust variance estimation (RVE) was used to account for dependencies within studies. Overall, interventions designed to improve reading comprehension outcomes for adolescents with RDs were effective (g = 0.63). Meta‐regression analyses identified several significant moderators that were associated with intervention efficacy, such as text content, duration of intervention, agent of intervention, status of student, type of dependent measure, and study quality. We provide study limitations as well as implications for research and practice.</p> <p>Reading is a primary vehicle for learning academic content once students enter middle and high school (Vaughn & Fletcher, [<reflink idref="bib80" id="ref1">80</reflink>]). The texts secondary‐level students encounter become increasingly complex in terms of vocabulary and syntax, and the text demands placed on readers can create barriers to academic success. Thus, it is not surprising that many students struggle with complex secondary texts, as evident in their reading achievement scores on the National Assessment of Educational Progress (NAEP). For example, when assessed in 2022, only 31% of eighth graders scored at the advanced or proficient range on the reading subtest, with 69% of students falling below proficient (National Center for Education Statistics [NCES], [<reflink idref="bib57" id="ref2">57</reflink>]). This score was a drop from the 2019 NAEP reading scores. Although the drop in scores may be attributed to schooling disruptions due to the COVID‐19 pandemic, scores from the 2019 test administration were lower than previous test administration scores, demonstrating a downward trend in reading scores for eighth graders. Twelfth‐grade students, when last assessed in 2019, also fared poorly. Thirty‐seven percent of students scored in the advanced or proficient range while 63% scored below proficient (NCES, [<reflink idref="bib57" id="ref3">57</reflink>]).</p> <p>Students with reading difficulties and disabilities are at a particular disadvantage when reading complex texts, as they often struggle with many foundational skills at the word and text levels. For example, on the NAEP, only 9% of students with disabilities (SWDs) scored in the advanced or proficient range in 2019 and 2022. The low proficiency levels of SWDs and others who struggle with reading are of considerable concern, as reading proficiency has been linked to positive postsecondary outcomes (Hein et al., [<reflink idref="bib40" id="ref4">40</reflink>]). In the following, we refer to students with reading disabilities and those who struggle with reading as students with reading difficulties (RDs).</p> <hd id="AN0164007724-2">Reading Comprehension of Secondary Students with Reading Difficulties</hd> <p>Secondary‐level readers must be able to read narrative and expository texts, recognize text structures and features, and use these to support their comprehension across multiple texts and text contents to gain an overall understanding (Catts, [<reflink idref="bib21" id="ref5">21</reflink>]). To successfully comprehend, readers need fluency with multiple component skills. Researchers have demonstrated that reading comprehension is dependent on (a) word‐level knowledge, which involves the complex interplay of phonetic, morphological, semantic, and syntactic knowledge (Goodwin & Ahn, [<reflink idref="bib38" id="ref6">38</reflink>]; Wanzek et al., [<reflink idref="bib89" id="ref7">89</reflink>]); (b) automatic word recognition (Austin et al., [<reflink idref="bib4" id="ref8">4</reflink>]; Compton et al., [<reflink idref="bib23" id="ref9">23</reflink>]); (c) text reading fluency (Chard et al., [<reflink idref="bib22" id="ref10">22</reflink>]; Stevens et al., [<reflink idref="bib74" id="ref11">74</reflink>]); (d) oral language comprehension (Tarvainen et al., [<reflink idref="bib78" id="ref12">78</reflink>]); (e) background knowledge (O'Reilly et al., [<reflink idref="bib59" id="ref13">59</reflink>]; Smith et al., [<reflink idref="bib70" id="ref14">70</reflink>]); (f) knowledge of text structures (Cain et al., [<reflink idref="bib19" id="ref15">19</reflink>]; Meyer et al., [<reflink idref="bib52" id="ref16">52</reflink>]); and (g) strategic knowledge (e.g., ability to make inference, summarize text, monitor comprehension; Reed & Lynn, [<reflink idref="bib62" id="ref17">62</reflink>]; Shelton et al., [<reflink idref="bib69" id="ref18">69</reflink>]). Students with RDs often demonstrate deficits in these component skills that are either the result of a disability or lack of appropriate and sustained exposure to strong literacy learning opportunities and environments.</p> <p>Reading achievement scores of secondary students with RDs suggest they require additional supports with specially designed instruction to navigate these challenges. Researchers recommend that to impact the reading achievement of secondary students, teachers need to implement effective reading interventions: (a) school‐wide across content areas (Vaughn, Wexler et al., [<reflink idref="bib86" id="ref19">86</reflink>]); (b) with different types of expository and narrative texts, each with their own text structure (Gersten et al., [<reflink idref="bib37" id="ref20">37</reflink>]); and (c) for sufficient periods of time (Vaughn & Fletcher, [<reflink idref="bib80" id="ref21">80</reflink>]). Thus, it is essential to identify strategies educators can employ to support older students with RDs in comprehending varied texts and determining conditions under which interventions should be implemented.</p> <hd id="AN0164007724-3">Instructional Strategies for Increasing Reading Comprehension Achievement</hd> <p>In the research literature, (meta)cognitive and multicomponent strategies are two of the most common instructional strategies that support adolescents' reading comprehension. Other strategies include providing older students with instruction that focuses exclusively on phonics, peer‐mediated strategies, or computer‐adapted instruction (CAI).</p> <hd id="AN0164007724-4">(Meta)cognitive Strategies</hd> <p>(Meta)cognitive strategies are strategies that teach students a specific skill for how to comprehend a text or how to be aware of and monitor their own thinking and strategy use while reading (Budin et al., [<reflink idref="bib18" id="ref22">18</reflink>]; Gersten et al., [<reflink idref="bib37" id="ref23">37</reflink>]). For example, Berkeley and Riccomini ([<reflink idref="bib10" id="ref24">10</reflink>]) taught students a mnemonic device to aid them in recalling the steps for comprehending expository texts. The strategy also included a self‐monitoring checklist that helped students reflect on their understanding or misunderstandings of the text.</p> <hd id="AN0164007724-5">Multicomponent Strategies</hd> <p>Multicomponent strategies are those that aim to support the reading comprehension of SWDs and other struggling readers using a combination of explicit phonics or fluency instruction, vocabulary instruction, explicit instruction in text structures, and development of students' background knowledge (Gersten et al., [<reflink idref="bib37" id="ref25">37</reflink>]).</p> <hd id="AN0164007724-6">Other Strategies</hd> <p>Other strategies researchers have examined for their effectiveness with improving adolescent reading comprehension include explicit instruction solely in phonics, peer‐mediated strategies, and computer‐adapted instruction (CAI). Although providing direct phonics instruction is not a reading comprehension strategy, the poor decoding skills of older SWDs can impact their ability to comprehend text (Moats & Tolman, [<reflink idref="bib53" id="ref26">53</reflink>]). Thus, some researchers have examined if providing phonics instruction to older students would help improve their reading comprehension (e.g., Diliberto et al., [<reflink idref="bib27" id="ref27">27</reflink>]).</p> <hd id="AN0164007724-7">PREVIOUS ANALYSES</hd> <p>Prior meta‐analyses aimed at identifying interventions that improve the reading comprehension of upper‐elementary and secondary students with RDs have yielded positive mean effect sizes (ESs) with ranges of 0.09 to 0.95 (Berkeley et al., [<reflink idref="bib11" id="ref28">11</reflink>]; Edmonds et al., [<reflink idref="bib29" id="ref29">29</reflink>]; Filderman et al., [<reflink idref="bib31" id="ref30">31</reflink>]; Flynn et al., [<reflink idref="bib33" id="ref31">33</reflink>]; Scammacca et al., [[<reflink idref="bib65" id="ref32">65</reflink>]]; Swanson, Stevens, et al., [<reflink idref="bib75" id="ref33">75</reflink>]; Wanzek et al., [<reflink idref="bib91" id="ref34">91</reflink>]). Drawing conclusions from these reviews, however, is challenging, as they yielded mixed evidence about the efficacy of reading interventions. For example, Swanson, Stevens, et al. ([<reflink idref="bib75" id="ref35">75</reflink>]) found multicomponent strategies had the largest effects on reading comprehension outcomes for students with RDs, whereas Berkeley et al. ([<reflink idref="bib11" id="ref36">11</reflink>]), Edmonds et al. ([<reflink idref="bib29" id="ref37">29</reflink>]), and Filderman et al. ([<reflink idref="bib31" id="ref38">31</reflink>]) noted that (meta)cognitive strategies had the largest effect on reading comprehension outcomes for students with RDs.</p> <p>These mixed findings may be the result of differences in study inclusion requirements and analyses. For example, most meta‐analytic studies included upper‐elementary and secondary students (e.g., Filderman et al., [<reflink idref="bib31" id="ref39">31</reflink>]; Scammacca et al., [<reflink idref="bib66" id="ref40">66</reflink>]; Swanson, Stevens, et al., [<reflink idref="bib75" id="ref41">75</reflink>]; Wanzek et al., [<reflink idref="bib91" id="ref42">91</reflink>]), and only one study, conducted more than 10 years ago, focused solely on struggling secondary readers (Edmonds et al., [<reflink idref="bib29" id="ref43">29</reflink>]). Differences in study samples may also contribute to the mixed findings. Researchers in previous studies found that ESs for reading interventions (Swanson, Stevens, et al., [<reflink idref="bib75" id="ref44">75</reflink>]) or annual growth effects (Bloom et al., [<reflink idref="bib12" id="ref45">12</reflink>]) were larger for students in upper‐elementary school than secondary schools. Combining reading comprehension outcomes for upper‐elementary and secondary students in meta‐analytic studies may complicate researchers' efforts to identify effective interventions for secondary readers.</p> <p>Researchers have also focused on different instructional settings in their meta‐analytic reviews. Swanson, Stevens, et al. ([<reflink idref="bib74" id="ref46">74</reflink>]) only examined Tier 1 interventions conducted in general education classrooms within multitiered systems of support models, whereas Berkeley et al. ([<reflink idref="bib11" id="ref47">11</reflink>]), Edmonds et al. ([<reflink idref="bib29" id="ref48">29</reflink>]), and Filderman et al. ([<reflink idref="bib31" id="ref49">31</reflink>]) included both general and special education settings. Additionally, only two of the meta‐analyses (Filderman et al., [<reflink idref="bib31" id="ref50">31</reflink>]; Swanson, Stevens, et al., [<reflink idref="bib75" id="ref51">75</reflink>]), employed rigorous meta‐analytic techniques. These researchers used robust variance estimation (RVE; Hedges et al., [<reflink idref="bib39" id="ref52">39</reflink>]), which allowed them to account for dependencies that may have occurred among multiple outcome measures used in studies. Other researchers did not use these analyses, which may have resulted in Type I and II errors (Tanner‐Smith & Tipton, [<reflink idref="bib77" id="ref53">77</reflink>]), and potentially led researchers to draw different conclusions about their findings. Finally, previous researchers did not determine if specific interventions were more effective with different text content, such as content from English language arts compared to content from math or social studies. Given that disciplinary texts have their own unique structures and language (Catts, [<reflink idref="bib21" id="ref54">21</reflink>]), it seems important to determine if interventions are differentially effective depending on the content‐specific nature of the text (e.g., social studies versus science).</p> <p>More meta‐analytic research is needed to identify which interventions are most impactful on the reading comprehension outcomes of secondary students with RDs. The results of such research could help practitioners understand how interventions are influenced by text structure (e.g., expository vs. narrative), text content (e.g., science vs. social studies), and other contextual variables, such as instructional setting, group size, and duration of intervention. Further, these meta‐analytic studies should employ rigorous statistical methodologies that reduce the possibility of Type 1 and II errors.</p> <hd id="AN0164007724-8">STUDY PURPOSE AND RESEARCH QUESTIONS</hd> <p>The current meta‐analytic review aimed to provide information about the efficacy of specific reading comprehension interventions for students with RDs in Grades 6−12. The study extends and refines earlier meta‐analytic studies (Berkeley et al., [<reflink idref="bib11" id="ref55">11</reflink>]; Edmonds et al., [<reflink idref="bib29" id="ref56">29</reflink>]; Filderman et al., [<reflink idref="bib31" id="ref57">31</reflink>]; Flynn et al., [<reflink idref="bib33" id="ref58">33</reflink>]; Scammacca et al., [[<reflink idref="bib65" id="ref59">65</reflink>]]; Swanson, Stevens, et al., [<reflink idref="bib75" id="ref60">75</reflink>]; Wanzek et al., [<reflink idref="bib91" id="ref61">91</reflink>]) by (a) focusing exclusively on secondary students (Grades 6−12) who are students with RDs, (b) identifying interventions that are most effective for these students, and (c) employing meta‐analytic techniques that account for moderators not previously examined, such as text content and instructional setting, and dependencies among outcome variables. As done in previous studies, we analyzed moderating variables that can identify who profits most from interventions and the conditions under which interventions are likely to be most effective. The following research questions guided our study:</p> <p></p> <ulist> <item> What is the mean ES of reading interventions that target reading comprehension for students with RDs in Grades 6−12?</item> <p></p> <item> Which intervention characteristics (e.g., interventional setting, duration of intervention) and design characteristics (i.e., type of dependent measure, study quality) moderate interventions' effect?</item> <p></p> <item> Are specific reading comprehension strategies more effective than others, and for which text structure and text content are reading interventions most effective?</item> </ulist> <hd id="AN0164007724-9">METHODS</hd> <p></p> <hd id="AN0164007724-10">Search Procedures</hd> <p>An electronic database search was conducted using Academic Search Premier to search the following databases: APA PsychInfo, Education Source, ERIC, Professional Development Collection, Psychology and Behavioral Sciences Collection, and Teacher Reference Center. Studies were searched from January 1917, the earliest date available for searching the electronic database, through December 2022. The following Boolean logic was used to conduct the search: <emph>learning disabilit*</emph> OR <emph>reading disabilit*</emph> OR <emph>dyslexia</emph> AND <emph>adoles*</emph> OR <emph>middle school</emph> OR <emph>high school</emph> OR <emph>junior high school</emph> AND <emph>reading comprehension</emph> OR <emph>vocabulary</emph> OR <emph>literacy</emph> OR <emph>fluency</emph> OR <emph>phonic*</emph> OR <emph>phonemic</emph> OR <emph>main idea</emph> OR <emph>summariz*</emph> OR <emph>collaborative strategic reading</emph> OR <emph>reciprocal teaching</emph> OR <emph>multicomponent</emph> OR <emph>peer assisted</emph> OR <emph>PALS; peer tutor*</emph> OR <emph>computer base*</emph> OR <emph>computer assist*</emph>. A backward and forward search and a review of references of previously published systematic reviews and meta‐analyses were also used to identify additional studies. Additionally, to identify any additional studies, a hand search was conducted of targeted journals (<emph>Exceptional Children, Learning Disabilities Research and Practice, Journal of Special Education</emph>, and <emph>Journal of Learning Disabilities</emph>).</p> <hd id="AN0164007724-11">Inclusion Criteria</hd> <p>Studies were included if they (a) were peer‐reviewed; (b) employed experimental or quasi‐experimental group design studies; (b) were conducted in the United States and published in English; (c) included students in Grades 6−12 and/or children ages 12 to 18; (d) addressed students identified as students with RDs (see operational definition); (e) included a reading comprehension outcome measure; and (f) provided information about ESs, or information to calculate ESs.</p> <hd id="AN0164007724-12">Exclusion Criteria</hd> <p>We excluded studies in which the sample included students from Grades K−5, as we were interested in reading comprehension outcomes for secondary students only. However, we did include one study that had one student in Grade 5 in their sample (Reed & Lynn, [<reflink idref="bib62" id="ref62">62</reflink>]) as the remaining 23 students were in Grades 6−8. We excluded gray literature (e.g., dissertations, master's theses, government documents, working papers), as we considered peer‐review to be an indicator of the quality of studies (Adams et al., [<reflink idref="bib2" id="ref63">2</reflink>]; Herrera et al., [<reflink idref="bib41" id="ref64">41</reflink>]). Additionally, studies were excluded if they were not conducted in the United States or if reading strategies were implemented in a language other than English. We chose to exclude studies that implemented interventions in another language as the phonology and orthography of English differs from other languages; thus, strategies for supporting students with RDs may differ across languages. Studies were also excluded if they did not provide information to calculate ESs.</p> <hd id="AN0164007724-13">Search Results</hd> <p>The initial database search yielded a total of 3,460 studies. Additional searches conducted through backward and forward searching and reference lists of previous syntheses and meta‐analyses yielded one additional study (Jitendra et al., [<reflink idref="bib42" id="ref65">42</reflink>]). A total of 3,461 studies were screened. After the removal of duplicates and an initial title and abstract screening, we were left with 119 articles for full‐text review. The first and second authors then applied our inclusion and exclusion criteria to all 119 articles. The resulting sample was 37 articles. Figure 1 presents the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA; Moher et al., [<reflink idref="bib54" id="ref66">54</reflink>]) flow chart that documents our screening procedures.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/7MJ/01may23/ldrp12307-fig-0001.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="ldrp12307-fig-0001.jpg" title="1 PRISMA diagram of search process. [Colour figure can be viewed at wileyonlinelibrary.com]" /> </p> <p></p> <hd id="AN0164007724-15">Coding Procedures</hd> <p>We developed a codebook that included variables based on (a) findings from prior syntheses and meta‐analyses on reading comprehension (Berkeley et al., [<reflink idref="bib11" id="ref67">11</reflink>]; Gersten et al., [<reflink idref="bib37" id="ref68">37</reflink>]; Herrera et al., [<reflink idref="bib41" id="ref69">41</reflink>]; Mastropieri & Scruggs, [<reflink idref="bib50" id="ref70">50</reflink>]; Solis et al., [<reflink idref="bib71" id="ref71">71</reflink>]; Wanzek et al., [<reflink idref="bib91" id="ref72">91</reflink>]); (b) Lipsey's ([<reflink idref="bib47" id="ref73">47</reflink>]) guidance for conducting syntheses and meta‐analyses; and (c) study quality reporting standards developed by Cook et al. ([<reflink idref="bib24" id="ref74">24</reflink>]) for the Council for Exceptional Children (CEC). Variables coded included intervention characteristics (i.e., type of intervention, text structure, text content, instructional setting, group size, duration of intervention, agent of intervention, status of students) and study design characteristics (i.e., type of dependent measure, study quality). The coding of each variable may be found in Table 1.</p> <p>1 TABLE Operational Definitions of Intervention and Study Design Characteristics</p> <p> <ephtml> <table><thead><tr><th>Moderator</th><th align="left">Operational Definitions</th></tr></thead><tbody><tr><td align="center">Intervention characteristics</td></tr><tr><td>Reading comprehension strategy</td><td /></tr><tr><td>(Meta)cognitive</td><td>Interventions that employed (meta)cognitive strategies involve teaching students how to aware of and monitor their thinking while reading text or a specific strategy for comprehending the text (e.g., summarization; Gersten et al., <xref ref-type="bibr" rid="bibr37">2001</xref>).</td></tr><tr><td>Multicomponent</td><td>Multicomponent interventions aim to support the reading comprehension of students with RDs through some combination of the following: explicit instruction in phonics, fluency, text structures, and vocabulary as well as development of background knowledge (Gersten et al., <xref ref-type="bibr" rid="bibr37">2001</xref>).</td></tr><tr><td>"Other"</td><td>Interventions included in the "other" category focused solely on teaching students' explicit phonics instruction, peer‐mediated instruction, or CAI.</td></tr><tr><td>Text content</td><td /></tr><tr><td>ELA</td><td>Texts selected with topics in ELA classes</td></tr><tr><td>Science</td><td>Texts selected with topics in science classes</td></tr><tr><td>Social studies</td><td>Texts selected with topics in social studies classes</td></tr><tr><td>Multiple subjects</td><td>Texts selected with topics in more than two classes (e.g., social studies and science classes; Vaughn, Roberts, Wexler, et al., <xref ref-type="bibr" rid="bibr82">2015</xref>)</td></tr><tr><td>Text structure</td><td /></tr><tr><td>Expository</td><td>Nonfiction texts that provide information about a fact or a topic using various text features (e.g., descriptive, cause‐effect; Gajria et al., <xref ref-type="bibr" rid="bibr34">2007</xref>).</td></tr><tr><td>Narrative</td><td>Narrative texts that include fiction (e.g., novels) and nonfiction (e.g., memoirs) texts that use imaginative language (Sejnost & Thiese, <xref ref-type="bibr" rid="bibr67">2010</xref>).</td></tr><tr><td>Instructional setting</td><td /></tr><tr><td>Inclusive</td><td>Interventions delivered in inclusive general education classrooms</td></tr><tr><td>Exclusive</td><td>Interventions exclusively delivered in pull‐out or resource rooms</td></tr><tr><td>Group size</td><td /></tr><tr><td>Small group</td><td>Groups of five or fewer students (Vaughn et al., <xref ref-type="bibr" rid="bibr87">2010</xref>)</td></tr><tr><td>Large group</td><td>Groups of six or more students</td></tr><tr><td>Duration of intervention</td><td /></tr><tr><td>Short term</td><td>Intervention lasted less than 2 months.</td></tr><tr><td>Intermediate term</td><td>Intervention lasted more than 2 months and less than 1 year.</td></tr><tr><td>Long term</td><td>Intervention lasted more than 1 year (Vaughn & Fletcher, <xref ref-type="bibr" rid="bibr80">2012</xref>; Vaughn et al., <xref ref-type="bibr" rid="bibr87">2010</xref>).</td></tr><tr><td>Agent of intervention</td><td /></tr><tr><td>Teacher</td><td>Intervention implemented by general and/or special education teachers</td></tr><tr><td>Researcher</td><td>Intervention implemented by researchers and/or research assistants</td></tr><tr><td>Other</td><td>Intervention implemented by other agents (e.g., undergraduate tutors)</td></tr><tr><td>Status of students</td><td /></tr><tr><td>SWDs</td><td>Any student that received special education services for reading.</td></tr><tr><td>At‐risk students</td><td>Any student not identified with a disability that researchers identified as not making adequate progress in reading (e.g., students who scored at or below the 30th percentile; Vaugh et al., <xref ref-type="bibr" rid="bibr85">2012</xref>).</td></tr><tr><td>Students with RDs</td><td>Both SWDs and at‐risk students.</td></tr><tr><td align="center">Study design characteristics</td></tr><tr><td>Type of dependent measure</td><td /></tr><tr><td>Standardized</td><td>Standardized measures include norm‐referenced tests (e.g., Woodcock–Johnson III) and CBM (e.g., MAZE).</td></tr><tr><td>Researcher‐developed</td><td>Measures developed by researchers for the given study.</td></tr><tr><td>Study quality</td><td /></tr><tr><td>High quality</td><td>A study met the following four criteria: (a) at least 20 (80%) out of 24 quality indicators suggested by CEC were present, (b) random assignment was used, (c) researchers provided fidelity‐of‐implementation data, and (d) pretest control was employed.</td></tr><tr><td>Not high quality</td><td>A study did not meet the criteria.</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note</emph>. Student with RDs = students with reading difficulties; CAI = computer‐adapted instruction; SWDs = students with disabilities; CBM = curriculum‐based measurement; CEC = Council for Exceptional Children.</p> <p>All articles were double‐coded. To code studies according to selected variables, the second author coded a sample of data and reviewed it with the first author to ensure coding procedures were rigorous and would yield reliable results. The second author then trained two additional authors to code articles until at least 90% accuracy was reached. After training was completed, all four authors independently extracted data on all variables on a subset of articles, with the second author coding all articles. Interrater agreement was calculated by the number of disagreements divided by the number of variables (<emph>N =</emph> 64) multiplied by 100, with an average agreement of 89%. The second author met individually with each rater to review disagreements until consensus was reached. Most disagreements were related to type of text used and which assessment outcomes to include.</p> <hd id="AN0164007724-16">Effect Size Calculation</hd> <p>We calculated Hedge's <emph>g</emph> for all studies to adjust for small sample bias in Cohen's <emph>d</emph> (Borenstein et al., [<reflink idref="bib15" id="ref75">15</reflink>]). When studies provided pre‐ and posttest measures from treatment and control groups, respectively, we calculated pooling estimates based on pooled pre‐ and posttest standard deviations to control for baseline effects (Morris, [<reflink idref="bib55" id="ref76">55</reflink>]). If studies did not provide information on means or standard deviations, we used other test statistics (e.g., <emph>t</emph>‐tests, <emph>f</emph>‐tests) and converted them to <emph>g</emph> (Borenstein et al., [<reflink idref="bib15" id="ref77">15</reflink>]).</p> <p>We only included comprehension‐related student measures (e.g., Woodcock–Johnson III Passage Comprehension subtest) from each study, as we were interested in how interventions improved students' reading comprehension. We also divided the different types of reading comprehension measures into three categories: standardized measures (e.g., norm‐referenced tests, curriculum‐based measurement), researcher‐developed measures (i.e., made by researchers for the study), and both types of measures. If a study reported multiple data points, we computed ESs between the first and last waves only. Maintenance (e.g., delayed posttesting; Berkeley et al., [<reflink idref="bib9" id="ref78">9</reflink>]) and generalization measures (e.g., near‐transfer tests; Jitendra et al., [<reflink idref="bib42" id="ref79">42</reflink>]) were excluded since definitions of those measures were unclear or not consistent across studies.</p> <hd id="AN0164007724-17">Meta‐Analytic Procedures</hd> <p>We calculated 97 ESs from 37 studies. We assumed there would be some dependency between measures, and that covariates (e.g., type of intervention, group size, text content) would differ across studies, impacting the variance of interventions (Borenstein et al., [<reflink idref="bib15" id="ref80">15</reflink>]). Therefore, we used random‐effects robust variance estimates (RVE) to calculate overall treatment effects and conduct the moderator analyses. RVE is a meta‐analytic approach for nested ESs to ensure accurate estimates accounting for within‐ and between‐study variance (Hedges et al., [<reflink idref="bib39" id="ref81">39</reflink>]). We used Fisher and Tipton's ([<reflink idref="bib32" id="ref82">32</reflink>]) <emph>robumeta</emph> package in <emph>R</emph> (R Core Team, [<reflink idref="bib61" id="ref83">61</reflink>]) to conduct the analyses. Prior research suggests that RVE meta‐analyses with fewer than 40 studies may have inflated Type I error (Tanner‐Smith & Tipton, [<reflink idref="bib77" id="ref84">77</reflink>]); thus, we used the small = TRUE command to adjust for potential small sample size bias. Further, effect size estimates with degrees of freedom (<emph>df</emph>) fewer than 4 are underpowered and increase Type I error (Tanner‐Smith & Tipton, [<reflink idref="bib77" id="ref85">77</reflink>]). Thus, we evaluated and marked estimates with <emph>df</emph> < 4 in Tables 3 and 4 and interpreted only reliable effects.</p> <p>To provide equal weights for correlated measures nested in one study, we used the correlated‐effects model for overall and moderator analyses using the modelweights = "CORR" command. We also conducted sensitivity analyses to determine the effect of the within‐study effect size correlation (<emph>ρ</emph>) using different values of <emph>ρ</emph> (e.g., 0, 0.2, 0.4, 0.6, 0.8, 1; Fisher & Tipton, [<reflink idref="bib32" id="ref86">32</reflink>]). As estimates of τ<sups>2</sups> (the between‐study variance) were not sensitive to the varying <emph>ρ</emph> values, we used the default value of <emph>ρ</emph> (= 0.8) across our meta‐regression models. Lastly, we assessed heterogeneity of variance using the prediction interval and τ<sups>2</sups>. According to the recent work of Borenstein ([[<reflink idref="bib14" id="ref87">14</reflink>], [<reflink idref="bib13" id="ref88">13</reflink>]]), the prediction interval is a more intuitive index than traditional heterogeneity statistics (e.g., <emph>Q</emph>, <emph>I</emph><sups>2</sups>), which allows researchers to quantify how the true ESs are widely distributed across studies. We reported the specific range of the ESs (i.e., the prediction interval) for each moderator. For the between‐study variance (τ<sups>2</sups>), estimates of τ<sups>2</sups> are closer to zero if the true variance is small.</p> <hd id="AN0164007724-18">Meta‐Regression Analyses</hd> <p>We estimated an intercept‐only model with RVE without conditioning on any moderator variables to obtain the weighted mean ESs of reading interventions. For the moderator analyses, we used separate RVE meta‐regression models with a single moderator as a covariate. It would be ideal to use one meta‐regression model with the forced‐entry method that includes all moderators in the given model, controlling for other covariates (Fisher & Tipton, [<reflink idref="bib32" id="ref89">32</reflink>]). However, due to the small number of studies in certain categories, the single‐model approach may not generate reliable results with sufficient <emph>df</emph> in this study. All included moderator variables were categorical; they were entered as dummy variables (0 or 1). For subset analyses, we created separate data sets for each category and used intercept‐only models to compare weighted mean ESs in subsets (i.e., reading comprehension strategy, text content, and text structure).</p> <hd id="AN0164007724-19">Publication Bias</hd> <p>Publication bias is a potential threat to the validity of meta‐analyses when published studies are systematically not representative of the target population of all studies (Banks et al., [<reflink idref="bib6" id="ref90">6</reflink>]). To examine publication bias, we first created a funnel plot to visually evaluate whether the distribution of ESs was symmetric. We then performed the following two publication bias statistics to assess publication bias: a trim‐and‐fill test (Duval & Tweedie, [<reflink idref="bib28" id="ref91">28</reflink>]) and fail‐safe <emph>N</emph> statistics, including Rosenthal's ([<reflink idref="bib64" id="ref92">64</reflink>]) and Orwin's ([<reflink idref="bib60" id="ref93">60</reflink>]) approaches.</p> <hd id="AN0164007724-20">RESULTS</hd> <p></p> <hd id="AN0164007724-21">Study Characteristics</hd> <p></p> <hd id="AN0164007724-22">Variables for Intervention Characteristics</hd> <p>Table 2 displays the study characteristic features. As illustrated, 34 of 37 studies employed an experimental design and 3 employed a quasi‐experimental design. Most studies included students in middle grades (<emph>n</emph> = 23); the remaining 13 studies were conducted in high school settings. Only Reed and Lynn ([<reflink idref="bib62" id="ref94">62</reflink>]) included one student in Grade 5 and 23 students in Grades 6−8 in their samples. Eighteen studies employed (meta)cognitive interventions, and 14 employed multicomponent interventions. Five studies were categorized as "other" and included a mixture of CAI, peer‐mediated instruction, or explicit phonics instruction. Text content and structure varied across the studies.</p> <p>2 TABLE Descriptive Summary of Included Studies</p> <p> <ephtml> <table><thead><tr valign="bottom"><th>Authors; Study Design</th><th>Total Participants (# SWDs; # of SRDs)</th><th>Grade</th><th>Intervention Type; Control Condition</th><th>Text content; Text Structure</th><th>Classroom Setting; Group Size; Duration</th><th>Delivery Agent; Assessment Type</th><th>Pretest Control; FOI; Study Quality</th></tr></thead><tbody><tr><td>Asaro‐Sadler et al., <xref ref-type="bibr" rid="bibr3">2018</xref>; QED</td><td>30 (30; 0)</td><td>HS</td><td>MC; BAU</td><td>SS; Exp</td><td>EX; LG; ST</td><td>TE; STM</td><td>No; Yes; NHQ</td></tr><tr><td>Bakken et al., <xref ref-type="bibr" rid="bibr5">1997</xref>; RCT</td><td>54 (54; 0)</td><td>MS</td><td>T1: TS; T2: MC; BAU</td><td>Sci; Exp</td><td>EX; LG; ST</td><td>RE; RD</td><td>No; NR; NHQ</td></tr><tr><td>Barth & Elleman, <xref ref-type="bibr" rid="bibr7">2017</xref>; RCT</td><td>66 (20; 46)0002</td><td>MS</td><td>MC; BAU</td><td>ELA; Exp & NA</td><td>EX; SM; ST</td><td>OT; STM</td><td>Yes; Yes; HQ</td></tr><tr><td>Barth, Vaughn, et al., <xref ref-type="bibr" rid="bibr8">2016</xref>; RCT</td><td>134 (42; 92)0002</td><td>MS</td><td>MC; BAU</td><td>Sci; Exp</td><td>EX; SM; ST</td><td>OT; STM & RD</td><td>Yes; Yes; HQ</td></tr><tr><td>Berkeley, Mastropieri et al., <xref ref-type="bibr" rid="bibr9">2011</xref>; RCT</td><td>59 (59; 0)</td><td>MS & HS</td><td>MC; RN</td><td>ELA; Exp</td><td>EX; LG; ST</td><td>TE & RE; RD</td><td>Yes; Yes; HQ</td></tr><tr><td>Berkeley & Riccomini, <xref ref-type="bibr" rid="bibr10">2011</xref>; RCT</td><td>319 (31; 0)</td><td>MS</td><td>MC; IR</td><td>SS; Exp</td><td>IN; LG; ST</td><td>RE; RD</td><td>No; Yes; NHQ</td></tr><tr><td>Bos & Anders, <xref ref-type="bibr" rid="bibr16">1990</xref>; RCT</td><td>61 (61; 0)</td><td>MS</td><td>MC; EDI</td><td>Sci; Exp & NA</td><td>EX; LG; ST</td><td>RE; RD</td><td>No; NR; NHQ</td></tr><tr><td>Bryant et al., <xref ref-type="bibr" rid="bibr17">2000</xref>; RCT</td><td>60 (14; 17)</td><td>MS</td><td>Multi; none</td><td>ELA, SS, Sci; Exp & NA</td><td>IN; LG; IT</td><td>TE; RD</td><td>No; Yes; NHQ</td></tr><tr><td>Calhoon, <xref ref-type="bibr" rid="bibr20">2005</xref>; RCT</td><td>38 (38; 0)</td><td>MS</td><td>PM; Phonics</td><td>ELA; NA</td><td>EX; SM; IT</td><td>TE; STM</td><td>Yes; Yes; HQ</td></tr><tr><td>Darch & Gersten, <xref ref-type="bibr" rid="bibr25">1986</xref>; RCT</td><td>24 (24; 0)</td><td>HS</td><td>MC; Basal</td><td>ELA; NA</td><td>EX; LG; ST</td><td>RE; RD</td><td>No; NR; NHQ</td></tr><tr><td>Denton et al., <xref ref-type="bibr" rid="bibr26">2017</xref>; RCT</td><td>48 (3; 45)0002</td><td>HS</td><td>Multi; BAU</td><td>ELA; NA</td><td>EX; SM; ST</td><td>RE; STM</td><td>Yes; Yes; HQ</td></tr><tr><td>Diliberto et al., <xref ref-type="bibr" rid="bibr27">2008</xref>; QED</td><td>74 (40; 34)0002</td><td>MS</td><td>Phonics; CC</td><td>ELA; NR</td><td>EX; SM; IT</td><td>TE; STM</td><td>Yes; Yes; NHQ</td></tr><tr><td>Fagella‐Luby et al., <xref ref-type="bibr" rid="bibr30">2007</xref>; RCT</td><td>79 (14; 75)</td><td>MS</td><td>T1: MC; T2: MC</td><td>ELA; NA</td><td>IN; LG; ST</td><td>RE; RD</td><td>No; Yes; NHQ</td></tr><tr><td>Gajira & Salvia, <xref ref-type="bibr" rid="bibr35">1992</xref>; RCT</td><td>30 (30; 0)</td><td>MS & HS</td><td>MC; RW</td><td>ELA; Exp</td><td>EX; SM; ST</td><td>RE; RD</td><td>Yes; NR; NHQ</td></tr><tr><td>Jitendra et al., <xref ref-type="bibr" rid="bibr42">2000</xref>; RCT</td><td>33 (33; 0)</td><td>MS</td><td>MC; BAU</td><td>ELA; Exp & NA</td><td>EX; LG; ST</td><td>RE; RD</td><td>Yes; Yes; HQ</td></tr><tr><td>Katims & Harris, <xref ref-type="bibr" rid="bibr43">1997</xref>; RCT</td><td>207 (25; 182)</td><td>MS</td><td>MC; BAU</td><td>ELA; Exp & NA</td><td>IN; LG; ST</td><td>TE; RD</td><td>Yes; NR; NHQ</td></tr><tr><td>Kent et al., <xref ref-type="bibr" rid="bibr44">2015</xref>; RCT</td><td>24 (24; 0)</td><td>HS</td><td>PM; BAU</td><td>SS; Exp</td><td>IN; LG; ST</td><td>TE; RD</td><td>Yes; Yes; HQ</td></tr><tr><td>Kim et al., <xref ref-type="bibr" rid="bibr45">2006</xref>; RCT</td><td>34 (34; 0)</td><td>MS</td><td>MC; Multi</td><td>ELA; Exp</td><td>EX; LG; IT</td><td>TE & RE; RD & STM</td><td>No; Yes; NHQ</td></tr><tr><td>Klinger & Vaughn, <xref ref-type="bibr" rid="bibr46">1996</xref>; RCT</td><td>26 (26; 0)</td><td>MS</td><td>T1: MC; T2: MC</td><td>SS; Exp</td><td>EX; LG; ST</td><td>RE; STM</td><td>No; NR; NHQ</td></tr><tr><td>Lovett et al., <xref ref-type="bibr" rid="bibr48">2012</xref>; RCT</td><td>351 (351; 0)</td><td>HS</td><td>Multi; BAU</td><td>ELA; Exp & NA</td><td>EX; LG; ST</td><td>TE; STM</td><td>Yes; Yes; NHQ</td></tr><tr><td>Malone & Mastropieri, <xref ref-type="bibr" rid="bibr49">1991</xref>; RCT</td><td>45 (45; 0)</td><td>MS</td><td>T1: MC; T2: MC; T3: MC</td><td>SS; NR</td><td>EX; SM; ST</td><td>RE; RD</td><td>No; NR; NHQ</td></tr><tr><td>O'Connor et al., <xref ref-type="bibr" rid="bibr58">2017</xref>; RCT</td><td>34 (34; 0)</td><td>MS</td><td>Multi; BAU</td><td>SS; Exp</td><td>EX; LG; IT</td><td>TE; RD</td><td>Yes; Yes; HQ</td></tr><tr><td>Reed & Lynn, <xref ref-type="bibr" rid="bibr62">2016</xref>; QED</td><td>24 (16; 8)</td><td>MS</td><td>T1: MC; T2: MC; T3: MC</td><td>ELA; Exp & NA</td><td>EX; LG; ST</td><td>RE; STM</td><td>No; Yes; NHQ</td></tr><tr><td>Reynolds, <xref ref-type="bibr" rid="bibr63">2021</xref>; RCT</td><td>152 (14; 0)0002</td><td>HS</td><td>Multi; BAU</td><td>ELA, SS, Sci; EXP & NA</td><td>EX; SM; ST</td><td>OT; STM</td><td>Yes; Yes; HQ</td></tr><tr><td>Solis et al., <xref ref-type="bibr" rid="bibr72">2015</xref>; RCT</td><td>44 (12; 32)0002</td><td>HS</td><td>Multi; BAU</td><td>SS & Sci; Exp & NA</td><td>EX; LG; ST</td><td>TE; RD & STM</td><td>Yes; Yes; HQ</td></tr><tr><td>Solis et al., <xref ref-type="bibr" rid="bibr73">2018</xref>; RCT</td><td>91 (14; 77)0002</td><td>HS</td><td>Multi; BAU</td><td>SS & Sci; Exp & NA</td><td>EX; SM; IT</td><td>TE; STM</td><td>Yes; Yes; HQ</td></tr><tr><td>Swanson, Wanzek, et al., <xref ref-type="bibr" rid="bibr76">2017</xref>; RCT</td><td>78 (9; 69)0002</td><td>MS</td><td>Multi; BAU</td><td>SS; Exp</td><td>IN; LG; LT</td><td>TE; RD & STM</td><td>Yes; Yes; HQ</td></tr><tr><td>Twyman & Tindal, <xref ref-type="bibr" rid="bibr79">2006</xref>; RCT</td><td>24 (24; 0)</td><td>HS</td><td>CAI; BAU</td><td>SS; Exp</td><td>IN; LG; ST</td><td>TE; STM</td><td>No; Yes; NHQ</td></tr><tr><td>Vaughn, Klinger et al., <xref ref-type="bibr" rid="bibr81">2011</xref>; RCT</td><td>782 (84; 698)0002</td><td>MS</td><td>MC; BAU</td><td>ELA; Exp</td><td>IN; NR; IT</td><td>TE; STM</td><td>No; Yes; NHQ</td></tr><tr><td>Vaughn, Roberts, Schnakenberg, et al., <xref ref-type="bibr" rid="bibr82">2015</xref>; RCT</td><td>77 (77; 0)</td><td>HS</td><td>Multi; BAU</td><td>SS & Sci; Exp & NA</td><td>EX; LG; LT</td><td>TE; STM</td><td>Yes; Yes; HQ</td></tr><tr><td>Vaughn, Roberts, Wexler, et al., <xref ref-type="bibr" rid="bibr82">2015</xref>; RCT</td><td>375 (66; 309)0002</td><td>HS</td><td>Multi; BAU</td><td>SS & Sci; Exp & NA</td><td>EX; LG; LT</td><td>TE; STM</td><td>Yes; Yes; HQ</td></tr><tr><td>Vaughn, Wexler, Leroux, et al., <xref ref-type="bibr" rid="bibr85">2012</xref>; RCT</td><td>41 (9; 32)0002</td><td>MS</td><td>Multi; BAU</td><td>SS & Sci; Exp & NA</td><td>EX; SM; LT</td><td>TE; STM</td><td>Yes; Yes; HQ</td></tr><tr><td>Vaughn, Wexler, Roberts, et al., <xref ref-type="bibr" rid="bibr86">2011</xref>; RCT</td><td>182 (64; 118)</td><td>MS</td><td>Multi; BAU</td><td>SS & Sci; Exp & NA</td><td>EX; SM; LT</td><td>TE; STM</td><td>Yes; Yes; HQ</td></tr><tr><td>Wanzek et al., <xref ref-type="bibr" rid="bibr90">2011</xref>; RCT</td><td>135 (135; 0)</td><td>MS</td><td>Multi; BAU</td><td>ELA; Exp & NA</td><td>EX; LG; LT</td><td>TE; STM</td><td>Yes; Yes; HQ</td></tr><tr><td>Wexler et al., <xref ref-type="bibr" rid="bibr92">2010</xref>; RCT</td><td>96 (96; 0)</td><td>HS</td><td>PM; BAU</td><td>ELA; Exp</td><td>EX; LG; IT</td><td>RE; STM</td><td>Yes; Yes; HQ</td></tr><tr><td>Williams & Vaughn, <xref ref-type="bibr" rid="bibr93">2020</xref>; RCT</td><td>85 (85; 0)</td><td>HS</td><td>Multi; BAU</td><td>SS & Sci; Exp</td><td>EX; LG; LT</td><td>TE; STM</td><td>Yes; Yes; HQ</td></tr><tr><td>Wong & Jones, <xref ref-type="bibr" rid="bibr94">1982</xref>; RCT</td><td>120 (60; 0)</td><td>MS & HS</td><td>MC; NT</td><td>ELA; NR</td><td>EX; LG; ST</td><td>RE; RD</td><td>No; NR; NHQ</td></tr></tbody></table> </ephtml> </p> <ulist> <item>2 <emph>Note</emph>. SWDs = students with disabilities; SRDs = students with reading difficulties; FOI = fidelity of implementation; QED = quasi‐experimental design; RCT = random control trial; HS = high school; MS = middle school; MC = (meta)cognitive; T = treatment (intervention); TS = text structure; IR = independent reading; EDI = explicit definition instruction; Multi = multicomponent; PM = peer‐mediated; CAI = computer‐adapted instruction; BAU = business as usual; CC = core curriculum; RW = reader's workshop; ELA = English language arts; SS = social studies; Sci = Science; NR = not reported; Exp = expository; NA = narrative; LG = large group (> 5); SM = small group (≤ 5); ST = short term (< 2 months); IT = intermediate term (≥ 2 months, < 1 year); LT = long term (≥ 1 year); RE = researcher; TE = teacher; OT = others (e.g., undergraduate tutors); RD = researcher‐developed measures; STM = standardized measures; HQ = high quality; NHQ = not high quality; NT = no training.</item> <item>3 a Authors included number of students served by special education but did not disaggregate results based upon disability status or at‐risk status.</item> </ulist> <p>Researchers selected texts in English language arts (ELA; <emph>n</emph> = 16), social studies (<emph>n</emph> = 8), science (<emph>n</emph> = 4), and multiple content areas (<emph>n</emph> = 9). Also, researchers used different text structures, including expository passages (<emph>n</emph> = 16), both expository and narrative passages (<emph>n</emph> = 15), and narrative passages (<emph>n</emph> = 3). Three studies did not indicate the type of text structure used. Twenty‐nine studies were conducted in exclusive settings, and eight studies were conducted in inclusive settings.</p> <p>Most interventions (<emph>n =</emph> 28) were delivered to moderately large groups of students (more than five). In eight studies, interventions were implemented with groups of five or fewer students, and one study did not report intervention group size (Vaughn, Klinger, et al., [<reflink idref="bib81" id="ref95">81</reflink>]). Most studies included sessions that were short in duration; implemented for less than 2 months (<emph>n</emph> = 20). Ten interventions were implemented between 2 months to 1 year (intermediate duration). Seven interventions were implemented for 1 year or more (long duration).</p> <p>The intervention agent varied from study to study, with teachers acting as the intervention agent in 19 studies followed by researchers in 13 studies. In two studies, both teachers and researchers implemented interventions. In the remaining three studies, interventions were implemented by tutors or undergraduate students.</p> <p>Status of students was coded as either SWDs or at risk depending on authors' descriptions of student participants; however, many studies did not disaggregate scores for SWDs or at‐risk student groups, respectively. Thus, we classified students into two categories: (a) reference group includes some at‐risk students and some SWDs and (b) SWDs‐only group.</p> <hd id="AN0164007724-23">Variables for Study Design Characteristics</hd> <p>Researchers measured the effectiveness of interventions on the following dependent measures: researcher‐developed assessments (<emph>n</emph> = 14), standardized assessments (<emph>n</emph> = 19), or both types of measures (<emph>n</emph> = 4). Approximately half of the studies (<emph>n</emph> = 19) met our criteria for high quality, which means they (a) had at least 20 (80%) out of 24 quality indicators set forth by CEC (Cook et al., [<reflink idref="bib24" id="ref96">24</reflink>]), (b) implemented a randomized controlled trial (RCT) design, (c) reported measuring fidelity of implementation (FOI), and (d) controlled for pretest scores. The remaining 18 studies did not meet these quality standards. It is important to note that 17 out of 19 studies that met high‐quality criteria were published after 2005, when CEC first developed reporting standards for research.</p> <hd id="AN0164007724-24">Overall Effect Size</hd> <p>We included a total of 97 ESs from reading comprehension outcomes from 37 studies. Results indicate reading interventions had a positive and moderate effect (<emph>g</emph> = 0.631, <emph>p</emph> <.001, <emph>df</emph> = 34) on reading comprehension outcomes. That is, students with RDs (SWD and at‐risk students) in treatment groups outperformed those in control by 0.63 standard deviation units. Heterogeneity analysis found significant and high variability between ESs (95% prediction interval [PI] = −0.33 to 1.60; τ<sups>2</sups> = 0.242). These results suggest the presence of moderator effects.</p> <hd id="AN0164007724-25">Moderator Analyses of Intervention Characteristics</hd> <p>We analyzed eight moderators related to intervention characteristics (i.e., type of reading comprehension strategy, text structure, text content, instructional setting, group size, duration of intervention, agent of instruction, status of students). Table 3 presents the results of moderator analyses. As illustrated, we found high heterogeneity in ESs across our meta‐regression models, with the estimates of τ<sups>2</sups> ranging from 0.185 to 0.255.</p> <p>3 TABLE Estimates of the Meta‐Regression Analyses on Intervention Characteristics</p> <p> <ephtml> <table><thead><tr><th /><th /><th /><th /><th /><th /><th /><th>95% Prediction Interval</th><th /></tr><tr><th /><th><italic>k</italic></th><th><italic>n</italic></th><th><italic>β</italic></th><th><italic>SE</italic></th><th><italic>df</italic></th><th>Sig.</th><th>LL</th><th>UL</th><th><italic>τ<sup>2</sup></italic></th></tr></thead><tbody><tr><td>Overall mean ES</td><td>37</td><td>97</td><td>0.631</td><td>0.103</td><td>34</td><td><.001***</td><td>–0.33</td><td>1.60</td><td>0.242</td></tr><tr><td>Intervention characteristics</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>Reading comprehension strategy</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>(Meta)cognitive</td><td>18</td><td>59</td><td>0.720</td><td>0.207</td><td>32.0</td><td>.001**</td><td>–0.19</td><td>1.63</td><td>0.216</td></tr><tr><td>Multicomponent</td><td>14</td><td>30</td><td>–0.573</td><td>0.172</td><td>28.6</td><td>.002**</td><td>–1.51</td><td>0.36</td><td>0.229</td></tr><tr><td>Other (e.g., CAI, PM)</td><td>5</td><td>8</td><td>–0.333</td><td>0.172</td><td>5.21</td><td>.108</td><td>–1.31</td><td>0.65</td><td>0.250</td></tr><tr><td>Text content</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>ELA</td><td>16</td><td>46</td><td>0.206</td><td>0.221</td><td>30.3</td><td>.359</td><td>–0.78</td><td>1.20</td><td>0.255</td></tr><tr><td>Science</td><td>4</td><td>12</td><td>0.318</td><td>0.459</td><td>3.58^</td><td>.530</td><td>–0.67</td><td>1.31</td><td>0.254</td></tr><tr><td>Social studies</td><td>8</td><td>17</td><td>0.149</td><td>0.252</td><td>10.4</td><td>.566</td><td>–0.82</td><td>1.12</td><td>0.243</td></tr><tr><td>Multiple subjects</td><td>9</td><td>22</td><td>–0.517</td><td>0.151</td><td>13.8</td><td>.004**</td><td>–1.49</td><td>0.45</td><td>0.245</td></tr><tr><td>Text structure</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>Expository</td><td>16</td><td>42</td><td>0.019</td><td>0.215</td><td>30.4</td><td>.932</td><td>–0.97</td><td>1.01</td><td>0.255</td></tr><tr><td>Narrative</td><td>3</td><td>6</td><td>0.588</td><td>0.885</td><td>1.93^</td><td>.577</td><td>–0.38</td><td>1.55</td><td>0.243</td></tr><tr><td>Both expository and narrative</td><td>15</td><td>45</td><td>–0.318</td><td>0.193</td><td>29.8</td><td>.109</td><td>–1.30</td><td>0.66</td><td>0.251</td></tr><tr><td>Not reported</td><td>3</td><td>4</td><td>0.502</td><td>0.514</td><td>2.26^</td><td>.421</td><td>–0.46</td><td>1.46</td><td>0.239</td></tr><tr><td>Instructional setting</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>Inclusive (Ref: Exclusive; k = 29, n = 79)</td><td>8</td><td>18</td><td>–0.090</td><td>0.284</td><td>10.0</td><td>.758</td><td>–1.07</td><td>0.89</td><td>0.252</td></tr><tr><td>Group size</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>Small groups (≤ 5 per; Ref: large group; k = 28, n = 68)</td><td>8</td><td>26</td><td>0.006</td><td>0.269</td><td>17.5</td><td>.983</td><td>–0.98</td><td>0.99</td><td>0.254</td></tr><tr><td>Duration of intervention</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>Short term (≤ 2 months)</td><td>20</td><td>56</td><td>0.530</td><td>0.192</td><td>32.3</td><td>.009**</td><td>–0.43</td><td>1.49</td><td>0.242</td></tr><tr><td>Intermediate term (> 2 months, < 12 months)</td><td>10</td><td>24</td><td>–0.275</td><td>0.172</td><td>11.5</td><td>.136</td><td>–1.26</td><td>0.71</td><td>0.252</td></tr><tr><td>Long term (≥ 12 months)</td><td>7</td><td>17</td><td>–0.454</td><td>0.153</td><td>11.4</td><td>.012*</td><td>–1.44</td><td>0.53</td><td>0.252</td></tr><tr><td>Agent of intervention</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>Teacher</td><td>19</td><td>40</td><td>–0.615</td><td>0.218</td><td>32.2</td><td>.008**</td><td>–1.57</td><td>0.34</td><td>0.238</td></tr><tr><td>Researcher</td><td>13</td><td>40</td><td>0.867</td><td>0.284</td><td>19.7</td><td>.006**</td><td>0.02</td><td>1.71</td><td>0.187</td></tr><tr><td>Teacher and researcher</td><td>2</td><td>9</td><td>0.118</td><td>0.152</td><td>1.11^</td><td>.568</td><td>–0.85</td><td>1.09</td><td>0.245</td></tr><tr><td>Other (e.g., undergraduate tutors)</td><td>3</td><td>8</td><td>–0.416</td><td>0.226</td><td>2.46^</td><td>.183</td><td>–1.39</td><td>0.56</td><td>0.247</td></tr><tr><td>Status of students</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>SWD only (Ref: Some at‐risk, some SWD; k = 16, n = 43)</td><td>250003</td><td>54</td><td>0.455</td><td>0.155</td><td>28.0</td><td>.006**</td><td>–0.49</td><td>1.40</td><td>0.234</td></tr><tr><td>Study design characteristics</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>Type of dependent measure</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>Standardized (Ref: researcher‐developed; k = 18, n = 41)</td><td>230004</td><td>56</td><td>–0.747</td><td>0.204</td><td>29.8</td><td><.001***</td><td>–1.59</td><td>0.10</td><td>0.185</td></tr><tr><td>Study quality</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>High quality (Ref: not high quality; k = 18, n = 53)</td><td>19</td><td>44</td><td>–0.600</td><td>0.210</td><td>32.0</td><td>.007**</td><td>–1.53</td><td>0.33</td><td>0.224</td></tr></tbody></table> </ephtml> </p> <ulist> <item>4 <emph>Note</emph>. β = average Hedges' <emph>g</emph> effect size; SE = standard error, <emph>k</emph> = number of studies; <emph>n</emph> = number of effect sizes; LL, UL = 95% prediction interval lower and upper limits; CAI = computer‐based instruction; PM = peer‐mediated instruction.</item> <item>5 <emph>^</emph>Estimate is unreliable (<emph>df</emph> < 4).</item> <item>6 a Four studies measured the SWD group and at‐risk student group separately.</item> <item>7 b Four studies used both researcher‐developed and standardized measures.</item> <item>8 *<emph>p</emph> <.05. **<emph>p</emph> <.01. ***<emph>p</emph> <.001.</item> </ulist> <p>Reading comprehension strategies included three categories (i.e., [meta]cognitive, multicomponent, "other"). Studies mostly examined the impact of (meta)cognitive strategies (49%) and multicomponent strategies (38%). (Meta)cognitive strategies had substantially larger effects on reading comprehension than other strategies (<emph>β</emph> = 0.720, <emph>p</emph> =.001, <emph>df =</emph> 32). Additionally, multicomponent strategies were significantly less effective (<emph>β</emph> = −0.573, <emph>p</emph> =.002, <emph>df =</emph> 28.6) than other strategies. "Other" strategies were not reliable due to fewer than 4 <emph>df</emph>.</p> <p>Text content was generally not associated with treatment efficacy, except in the case of multiple text content studies. Interventions that used passages with topics in ELA (<emph>β</emph> = 0.206, <emph>p</emph> =.359, <emph>df =</emph> 30.3) or social studies (<emph>β</emph> = 0.149, <emph>p</emph> =.566, <emph>df =</emph> 10.4) were not statistically different from those using other text content. Studies that used text in multiple subject areas had a significantly smaller effect size (<emph>β</emph> = −0.517, <emph>p</emph> =.151, <emph>df =</emph> 13.8) than those using one subject area. Science was excluded due to its small <emph>df</emph> (3.58). Text structure (expository texts: <emph>β</emph> = 0.019, <emph>p</emph> =.932; narrative texts: <emph>β</emph> = 0.588, <emph>p</emph> =.577; both expository and narrative texts: <emph>β</emph> = −0.318, <emph>p</emph> =.109), instructional setting (inclusive: <emph>β</emph> = −0.090, <emph>p</emph> =.758, <emph>df =</emph> 10.0), and group size (small group: <emph>β</emph> = −0.040, <emph>p</emph> =.895, <emph>df =</emph> 14.4) were not statistically significant moderators of intervention effects.</p> <p>Interventions that were short in duration had the largest effect (<emph>β</emph> = 0.530, <emph>p</emph> =.009, <emph>df =</emph> 32.3) compared to long interventions (<emph>β</emph> = −0.454, <emph>p</emph> =.012, <emph>df =</emph> 11.4). Intermediate interventions (<emph>β</emph> = −0.374, <emph>p</emph> =.044, <emph>df =</emph> 16.9) were not statistically significant. Interventions implemented by researchers had a significantly larger effect (<emph>β</emph> = 0.867, <emph>p</emph> =.006, <emph>df =</emph> 19.7) than those implemented by teachers (<emph>β</emph> = −0.615, <emph>p</emph> =.008, <emph>df =</emph> 32.2). We excluded studies with agents that included (a) both researchers and teachers or (b) other agents (e.g., undergraduate tutors) due to their small <emph>df</emph> (< 4). Moderating effects were significant for student status. That is, interventions had a larger effect for the SWDs‐only group (<emph>β</emph> = 0.455, <emph>p</emph> =.006, <emph>df =</emph> 28.0) than for the at‐risk student group.</p> <hd id="AN0164007724-26">Moderator Analyses of Study Design Characteristics</hd> <p>Table 3 displays the meta‐regression analysis results for type of dependent measure and study quality. Interventions assessed using standardized tests had smaller effects (<emph>β</emph> = −0.747, <emph>p <</emph>.001, <emph>df =</emph> 29.8) than interventions assessed by researcher‐developed tests, and these effects were significant. High‐quality studies yielded significantly smaller effects (<emph>β</emph> = −0.600, <emph>p</emph> =.007, <emph>df =</emph> 32) than studies that did not meet the requirements for quality research.</p> <hd id="AN0164007724-27">Subset Analyses for Reading Comprehension Strategy, Text Structure, and Text Content</hd> <p>We performed three subset analyses to compare mean effects of intervention characteristics (i.e., reading comprehension strategy, text structure, and text content) to identify the most effective interventions for SWDs and students with RDs. The findings of the subset analyses are reported in Table 4.</p> <p>4 TABLE Estimates of Subgroup Analyses for Reading Comprehension Strategy, Text Structure, and Text Content</p> <p> <ephtml> <table><thead><tr><th /><th /><th /><th /><th /><th /><th /><th>95% Prediction Interval</th><th /></tr><tr><th /><th><italic>k</italic></th><th><italic>n</italic></th><th><italic>g</italic></th><th><italic>SE</italic></th><th><italic>df</italic></th><th>Sig.</th><th>LL</th><th>UL</th><th><italic>τ<sup>2</sup></italic></th></tr></thead><tbody><tr><td>Reading comprehension strategy</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>(Meta)cognitive</td><td>18</td><td>59</td><td>1.170</td><td>0.244</td><td>16.7</td><td><.001***</td><td>–0.51</td><td>2.85</td><td>0.737</td></tr><tr><td>Multicomponent</td><td>14</td><td>30</td><td>0.308</td><td>0.052</td><td>8.33</td><td><.001***</td><td>0.16</td><td>0.46</td><td>0.006</td></tr><tr><td>Other (e.g., CAI, PM)</td><td>5</td><td>8</td><td>0.356</td><td>0.132</td><td>3.14^</td><td>.071</td><td>0.36</td><td>0.36</td><td>0.00</td></tr><tr><td>Text content</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>ELA</td><td>16</td><td>46</td><td>0.808</td><td>0.214</td><td>14.3</td><td>.002**</td><td>–0.44</td><td>2.05</td><td>0.404</td></tr><tr><td>Science</td><td>4</td><td>12</td><td>0.991</td><td>0.397</td><td>2.98^</td><td>.088</td><td>–0.83</td><td>2.81</td><td>0.865</td></tr><tr><td>Social studies</td><td>8</td><td>17</td><td>0.755</td><td>0.227</td><td>6.88</td><td>.013*</td><td>–0.29</td><td>1.80</td><td>0.284</td></tr><tr><td>Multiple subjects</td><td>9</td><td>22</td><td>0.280</td><td>0.066</td><td>4.84</td><td>.009*</td><td>0.16</td><td>0.40</td><td>0.004</td></tr><tr><td>Text structure</td><td /><td /><td /><td /><td /><td /><td /><td /><td /></tr><tr><td>Expository</td><td>16</td><td>42</td><td>0.681</td><td>0.185</td><td>14.6</td><td>.002**</td><td>–0.58</td><td>1.94</td><td>0.411</td></tr><tr><td>Narrative</td><td>3</td><td>6</td><td>1.720</td><td>1.32</td><td>1.98^</td><td>.325</td><td>–1.38</td><td>4.82</td><td>2.501</td></tr><tr><td>Both expository and narrative</td><td>15</td><td>45</td><td>0.413</td><td>0.080</td><td>11.4</td><td><.001***</td><td>–0.05</td><td>0.88</td><td>0.056</td></tr><tr><td>Not reported</td><td>3</td><td>4</td><td>1.170</td><td>0.558</td><td>1.97^</td><td>.174</td><td>–0.30</td><td>2.64</td><td>0.565</td></tr></tbody></table> </ephtml> </p> <ulist> <item>9 <emph>Note. g</emph> = average Hedges' <emph>g</emph> effect size; SE = standard error; <emph>k</emph> = number of studies; <emph>n</emph> = number of effect sizes; LL, UL = 95% prediction interval lower and upper limits; CAI = computer‐based instruction; PM = peer‐mediated instruction.</item> <item>10 <emph>^</emph>Estimate is unreliable (<emph>df</emph> < 4).</item> <item>11 *<emph>p</emph> <.05. **<emph>p</emph> <.01. ***<emph>p</emph> <.001.</item> </ulist> <hd id="AN0164007724-28">Mean Effect Comparison for Reading Comprehension Strategies</hd> <p>Results showed the mean ESs for (meta)cognitive and multicomponent reading comprehension strategies were statistically significant and reliable (<emph>df</emph> ≥ 4). Other strategies (<emph>g</emph> = 0.356, <emph>p =</emph>.071, <emph>df =</emph> 3.14) produced untrustworthy estimates due to their small <emph>df</emph>. Studies that examined (meta)cognitive strategies generated the highest mean effect size (<emph>g</emph> = 1.170, <emph>p <</emph>.001, <emph>df =</emph> 16.7); that is, students who received (meta)cognitive strategies performed 1.17 standard deviation units higher than those in control groups. The significant variance between ESs for these studies indicated that between‐study heterogeneity was likely more than chance (95% PI = −0.51 to 2.85; τ<sups>2</sups> = 0.74). We obtained a medium and positive mean effect size (<emph>g</emph> = 0.308, <emph>p <</emph>.001, <emph>df =</emph> 8.33) for multicomponent strategies, with a 0.308 standard deviation higher effect on students in treatment compared to control. The small between‐study variance (95% PI = 0.16 to 0.46; τ<sups>2</sups> = 0.006) indicated there was little variability in the effectiveness of the multicomponent interventions.</p> <hd id="AN0164007724-29">Mean Effect Comparison for Text Content</hd> <p>Interventions that used text with topics in ELA and social studies had significant effects (<emph>g</emph> = 0.808, <emph>p =</emph>.002, <emph>df =</emph> 14.3 for ELA; <emph>g</emph> = 0.755, <emph>p =</emph>.013, <emph>df =</emph> 6.88 for social studies; <emph>g</emph> = 0.280, <emph>p =</emph>.009, <emph>df =</emph> 4.84 for multiple subjects). Students who received interventions using text with topics in ELA outperformed those in control groups by 0.808 standard deviation units. Interventions that used solely social studies readings had a 0.755 increase in their comprehension scores compared to the control. Interventions that used passages in more than two content areas were associated with a 0.280 increase in student comprehension outcomes. ESs varied more for ELA than social studies and multiple subjects; there was substantial between‐study variance for ELA (95% PI = −0.44 to 2.05; τ<sups>2</sups> = 0.404). Small <emph>df</emph> for science makes it difficult to assess ESs.</p> <hd id="AN0164007724-30">Mean Effect Comparison for Text Structure</hd> <p>Expository texts had the largest effect on intervention efficacy (<emph>g</emph> = 0.681, <emph>p =</emph>.002, <emph>df =</emph> 14.6). Students exposed to interventions using expository texts outperformed those in control conditions by 0.681 standard deviation units. Using a combination of expository and narrative texts had a significant moderating effect on intervention effectiveness (<emph>g</emph> = 0.413, <emph>p <</emph>.001, <emph>df =</emph> 11.4). Students exposed to interventions using both expository and narrative texts outperformed those in control conditions by 0.413. The variance in effects for expository texts (95% PI = −0.58 to 1.94; τ<sups>2</sups> = 0.411) and both expository and narrative texts (95% PI = −0.05 to 0.88; τ<sups>2</sups> = 0.056) suggest considerable heterogeneity in the moderator variable.</p> <hd id="AN0164007724-31">Publication Bias</hd> <p>Results indicate no evidence of publication bias. Figure 2 shows that the <emph>ES</emph>s are asymmetrically distributed around the mean with several outliers, and some data points on both left and right sides fell of the 95% CI. This provides some evidence of publication bias; however, results of the trim‐and‐fill test (Duval & Tweedie, [<reflink idref="bib28" id="ref97">28</reflink>]) presented in Table 5 showed that the mean effect size (<emph>ES</emph> = 0.775, <emph>p</emph> <.001) was the same as the effect size obtained from the random‐effect model (<emph>ES</emph> = 0.775, <emph>p</emph> <.001), suggesting no evidence of publication bias. Fail‐safe <emph>N</emph> statistics across the two methods reported in Table 6 estimated the number of null effect studies that could reduce the obtained <emph>ES</emph>s was not statistically significant (Borenstein et al., [<reflink idref="bib15" id="ref98">15</reflink>]; McEwan et al., [<reflink idref="bib51" id="ref99">51</reflink>]). In Rosenthal's approach ([<reflink idref="bib64" id="ref100">64</reflink>]), since our resulting fail‐safe <emph>N</emph> (<emph>N</emph> = 13,657) was larger than the critical value 495, publication bias is unlikely within the pool of studies. Further, Orwin's approach ([<reflink idref="bib60" id="ref101">60</reflink>]) to establishing publication bias showed that 750 null effect studies would be required to move the average effect size (<emph>ES</emph> = 0.873) below 0.100 (Vevea et al., [<reflink idref="bib88" id="ref102">88</reflink>]). As it is unlikely that including gray literature would result in 750 null effect studies, we conclude that the results indicate there is no potential publication bias.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/7MJ/01may23/ldrp12307-fig-0002.jpg?ephost1=dGJyMNHX8kSepq84v%2bvlOLCmsE6epq5Srqa4SK6WxWXS" alt="ldrp12307-fig-0002.jpg" title="2 Funnel plot displaying distributions of effect sizes." /> </p> <p></p> <p>5 TABLE Publication Bias Assessment: Trim‐and‐Fill Test</p> <p> <ephtml> <table><thead><tr><th>Test</th><th>Estimate</th><th><italic>SE</italic></th><th>Imputed</th></tr></thead><tbody><tr><td>Obtained effect size<sup>a</sup></td><td>0.775***</td><td>0.097</td><td>n/a</td></tr><tr><td>Trim‐and‐fill</td><td>0.775***</td><td>0.097</td><td>0</td></tr></tbody></table> </ephtml> </p> <ulist> <item>12 <emph>Note</emph>. Obtained effect size<sups>a</sups> was estimated using the random‐effects model. SE = standard errors.</item> <item>13 ***<emph>p</emph> <.001.</item> <item>6 TABLE Publication Bias Assessment: Fail‐Safe N Statistics: No Evidence of Publication Bias</item> </ulist> <p> <ephtml> <table><thead><tr valign="bottom"><th>Fail‐Safe <italic>N</italic> Approach</th><th>Fail‐Safe <italic>N</italic></th><th>Average Effect Size</th><th>Target Effect Size</th><th>Observed Significance Level</th><th>Target Significance Level</th><th>Note</th></tr></thead><tbody><tr><td>Obtained effect size<sup>+</sup></td><td /><td>0.775***</td><td /><td /><td /><td /></tr><tr><td>Rosenthal</td><td>13,657</td><td /><td /><td><.0001</td><td>0.05</td><td>Critical value: 5(97) + 10 = 495</td></tr><tr><td>Orwin<sup>a</sup></td><td>94</td><td>0.873</td><td>0.437</td><td /><td /><td /></tr><tr><td>Orwin<sup>b</sup></td><td>750</td><td>0.873</td><td>0.100</td><td /><td /><td /></tr></tbody></table> </ephtml> </p> <ulist> <item>14 <emph>Note</emph>. Obtained effect size<sups>+</sups> was estimated using the random‐effects model. Rosenthal's bias formula = 5<emph>n</emph> + 10; <emph>n</emph> = number of effect sizes. In the Orwin<sups>a</sups> approach, the target effect size was set by the default value. In the Orwin<sups>b</sups> approach, the target effect size was set as 0.1000.</item> <item>15 ***<emph>p</emph> <.001.</item> </ulist> <hd id="AN0164007724-33">DISCUSSION</hd> <p>As students enter middle and high school, reading comprehension becomes more complex. Secondary students not only have to decode printed material, they also must read and comprehend more discipline specific language and text structures (Catts, [<reflink idref="bib21" id="ref103">21</reflink>]). Secondary students, particularly those with RDs, demonstrate persistent struggles comprehending grade‐level texts as evident from trends in their NAEP scores (NCES, [<reflink idref="bib57" id="ref104">57</reflink>]). To change this trajectory, educators need information on effective reading comprehension interventions for secondary students that will support all learners, especially those with RDs.</p> <p>In the current study, we identified and synthesized 37 studies conducted between 1982 and 2021 and examined the intervention characteristics (i.e., intervention type, text content, text structure, classroom setting, group size, duration, agent of intervention, student status) and study design variables (i.e., type of dependent measure, study quality) that moderated the effect of intervention on reading comprehension outcomes. Further, we conducted subset analyses (i.e., separate moderator analyses) to identify which reading comprehension strategies were more effective and which text structures and text content had larger effects on student achievement. In the following, we discuss our findings considering those from prior meta‐analyses and the limitations of our current study. We also provide implications for future research and practice.</p> <hd id="AN0164007724-34">Overall Effect of Reading Comprehension Interventions</hd> <p>Our results indicate that reading comprehension interventions for students with RDs in Grades 6−12 had a significant, moderate effect on their reading comprehension outcomes though there was substantial heterogeneity among ESs. This heterogeneity suggests that the effectiveness of interventions likely varied due to intervention characteristics (e.g., intervention type, text structure) and/or conditions of implementation (e.g., group size, instructional setting). Our effect size for overall interventions (mean ES =.63) is similar to those obtained from meta‐analyses conducted within the last 10 years (mean ES =.59, Filderman et al., [<reflink idref="bib31" id="ref105">31</reflink>]; mean ES =.49, Scammacca et al., [<reflink idref="bib66" id="ref106">66</reflink>]). Like Filderman et al., we also found a substantial amount of heterogeneity in ESs (τ<sups>2</sups> = 0.24; τ<sups>2</sups> = 0.31 for Filderman et al.).</p> <hd id="AN0164007724-35">Moderator Analyses on Intervention and Study Design Characteristics</hd> <p>We examined moderators of intervention effectiveness, as heterogeneity analyses revealed considerable variance among ESs. Five intervention characteristics (i.e., reading comprehension strategy, text content, duration of intervention, agent of intervention, status of students) and two study design characteristics (i.e., type of measure, study quality) significantly influenced overall effects of interventions on students' reading comprehension.</p> <hd id="AN0164007724-36">Intervention Type</hd> <p>(Meta)cognitive strategies produced the strongest effects (<emph>β</emph> = 0.720, 95% PI = −0.19 to 1.63) on student outcomes. These findings are supported in three previous meta‐analyses (Berkeley et al., [<reflink idref="bib11" id="ref107">11</reflink>]; Edmonds et al., [<reflink idref="bib29" id="ref108">29</reflink>]; Filderman et al., [<reflink idref="bib31" id="ref109">31</reflink>]), suggesting that, as a class of interventions, (meta)cognitive strategies are effective in improving the reading comprehension of students with RDs. We do not know, however, the degree to which specific (meta)cognitive interventions, such as prediction, summarization, or inferencing, were effective. Since we focused exclusively on students with RDs in Grades 6−12, we did not have sufficient studies of specific (meta)cognitive strategies to assess their differential effectiveness.</p> <p>Multicomponent strategies yielded smaller effects on student comprehension measures than the other two reading strategies (i.e., [meta]cognitive, "other"). The smaller mean ESs were consistent with findings from previous meta‐analyses (Edmonds et al., [<reflink idref="bib29" id="ref110">29</reflink>]; Scammacca et al., [[<reflink idref="bib65" id="ref111">65</reflink>]]). Smaller effects may be related to the moderating effects of study quality, measure type, or some other condition.</p> <p>Study quality was a significant moderator in our meta‐regression, and it varied more for studies of (meta)cognitive strategies than multicomponent strategies; only 4 out of 18 (meta)cognitive strategy studies were rated as high quality (e.g., scored 20 or above on inclusion of quality indicators, used RCT, controlled for pretest scores), whereas 12 out of 14 multicomponent strategy studies were identified as high‐quality studies. In previous meta‐analyses, studies that incorporated more rigorous designs and methods tended to have lower ESs than those employing less rigorous ones (Myers et al., [<reflink idref="bib56" id="ref112">56</reflink>]; Scammacca et al., [<reflink idref="bib66" id="ref113">66</reflink>]). Most studies of multicomponent interventions included in our meta‐analysis also employed standardized measures (70%). Standardized measures were used to calculate effects for 77% (23 of the 30) of dependent variables in multicomponent interventions compared to 44% (26 of the 59) of ESs generated for standardized measures in the (meta)cognitive intervention studies. Researchers have shown ESs are often lower when standardized assessments versus researcher‐developed tests are employed (Berkeley et al., [<reflink idref="bib11" id="ref114">11</reflink>]; Edmonds et al., [<reflink idref="bib29" id="ref115">29</reflink>]; Scammacca et al., [[<reflink idref="bib65" id="ref116">65</reflink>]]; Swanson, Stevens, et al., [<reflink idref="bib75" id="ref117">75</reflink>]).</p> <hd id="AN0164007724-37">Text Content</hd> <p>This is the first meta‐analytic study, that we are aware of, to examine the moderating effect of text content (i.e., ELA, social studies, science, multiple subjects). Since scholars have argued that disciplinary texts are unique in terms of their language and structure (Shanahan & Shanahan, [<reflink idref="bib68" id="ref118">68</reflink>]), we felt that a closer examination of text content was important. Interesting, we found that text content did not moderate the effect of interventions using ELA and social studies texts, and the number of ESs for science were too low in number to assess. However, when reading interventions were assessed across multiple subjects (e.g., science and social studies), the mean ES were significantly smaller (<emph>β</emph> = −0.517) than they were for single subjects (<emph>β</emph> = 0.206; ELA, <emph>β</emph> = 0.318; science, <emph>β</emph> = 0.149; social studies). It is important to note that most studies (77%) assessing reading interventions across multiple subjects examined their efficacy using standardized tests, not researcher‐developed, and this may have accounted for the significantly smaller mean ES. At this point, it is difficult to assess scholars' claims about the need to teach strategies that are specific to disciplinary texts, as we were unable to identify strategies that were differentially effective according to the content area of the text.</p> <hd id="AN0164007724-38">Duration</hd> <p>Moderator analyses of intervention duration yielded what seem to be counterintuitive findings. That is, short interventions, those lasting 2 months or less, generated the largest effect (<emph>β</emph> = 0.530), whereas medium (<emph>β</emph> = −0.275) and long (<emph>β</emph> = −0.454) interventions yielded smaller effects, a finding that was confirmed in two prior meta‐analyses (Filderman et al., [<reflink idref="bib31" id="ref119">31</reflink>]; Scammacca et al., [<reflink idref="bib66" id="ref120">66</reflink>]). It is important to note, however, that some variables may have confounded the moderating effect of duration in our study and the two prior ones. For example, the studies coded as long in duration all focused on multicomponent strategies, and efficacy in these studies was assessed mostly by standardized measures. Finally, in each of these meta‐analytic studies, intervention duration was coded as a categorical variable, as frequency and hours of intervention were missing in many studies; coding duration as a continuous variable may provide more precise information to estimate the mean ES of intervention duration.</p> <hd id="AN0164007724-39">Implementation Conditions</hd> <p>Our findings showed interventions implemented by researchers yielded a larger mean ES (<emph>β</emph> = 0.867) than interventions implemented by other agents (e.g., teacher, undergraduate tutor), a finding supported in previous research (Berkeley et al., [<reflink idref="bib11" id="ref121">11</reflink>]; Edmonds et al., [<reflink idref="bib29" id="ref122">29</reflink>]; Scammacca et al., [[<reflink idref="bib65" id="ref123">65</reflink>]]). These findings are not surprising as researchers likely implement interventions as designed; however, they raise questions about teachers' abilities to implement interventions in ways that can improve students' reading comprehension.</p> <p>Also, the SWDs‐only group demonstrated greater improvements (<emph>β</emph> = 0.455) in reading comprehension skills than the reference group (some at‐risk students and some SWDs), a finding supported in previous meta‐analyses (Scammacca et al., [[<reflink idref="bib65" id="ref124">65</reflink>]]). However, findings from our meta‐analysis are difficult to interpret due to differences in studies of (meta)cognitive and multicomponent strategies. That is, students at risk for reading failure were more likely to be included in studies of multicomponent strategies than studies of (meta)cognitive strategies, the latter of which generated larger effects, used more researcher‐developed measures, and were of lower study quality. Without more consistency in study conditions, it is difficult to determine if SWDs respond better than students at risk for reading failure to strategies analyzed in ours and previous meta‐analyses.</p> <p>In our study, high‐quality studies were also associated with a smaller mean ES than those with lower quality. This finding was established in the meta‐analysis conducted by Scammacca et al. ([<reflink idref="bib66" id="ref125">66</reflink>]), but not the one conducted by Filderman et al. ([<reflink idref="bib31" id="ref126">31</reflink>]). Scammacca et al. found studies conducted after 2005, when quality indicators were established (Gersten et al., [<reflink idref="bib36" id="ref127">36</reflink>]), had lower effect sizes than studies published prior to the creation of quality indicators.</p> <hd id="AN0164007724-40">Subset Analyses on Intervention Characteristics</hd> <p>To determine if specific reading comprehension strategies were more effective than others, and were differentially effective according to text structure and text content, we conducted subset analyses. In the following discussion of ESs for subset analyses, we suggest reasons for the heterogeneity of ESs. We did not discuss heterogeneity of ESs in the moderator analyses because those findings may be confounded. Borenstein et al. ([<reflink idref="bib15" id="ref128">15</reflink>]) found that the dispersion of targeted studies within subgroup analyses tends to be smaller than it is in moderator analyses. Likely, subset analyses reduce potential confounding of treatment effects caused by the presence of other variables compared to moderator analyses.</p> <hd id="AN0164007724-41">Reading Comprehension Strategy</hd> <p>Our subset analyses supported our moderator analyses and showed that only (meta)cognitive and multicomponent strategies yielded significant effects; yet there were differences in the heterogeneity of ESs for both subgroups. Studies of the other strategy types (e.g., peer‐mediated strategies) were diverse and underpowered due to small <emph>df</emph>; thus, we did not conduct subset analyses on them.</p> <p>Studies of (meta)cognitive strategies yielded a large, significant mean ES, but there was considerable heterogeneity among the ESs. This heterogeneity suggests the presence of moderators, or interactions among moderators, that we could not test because of insufficient sample sizes. Perhaps, certain (meta)cognitive interventions (e.g., summarization, self‐monitoring, inferencing) are more effective for improving the comprehension of students with RDs than other (meta)cognitive interventions. According to Filderman et al. ([<reflink idref="bib31" id="ref129">31</reflink>]), main idea strategy (<emph>g =</emph> 0.72) and prediction strategy (<emph>g =</emph> 0.60) yielded larger effects than other (meta)cognitive strategies (e.g., <emph>g</emph> = 0.47, text structure strategy). Also, specific interventions (e.g., summarization) may be more effective for certain text structures (e.g., expository) or text content and less effective for others. Also, as we noted previously, there were differences in the study quality indicators and dependent measures used for (meta)cognitive vs. Multicomponent interventions, which may also have contributed to the heterogeneity of ESs.</p> <p>The ESs for studies focused on multicomponent strategies were significant, smaller, and more homogeneous than those found for (meta)cognitive strategies—a finding consistent with those of previous studies (Edmonds et al., [<reflink idref="bib29" id="ref130">29</reflink>]; Scammacca et al., [[<reflink idref="bib65" id="ref131">65</reflink>]]). Many of the multicomponent studies were conducted by the same group of authors (Vaughn et al.); thus, interventions tested likely were more similar in multicomponent studies than in studies of (meta)cognitive interventions. Additionally, as noted previously, studies of multicomponent interventions were more likely to meet rigorous indicators for study quality and employ standardized measures than studies of (meta)cognitive strategies. Thus, there may have been fewer confounding variables present in the multicomponent studies compared to (meta)cognitive studies. Clearly, we need more studies where implementation conditions and intervention characteristics are similar to better understand the efficacy of (meta)cognitive strategies compared to multicomponent strategies.</p> <hd id="AN0164007724-42">Text Content</hd> <p>Although ELA and social studies texts were not significant moderators of intervention effects, interventions implemented using these texts produced treatment effects that were higher than effects for multiple subjects. Interventions employed in ELA yielded the largest mean ES, and most interventions used in these studies incorporated (meta)cognitive strategies. Yet, high between‐study heterogeneity was observed, suggesting that other variables were influencing outcomes. For social studies, for example, there was a similarly large effect and high heterogeneity in the ESs. Interesting, all but one of the studies that incorporated social studies text employed strategies that included or exclusively focused on summarization, and all but three were of lower quality according to our criteria. Further, all studies that involved texts across multiple subjects employed multicomponent, and we found low heterogeneity in the ESs between those studies. Likely, there are interactions between strategy type, content area, and study conditions that need to be investigated in future research.</p> <hd id="AN0164007724-43">Text Structure</hd> <p>Although text structure was not a significant moderator, subset analyses showed that interventions using expository texts and both expository and narrative texts were associated with significant, positive effects. Interventions that used expository texts had the largest effect on students' comprehension, but there was considerable heterogeneity among ESs. Interventions that used both expository and narrative texts had smaller effects on intervention efficacy, and there was low heterogeneity among ESs. It is worth noting that multicomponent intervention studies were more likely to employ both expository and narrative texts (10 out of 15 studies), and nearly all of these studies (8 out of 10 studies) employed standardized dependent measures and were conducted by Vaughn et al. Perhaps intervention type and the conditions under which the interventions were employed contributed more to the homogeneous and small effects found for expository and narrative texts.</p> <hd id="AN0164007724-44">Limitations</hd> <p>Our results show a significant overall effect for reading comprehension interventions for adolescents with disabilities and other students with RDs and are mostly consistent with findings from the two most recent meta‐analyses (Filderman et al., [<reflink idref="bib31" id="ref132">31</reflink>]; Scammacca et al., [<reflink idref="bib66" id="ref133">66</reflink>]). There are, however, three limitations that should be considered.</p> <p>First, although we included more studies than in previous reviews, we could not test for moderating effects of (meta)cognitive strategy type (e.g., inferencing, summarization) or for other strategies, such as peer‐mediated instruction, due to insufficient statistical power. Given the heterogeneity present in ESs and the large mean ES for the (meta)cognitive strategies, this seems like an important limitation of our study.</p> <p>Second, we could not test for interactions among moderators (e.g., interactions between a reading comprehension strategy type and text structure) because of small sample sizes. Thus, sample limitations do not allow us to thoroughly explore the ideal combinations of intervention characteristics (e.g., inference‐making strategy and narrative texts; Cain et al., [<reflink idref="bib19" id="ref134">19</reflink>]) that could yield the largest treatment effects for students with RDs.</p> <p>Lastly, although our findings suggest no empirical evidence of publication bias, incorporating gray literature is still highly recommended to extend the scope of research evidence and reduce effects of potential publication bias (Adams et al., [<reflink idref="bib2" id="ref135">2</reflink>]). Future meta‐analyses that include gray literature, such as unpublished papers, dissertations, or government reports, could contribute to securing more reliable and generalizable estimates for robust moderator analyses.</p> <hd id="AN0164007724-45">CONCLUSIONS AND IMPLICATIONS</hd> <p>Our meta‐analytic study was the first published in over a decade to focus exclusively on secondary students (e.g., Edmonds et al., [<reflink idref="bib29" id="ref136">29</reflink>]), and unlike Edmonds et al., our study employed robust meta‐analytic techniques. Given that content‐area texts pose increasing challenges for students with RDs as they enter the secondary grades, it is important to understand the specific effect interventions have on these students' comprehension and the conditions under which those interventions are effective.</p> <p>Like previous meta‐analytic studies, findings from our study demonstrate that secondary students with RDs can benefit from (meta)cognitive and multicomponent interventions designed to improve their reading comprehension. Our study was also the first to show that text content (i.e., ELA, social studies, science, multiple subjects) was a moderator, and findings suggest that reading interventions were more effective when ELA and social studies texts were used. Additionally, we found that multicomponent interventions were not only effective but similar in effectiveness. Subgroup analyses showed small heterogeneity among ES for these interventions. These findings suggest that multicomponent strategies should be implemented to support the learning of students with RDs.</p> <p>In contrast, large variations in ESs for (meta)cognitive interventions make it difficult to draw conclusions for practice. Since instruction at the secondary level is organized according to specific content areas, we need more high‐quality studies of how certain types of interventions can be used with different text content and text structure to improve reading comprehension for students with RDs. For example, we need to better understand if summarization strategies are more effective for social studies and expository texts than inferencing strategies, or if both are equally effective. This additional research is particularly important given the heterogeneity of ESs in our study and the fact that Filderman et al. ([<reflink idref="bib31" id="ref137">31</reflink>]) found differential effects for individual types of (meta)cognitive strategies for elementary and secondary students.</p> <p>We also need to better understand the efficacy of intervention type for subgroups of students experiencing difficulties with reading comprehension. In our study, students formally identified as having reading disabilities outperformed students with other reading difficulties; however, this moderating effect could have been the result of other differences in intervention characteristics and conditions.</p> <p>Finally, to improve teachers' use of these strategies, we need more research examining the conditions that effectively support strategy implementation. Like findings from other meta‐analytic studies (e.g., Scammacca et al., [<reflink idref="bib66" id="ref138">66</reflink>]), we found that instructional agent was a moderator of intervention effectiveness—studies implemented by researchers had stronger effects than those implemented by teachers. We need more research that improves our field's understanding of the conditions that support intervention efficacy. As an example, Vaughn, Roberts, Swanson, et al. ([<reflink idref="bib83" id="ref139">83</reflink>]) explored the moderating effects of initial teaching quality, as determined by implementation fidelity, on efficacy of their comprehension intervention. Additionally, we need to understand how other factors, such as professional development, time to plan for intervention use, and curriculum structure support teachers' effective use of (meta)cognitive and multicomponent strategies.</p> <p>Although aspects of our findings are inconclusive, findings from our study and previous meta‐analytic studies do provide direction for practitioners. Specifically, the small and homogeneous effects for multicomponent strategies suggest that teachers should be trained to implement these strategies and provided with the supports to do so. Teachers should also be trained to implement (meta)cognitive strategies, but with some important caveats. Teacher educators and professional development providers should consider selecting those interventions with the largest ES in our meta‐analytic study and Filderman et al.'s ([<reflink idref="bib31" id="ref140">31</reflink>]) as the focus of teacher preparation and professional development. Moreover, training in these interventions should help teachers determine how to support students' reading comprehension of specific content and text structure. Finally, teachers should be taught to assess the efficacy of any intervention they are implementing, as students with RDs do not benefit equally from certain interventions.</p> <ref id="AN0164007724-46"> <title> References </title> <blist> <bibl id="bib1" type="bt">1</bibl> <bibtext> *Studies included in the meta‐analysis.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref63" type="bt">2</bibl> <bibtext> Adams, R. J., Smart, P., & Huff, A. S. (2017). Shades of grey: Guidelines for working with the grey literature in systematic reviews for management and organizational studies. International Journal of Management Reviews, 19 (4), 432 – 454. https://doi.org/10.1111/ijmr.12102</bibtext> </blist> <blist> <bibl id="bib3" type="bt">3</bibl> <bibtext> * Asaro‐Saddler, K., Muir‐Knox, H., & Meredith, H. (2018). The effects of a summary writing strategy on the literacy skills of adolescents with disabilities. Exceptionality, 26 (2), 106 – 118. https://doi.org/10.1080/09362835.2017.1283626</bibtext> </blist> <blist> <bibl id="bib4" idref="ref8" type="bt">4</bibl> <bibtext> Austin, C. R., Vaughn, S., Clemens, N. H., Pustejovsky, J. E., & Boucher, A. N. (2022). The relative effects of instruction linking word reading and word meaning compared to word reading instruction alone on the accuracy, fluency, and word meaning knowledge of 4th‐5th grade students with dyslexia. Scientific Studies of Reading, 26 (3), 1 – 19. https://doi.org/10.1080/10888438.2021.1947294</bibtext> </blist> <blist> <bibl id="bib5" type="bt">5</bibl> <bibtext> * Bakken, J. P., Mastropieri, M. A., & Scruggs, T. E. (1997). Reading comprehension of expository science material and students with learning disabilities: A comparison of strategies. The Journal of Special Education, 31 (3), 300 – 324. https://doi.org/10.1177/002246699703100302</bibtext> </blist> <blist> <bibl id="bib6" idref="ref90" type="bt">6</bibl> <bibtext> Banks, G. C., Kepes, S., & Banks, K. P. (2012). Publication bias: The antagonist of meta‐analytic reviews and effective policymaking. Educational Evaluation and Policy Analysis, 34 (3), 259 – 277. https://doi.org/10.3102/0162373712446144</bibtext> </blist> <blist> <bibl id="bib7" type="bt">7</bibl> <bibtext> * Barth, A. E., & Elleman, A. (2017). Evaluating the impact of a multistrategy inference intervention for middle‐grade struggling readers. Language, Speech, and Hearing Services in Schools, 48 (1), 31 – 41. https://doi.org/10.1044/2016_LSHSS‐16‐0041</bibtext> </blist> <blist> <bibl id="bib8" type="bt">8</bibl> <bibtext> * Barth, A. E., Vaughn, S., Capin, P., Cho, E., Stillman‐Spisak, S., Martinez, L., & Kincaid, H. (2016). Effects of a text‐processing comprehension intervention on struggling middle school readers. Topics in Language Disorders, 36 (4), 368 – 389. https://doi.org/10.1097/TLD.0000000000000101</bibtext> </blist> <blist> <bibl id="bib9" idref="ref78" type="bt">9</bibl> <bibtext> * Berkeley, S., Mastropieri, M. A., & Scruggs, T. E. (2011). Reading comprehension strategy instruction and attribution retraining for secondary students with learning and other mild disabilities. Journal of Learning Disabilities, 44 (1), 18 – 32. https://doi.org/10.1177/0022219410371677</bibtext> </blist> <blist> <bibtext> * Berkeley, S., & Riccomini, P. J. (2011). QRAC‐the‐code: A comprehension monitoring strategy for middle school social studies textbooks. Journal of Learning Disabilities, 46 (2), 154 – 165. https://doi.org/10.1177/0022219411409412</bibtext> </blist> <blist> <bibtext> Berkeley, S., Scruggs, T. E., & Mastropieri, M. A. (2010). Reading comprehension instruction for students with learning disabilities, 1995–2006: A meta‐analysis. Remedial and Special Education, 31 (6), 423 – 436. https://doi.org/10.1177/0741932509355988</bibtext> </blist> <blist> <bibtext> Bloom, H. S., Hill, C. J., Black, A. R., & Lipsey, M. W. (2008). Performance trajectories and performance gaps as achievement effect‐size benchmarks for educational interventions. Journal of Research on Educational Effectiveness, 1 (4), 289 – 328. https://doi.org/10.1080/19345740802400072</bibtext> </blist> <blist> <bibtext> Borenstein, M. (2022). In a meta‐analysis, the I‐squared statistic does not tell us how much the effect size varies. Journal of Clinical Epidemiology. Advance online publication. https://doi.org/10.1016/j.jclinepi.2022.10.003</bibtext> </blist> <blist> <bibtext> Borenstein, M. (2019). Common mistakes in meta‐analysis and how to avoid them. Biostat.</bibtext> </blist> <blist> <bibtext> Borenstein, M., Hedges, L. V., Higgins, J. P., & Rothstein, H. R. (2009). Introduction to meta‐analysis. John Wiley & Sons.</bibtext> </blist> <blist> <bibtext> * Bos, C. S., & Anders, P. L. (1990). Effects of interactive vocabulary instruction on the vocabulary learning and reading comprehension of junior‐high learning disabled students. Learning Disability Quarterly, 13 (1), 31 – 42. https://doi.org/10.2307/1510390</bibtext> </blist> <blist> <bibtext> * Bryant, D. P., Vaughn, S., Linan‐Thompson, S., Ugel, N., Hamff, A., & Hougen, M. (2000). Reading outcomes for students with and without reading disabilities in general education middle‐school content area classes. Learning Disability Quarterly, 23 (4), 238 – 252. https://doi.org/10.2307/1511347</bibtext> </blist> <blist> <bibtext> Budin, S., Patti, A. L., & Rafferty, L. A. (2019). Teaching cognitive and metacognitive strategies to support learning and independence. In J. McLeskey, L. Maheady, B. Billingsley, M. T. Brownell, & T. J. Lewis (Eds.), High leverage practices for inclusive classrooms (2nd edn., pp. 201 – 217). Routledge.</bibtext> </blist> <blist> <bibtext> Cain, K., Oakhill, J., & Bryant, P. (2004). Children's reading comprehension ability: Concurrent prediction by working memory, verbal ability, and component skills. Journal of Educational Psychology, 96 (1), 31 – 42. https://doi.org/10.1037/0022‐0663.96.1.31</bibtext> </blist> <blist> <bibtext> * Calhoon, M. B. (2005). Effects of a peer‐mediated phonological skill and reading comprehension program on reading skill acquisition for middle school students with reading disabilities. Journal of Learning Disabilities, 38 (5), 424 – 433. https://doi.org/10.1177/00222194050380050501</bibtext> </blist> <blist> <bibtext> Catts, H. W. (2022). Rethinking how to promote reading comprehension. American Educator, 45 (4), 26.</bibtext> </blist> <blist> <bibtext> Chard, D. J., Ketterlin‐Geller, L., Baker, S. K., Doabler, C., & Apichatabutra, C. (2009). Repeated reading interventions for students with learning disabilities: Status of the evidence. Exceptional Children, 75 (3), 263 – 281. https://doi.org/10.1177/001440290907500301</bibtext> </blist> <blist> <bibtext> Compton, D. L., Miller, A. C., Elleman, A. M., & Steacy, L. M. (2014). Have we forsaken reading theory in the name of "quick fix" interventions for children with reading disability? Scientific Studies of Reading, 18 (1), 55 – 73. https://doi.org/10.1080/10888438.2013.836200</bibtext> </blist> <blist> <bibtext> Cook, B., Buyssee, V., Klinger, J., Landrum, T. J., McWilliam, R. A., Tankersley, M., & Test, D. (2015). CEC's standards for classifying the evidence base of practices in special education. Remedial and Special Education, 36 (4), 220 – 234. https://doi.org/10.1177/0741932514557271</bibtext> </blist> <blist> <bibtext> * Darch, C., & Gersten, R. (1986). Direction‐setting activities in reading comprehension: A comparison of two approaches. Learning Disability Quarterly, 9 (3), 235 – 243. https://doi.org/10.2307/1510469</bibtext> </blist> <blist> <bibtext> * Denton, C. A., York, M. J., Francis, D. J., Haring, C., Ahmed, Y., & Bidulescu, A. (2017). An investigation of an intervention to promote inference generation by adolescent poor comprehenders. Learning Disabilities Research & Practice, 32 (2), 85 – 98. https://doi.org/10.1111/ldrp.12134</bibtext> </blist> <blist> <bibtext> * Diliberto, J. A., Beattie, J. R., Flowers, C. P., & Algozzine, R. F. (2008). Effects of teaching syllable skills instruction on reading achievement in struggling middle school readers. Literacy Research and Instruction, 48 (1), 14 – 27. https://doi.org/10.1080/19388070802226253</bibtext> </blist> <blist> <bibtext> Duval, S., & Tweedie, R. (2000). A nonparametric "Trim and Fill" method of accounting for publication bias in meta‐analysis. Journal of American Statistical Association, 95 (449), 89 – 98. https://doi.org/10.1080/01621459.2000.10473905</bibtext> </blist> <blist> <bibtext> Edmonds, M. S., Vaughn, S., Wexler, J., Reutebuch, C., Cable, A., Tackett, K. K., & Schnakenberg, J. W. (2009). A synthesis of reading interventions and effects on reading comprehension outcomes for older struggling readers. Review of Educational Research, 79 (1), 262 – 300. https://doi.org/10.3102/0034654308325998</bibtext> </blist> <blist> <bibtext> * Faggella‐Luby, M., Schumaker, J. S., & Deshler, D. D. (2007). Embedded learning strategy instruction: Story‐structure pedagogy in heterogeneous secondary literature classes. Learning Disability Quarterly, 30 (2), 131 – 147. https://doi.org/10.2307/30035547</bibtext> </blist> <blist> <bibtext> Filderman, M. J., Austin, C. R., Boucher, A. N., O'Donnell, K., & Swanson, E. A. (2022). A meta‐analysis of the effects of reading comprehension interventions on the reading comprehension outcomes of struggling readers in third through 12th grades. Exceptional Children, 88 (2), 163 – 184. https://doi.org/10.1177/00144029211050860</bibtext> </blist> <blist> <bibtext> Fisher, Z., & Tipton, E. (2015). robumeta: An R‐package for robust variance estimation in meta‐analysis. https://arxiv.org/abs/1503.02220</bibtext> </blist> <blist> <bibtext> Flynn, L. J., Zheng, X., & Swanson, H. L. (2012). Instructing struggling older readers: A selective meta‐analysis of intervention research. Learning Disabilities Research & Practice, 27 (1), 21 – 32. https://doi.org/10.1111/j.1540‐5826.2011.00347.x</bibtext> </blist> <blist> <bibtext> Gajria, M., Jitendra, A. K., Sood, S., & Sacks, G. (2007). Improving comprehension of expository text in students with LD: A research synthesis. Journal of Learning Disabilities, 40 (3), 210 – 225. https://doi.org/10.1177/00222194070400030301</bibtext> </blist> <blist> <bibtext> * Gajria, M., & Salvia, J. (1992). The effects of summarization instruction on text comprehension of students with learning disabilities. Exceptional Children, 58 (6), 508 – 516. https://doi.org/10.1177/001440299205800605</bibtext> </blist> <blist> <bibtext> Gersten, R., Fuchs, L. S., Compton, D., Coyne, M., Greenwood, C., & Innocenti, M. S. (2005). Quality indicators for group experimental and quasi‐experimental research in special education. Exceptional Children, 71 (2), 149 – 164. https://doi.org/10.1177/001440290507100202</bibtext> </blist> <blist> <bibtext> Gersten, R., Fuchs, L. S., Williams, J. P., & Baker, S. (2001). Teaching reading comprehension strategies to students with learning disabilities: A review of research. Review of Educational Research, 71 (2), 279 – 320. https://doi.org/10.3102/00346543071002279</bibtext> </blist> <blist> <bibtext> Goodwin, A. P., & Ahn, S. (2013). A meta‐analysis of morphological interventions in English: Effects on literacy outcomes for school‐age children. Scientific Studies of Reading, 17 (4), 257 – 285. https://doi.org/10.1080/10888438.2012.689791</bibtext> </blist> <blist> <bibtext> Hedges, L. V., Tipton, E., & Johnson, M. C. (2010). Robust variance estimation in meta‐regression with dependent effect size estimates. Research Synthesis Methods, 1 (1), 39 – 65. https://doi.org/10.1002/jrsm.5</bibtext> </blist> <blist> <bibtext> Hein, V., Smerdon, B., & Sambolt, M. (2013). Predictors of postsecondary success. American Institutes for Research, College and Career Readiness and Success Center. https://ccrscenter.org/sites/default/files/CCRS%20Center_Predictors%20of%20Postsecondary%20Success_final_0.pdf</bibtext> </blist> <blist> <bibtext> Herrera, S., Truckenmiller, A. J., & Foorman, B. R. (2016). Summary of 20 years of research on the effectiveness of adolescent literacy programs and practices (REL 2016–178). U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance, Regional Educational Laboratory Southeast. Retrieved from <ulink href="http://ies.ed.gov/ncee/edlabs">http://ies.ed.gov/ncee/edlabs</ulink></bibtext> </blist> <blist> <bibtext> * Jitendra, A. K., Kay Hoppes, M., & Xin, Y. P. (2000). Enhancing main idea comprehension for students with learning problems: The role of a summarization strategy and self‐monitoring instruction. The Journal of Special Education, 34 (3), 127 – 139. https://doi.org/10.1177/002246690003400302</bibtext> </blist> <blist> <bibtext> * Katims, D. S., & Harris, S. (1997). Improving the reading comprehension of middle school students in inclusive classrooms. Journal of Adolescent & Adult Literacy, 41 (2), 116 – 123.</bibtext> </blist> <blist> <bibtext> * Kent, S., Wanzek, J., Swanson, E. A., & Vaughn, S. (2015). Team‐based learning for students with high‐incidence disabilities in high school social studies classrooms. Learning Disabilities Research & Practice, 30 (1), 3 – 14. https://doi.org/10.1111/ldrp.12048</bibtext> </blist> <blist> <bibtext> * Kim, A. H., Vaughn, S., Klingner, J. K., Woodruff, A. L., Klein Reutebuch, C., & Kouzekanani, K. (2006). Improving the reading comprehension of middle school students with disabilities through computer‐assisted collaborative strategic reading. Remedial and Special Education, 27 (4), 235 – 249. https://doi.org/10.1177/07419325060270040401</bibtext> </blist> <blist> <bibtext> * Klingner, J. K., & Vaughn, S. (1996). Reciprocal teaching of reading comprehension strategies for students with learning disabilities who use English as a second language. The Elementary School Journal, 96 (3), 275 – 293.</bibtext> </blist> <blist> <bibtext> Lipsey, M. W. (2019). Identifying potentially interesting variables and analysis opportunities. In H. Cooper, L. V. Hedges, & J. C. Valentine (Eds.), The handbook of research synthesis and meta‐analysis (3rd edn., pp. 141 – 152). Russell Sage Foundation.</bibtext> </blist> <blist> <bibtext> * Lovett, M. W., Lacerenza, L., De Palma, M., & Frijters, J. C. (2012). Evaluating the efficacy of remediation for struggling readers in high school. Journal of Learning Disabilities, 45 (2), 151 – 169. https://doi.org/10.1177/0022219410371678</bibtext> </blist> <blist> <bibtext> * Malone, L. D., & Mastropieri, M. A. (1991). Reading comprehension instruction: Summarization and self‐monitoring training for students with learning disabilities. Exceptional Children, 58 (3), 270 – 279. https://doi.org/10.1177/001440299105800309</bibtext> </blist> <blist> <bibtext> Mastropieri, M. A., & Scruggs, T. E. (1997). Best practices in promoting reading comprehension in students with learning disabilities 1976 to 1996. Remedial and Special Education, 18 (4), 198 – 213. https://doi.org/10.1177/074193259701800402</bibtext> </blist> <blist> <bibtext> McEwan, D., Ruissen, G. R., Eys, M. A., Zumbo, B. D., & Beauchamp, M. R. (2017). The effectiveness of teamwork training on teamwork behaviors and team performance: A systematic review and meta‐analysis of controlled interventions. PLoS One, 12 (1), 1 – 23. https://doi.org/10.1371/journal.pone.0169604</bibtext> </blist> <blist> <bibtext> Meyer, B. J., Wijekumar, K., Middlemiss, W., Higley, K., Lei, P. W., Meier, C., & Spielvogel, J. (2010). Web‐based tutoring of the structure strategy with or without elaborated feedback or choice for fifth‐and seventh‐grade readers. Reading Research Quarterly, 45 (1), 62 – 92. https://doi.org/10.1598/RRQ.45.1.4</bibtext> </blist> <blist> <bibtext> Moats, L. C., & Tolman, C. A. (2019). LETRS (Volume 1). Lexia.</bibtext> </blist> <blist> <bibtext> Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta‐analyses: The PRISMA statement. BMJ. https://doi.org/10.1136/bmj.b2535</bibtext> </blist> <blist> <bibtext> Morris, S. B. (2008). Estimating effect sizes from pretest‐posttest‐control group designs. Organizational Research Methods, 11 (2), 364 – 386. https://doi.org/10.1177/1094428106291059</bibtext> </blist> <blist> <bibtext> Myers, J. A., Witzel, B. S., Powell, S. R., Li, H., Pigott, T. D., Xin, Y. P., & Hughes, E. M. (2022). A meta‐analysis of mathematics word‐problem solving interventions for elementary students who evidence mathematics difficulties. Review of Educational Research, 92 (5). https://doi.org/10.3102/00346543211070049</bibtext> </blist> <blist> <bibtext> National Center for Education Statistics. (2022). NAEP report card: 2022 NAEP reading assessment highlighted results at Grades 4 and 8 for the nation, states, and districts. U.S. Department of Education. https://<ulink href="http://www.nationsreportcard.gov/highlights/reading/2022/">www.nationsreportcard.gov/highlights/reading/2022/</ulink></bibtext> </blist> <blist> <bibtext> * O'Connor, R. E., Sanchez, V., Beach, K. D., & Bocian, K. M. (2017). Special education teachers integrating reading with eighth grade US history content. Learning Disabilities Research & Practice, 32 (2), 99 – 111. https://doi.org/10.1111/ldrp.12131</bibtext> </blist> <blist> <bibtext> O'Reilly, T., Wang, Z., & Sabatini, J. (2019). How much knowledge is too little? When a lack of knowledge becomes a barrier to comprehension. Psychological Science, 30 (9), 1344 – 1351. https://doi.org/10.1177/0956797619862276</bibtext> </blist> <blist> <bibtext> Orwin, R. G. (1983). A fail‐safe N for effect size in meta‐analysis. Journal of Educational Statistics, 8 (2), 157 – 159. https://doi.org/10.3102/10769986008002157</bibtext> </blist> <blist> <bibtext> R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R‐project.org/</bibtext> </blist> <blist> <bibtext> * Reed, D. K., & Lynn, D. (2016). The effects of an inference‐making strategy taught with and without goal setting. Learning Disability Quarterly, 39 (3), 133 – 145. https://doi.org/10.1177/0731948715615557</bibtext> </blist> <blist> <bibtext> * Reynolds, D. (2021). Scaffolding the academic language of complex text: An intervention for late secondary students. Journal of Research in Reading, 44 (3), 508 – 528. https://doi.org/10.1111/1467‐9817.12353</bibtext> </blist> <blist> <bibtext> Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86 (3), 638 – 641. https://doi.org/10.1037/0033‐2909.86.3.638</bibtext> </blist> <blist> <bibtext> Scammacca, N., Roberts, G., Vaughn, S., Edmonds, M., Wexler, J., Reutebuch, C. K., & Torgesen, J. K. (2007). Interventions for adolescent struggling readers: A meta‐analysis with implications for practice. RMC Research Corporation, Center on Instruction.</bibtext> </blist> <blist> <bibtext> Scammacca, N. K., Roberts, G., Vaughn, S., & Stuebing, K. K. (2015). A meta‐analysis of interventions for struggling readers in Grades 4–12: 1980–2011. Journal of Learning Disabilities, 48 (4), 369 – 390. https://doi.org/10.1177/0022219413504995</bibtext> </blist> <blist> <bibtext> Sejnost, R. L., & Thiese, S. M. (2010). Building content literacy: Strategies for the adolescent learner. Corwin Press.</bibtext> </blist> <blist> <bibtext> Shanahan, T., & Shanahan, C. (2012). What is disciplinary literacy and why does it matter? Topics in Language Disorders, 32 (1), 7 – 18. https://doi.org/10.1097/TLD.0b013e318244557a</bibtext> </blist> <blist> <bibtext> Shelton, A., Lemons, C. J., & Wexler, J. (2021). Supporting main idea identification and text summarization in middle school co‐taught classes. Intervention in School and Clinic, 56 (4), 217 – 223. https://doi.org/10.1177/1053451220944380</bibtext> </blist> <blist> <bibtext> Smith, R., Snow, P., Serry, T., & Hammond, L. (2021). The role of background knowledge in reading comprehension: A critical review. Reading Psychology, 42 (3), 214 – 240. https://doi.org/10.1080/02702711.2021.1888348</bibtext> </blist> <blist> <bibtext> Solis, M., Ciullo, S., Vaughn, S., Pyle, N., Hassaram, B., & Leroux, A. (2012). Reading comprehension interventions for middle school students with learning disabilities: A synthesis of 30 years of research. Journal of Learning Disabilities, 45 (4), 327 – 340. https://doi.org/10.1177/0022219411402691</bibtext> </blist> <blist> <bibtext> * Solís, M., Vaughn, S., & Scammacca, N. (2015). The effects of an intensive reading intervention for ninth graders with very low reading comprehension. Learning Disabilities Research & Practice, 30 (3), 104 – 113. https://doi.org/10.1111/ldrp.12061</bibtext> </blist> <blist> <bibtext> * Solis, M., Vaughn, S., Stillman‐Spisak, S. J., & Cho, E. (2018). Effects of reading comprehension and vocabulary intervention on comprehension‐related outcomes for ninth graders with low reading comprehension. Reading & Writing Quarterly, 34 (6), 537 – 553. https://doi.org/10.1080/10573569.2018.1499059</bibtext> </blist> <blist> <bibtext> Stevens, E. A., Walker, M. A., & Vaughn, S. (2017). The effects of reading fluency interventions on the reading fluency and reading comprehension performance of elementary students with learning disabilities: A synthesis of the research from 2001 to 2014. Journal of Learning Disabilities, 50 (5), 576 – 590. https://doi.org/10.1177/0022219416638028</bibtext> </blist> <blist> <bibtext> Swanson, E., Stevens, E. A., Scammacca, N. K., Capin, P., Stewart, A. A., & Austin, C. R. (2017). The impact of tier 1 reading instruction on reading outcomes for students in Grades 4–12: A meta‐analysis. Reading and Writing, 30 (8), 1639 – 1665. https://doi.org/10.1007/s11145‐017‐9743‐3</bibtext> </blist> <blist> <bibtext> * Swanson, E., Wanzek, J., Vaughn, S., Fall, A. M., Roberts, G., Hall, C., & Miller, V. L. (2017). Middle school reading comprehension and content learning intervention for below‐average readers. Reading & Writing Quarterly, 33 (1), 37 – 53. https://doi.org/10.1080/10573569.2015.1072068</bibtext> </blist> <blist> <bibtext> Tanner‐Smith, E. E., & Tipton, E. (2014). Robust variance estimation with dependent effect sizes: Practical considerations including a software tutorial in Stata and SPSS. Research Synthesis Methods, 5 (1), 13 – 30. https://doi.org/10.1002/jrsm.1091</bibtext> </blist> <blist> <bibtext> Tarvainen, S., Stolt, S., & Launonen, K. (2020). Oral language comprehension interventions in 1–8‐year‐old children with language disorders or difficulties: A systematic scoping review. Autism & Developmental Language Impairments, 5, 1 – 24. https://doi.org/10.1177/2396941520946999</bibtext> </blist> <blist> <bibtext> * Twyman, T., & Tindal, G. (2006). Using a computer‐adapted, conceptually based history text to increase comprehension and problem‐solving skills of students with disabilities. Journal of Special Education Technology, 21 (2), 5 – 16. https://doi.org/10.1177/016264340602100201</bibtext> </blist> <blist> <bibtext> Vaughn, S., & Fletcher, J. M. (2012). Response to intervention with secondary school students with reading difficulties. Journal of Learning Disabilities, 45 (3), 244 – 256. https://doi.org/10.1177/2F0022219412442157</bibtext> </blist> <blist> <bibtext> * Vaughn, S., Klingner, J. K., Swanson, E. A., Boardman, A. G., Roberts, G., Mohammed, S. S., & Stillman‐Spisak, S. J. (2011). Efficacy of collaborative strategic reading with middle school students. American Educational Research Journal, 48 (4), 938 – 964. https://doi.org/10.3102/0002831211410305</bibtext> </blist> <blist> <bibtext> * Vaughn, S., Roberts, G., Schnakenberg, J. B., Fall, A. M., Vaughn, M. G., & Wexler, J. (2015). Improving reading comprehension for high school students with disabilities: Effects for comprehension and school retention. Exceptional Children, 82 (1), 117 – 131. https://doi.org/10.1177/0014402915585478</bibtext> </blist> <blist> <bibtext> Vaughn, S., Roberts, G., Swanson, E. A., Wanzek, J., Fall, A. M., & Stillman‐Spisak, S. J. (2015). Improving middle‐school students' knowledge and comprehension in social studies: A replication. Educational Psychology Review, 27 (1), 31 – 50. https://doi.org/10.1007/s10648‐014‐9274‐2</bibtext> </blist> <blist> <bibtext> * Vaughn, S., Roberts, G., Wexler, J., Vaughn, M. G., Fall, A. M., & Schnakenberg, J. B. (2015). High school students with reading comprehension difficulties: Results of a randomized control trial of a two‐year reading intervention. Journal of Learning Disabilities, 48 (5), 546 – 558. https://doi.org/10.1177/0022219413515511</bibtext> </blist> <blist> <bibtext> * Vaughn, S., Wexler, J., Leroux, A., Roberts, G., Denton, C., Barth, A., & Fletcher, J. (2012). Effects of intensive reading intervention for eighth‐grade students with persistently inadequate response to intervention. Journal of Learning Disabilities, 45 (6), 515 – 525. https://doi.org/10.1177/0022219411402692</bibtext> </blist> <blist> <bibtext> * Vaughn, S., Wexler, J., Roberts, G., Barth, A. A., Cirino, P. T., Romain, M. A., Francis, D., Fletcher, J., & Denton, C. A. (2011). Effects of individualized and standardized interventions on middle school students with reading disabilities. Exceptional Children, 77 (4), 391 – 407. https://doi.org/10.1177/001440291107700401</bibtext> </blist> <blist> <bibtext> Vaughn, S., Wanzek, J., Wexler, J., Barth, A., Cirino, P. T., Fletcher, J., Romain, M., Denton, C. A., Roberts, G., & Francis, D. (2010). The relative effects of group size on reading progress of older students with reading difficulties. Reading and Writing, 23 (8), 931 – 956. https://doi.org/10.1007/s11145‐009‐9183‐9</bibtext> </blist> <blist> <bibtext> Vevea, J. L., Coburn, K., & Sutton, A. (2019). Publication bias. In H. Cooper, L. V. Hedges, & J. C. Valentine. (Eds.), The handbook of research synthesis and meta‐analysis (3rd ed., pp. 383 – 432). Russell Sage Foundation.</bibtext> </blist> <blist> <bibtext> Wanzek, J., Dickson, S., Bursuck, W. D., & White, J. M. (2000). Teaching phonological awareness to students at risk for reading failure: An analysis of four instructional programs. Learning Disabilities Research & Practice, 15 (4), 226 – 239.</bibtext> </blist> <blist> <bibtext> * Wanzek, J., Vaughn, S., Roberts, G., & Fletcher, J. M. (2011). Efficacy of a reading intervention for middle school students with learning disabilities. Exceptional Children, 78 (1), 73 – 87. https://doi.org/10.1177/001440291107800105</bibtext> </blist> <blist> <bibtext> Wanzek, J., Vaughn, S., Scammacca, N. K., Metz, K., Murray, C. S., Roberts, G., & Danielson, L. (2013). Extensive reading interventions for students with reading difficulties after grade 3. Review of Educational Research, 83 (2), 163 – 195. https://doi.org/10.3102/0034654313477212</bibtext> </blist> <blist> <bibtext> * Wexler, J., Vaughn, S., Roberts, G., & Denton, C. A. (2010). The efficacy of repeated reading and wide reading practice for high school students with severe reading disabilities. Learning Disabilities Research & Practice, 25 (1), 2 – 10. https://doi.org/10.1111/j.1540‐5826.2009.00296.x</bibtext> </blist> <blist> <bibtext> * Williams, K. J., & Vaughn, S. (2020). Effects of an intensive reading intervention for ninth‐grade English learners with learning disabilities. Learning Disability Quarterly, 43 (3), 154 – 166. https://doi.org/10.1177/2F0731948719851745</bibtext> </blist> <blist> <bibtext> * Wong, B. Y., & Jones, W. (1982). Increasing metacomprehension in learning disabled and normally achieving students through self‐questioning training. Learning Disability Quarterly, 5 (3), 228 – 240. https://doi.org/10.2307/1510290</bibtext> </blist> </ref> <aug> <p>By Hyojong Sohn; Kelly Acosta; Mary T. Brownell; Nicholas A. Gage; Eilish Tompson and Carolyn Pudvah</p> <p>Reported by Author; Author; Author; Author; Author; Author</p> <p></p> <p>Hyojong Sohn, MS, is a doctoral candidate in Special Education at the University of Florida. Her research interests include teacher effectiveness, evidence‐based literacy practices, and teacher collaboration within an MTSS framework.</p> <p>Kelly Acosta, PhD, is an assistant professor of special education at Rhode Island College in Providence, Rhode Island. Her research interests include teacher education, special education policy, and teacher shortages, with a focus in reading and using qualitative research designs. Prior to her work at Rhode Island College, Kelly was a special educator for over 13 years in a large, multicultural high school located in the Northeastern United States.</p> <p>Mary Brownell, PhD, is a Distinguished Professor of Special Education at the University of Florida and Director of the Collaboration for Effective Educator Development, Accountability and Reform (CEEDAR Center). Her research interests include teacher education, teacher assessment, and professional development, primarily in the area of reading for students with reading difficulties.</p> <p>Nicholas A. Gage, PhD, is a Senior Researcher in Special Education at WestEd. His research is focused on using quantitative data to identify critical relations between malleable factors and student‐, classroom‐, and school‐level outcomes. His expertise includes meta‐analysis, multilevel modeling, structural equation modeling, machine learning models, and econometrics.</p> <p>Eilish Thompson, MEd, is a law student at Western New England Law School in Springfield, Massachusetts. Her current research interests are related to education law and policy with a focus on policy implementation and discriminatory practices.</p> <p>Carolyn Pudvah, MEd, is a special educator with over 20 years of experience. She has worked in several districts across the North and Southeastern United States in a variety of secondary classroom settings, including self‐contained and cotaught. Her primary focus is supporting students with mild to moderate disabilities in the areas of reading, study skills, and postsecondary transition. She currently teaches high school online for a large multicultural high school located in the Northeastern United States.</p> </aug> <nolink nlid="nl1" bibid="bib80" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib57" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib40" firstref="ref4"></nolink> <nolink nlid="nl4" bibid="bib21" firstref="ref5"></nolink> <nolink nlid="nl5" bibid="bib38" firstref="ref6"></nolink> <nolink nlid="nl6" bibid="bib89" firstref="ref7"></nolink> <nolink nlid="nl7" bibid="bib23" firstref="ref9"></nolink> <nolink nlid="nl8" bibid="bib22" firstref="ref10"></nolink> <nolink nlid="nl9" bibid="bib74" firstref="ref11"></nolink> <nolink nlid="nl10" bibid="bib78" firstref="ref12"></nolink> <nolink nlid="nl11" bibid="bib59" firstref="ref13"></nolink> <nolink nlid="nl12" bibid="bib70" firstref="ref14"></nolink> <nolink nlid="nl13" bibid="bib19" firstref="ref15"></nolink> <nolink nlid="nl14" bibid="bib52" firstref="ref16"></nolink> <nolink nlid="nl15" bibid="bib62" firstref="ref17"></nolink> <nolink nlid="nl16" bibid="bib69" firstref="ref18"></nolink> <nolink nlid="nl17" bibid="bib86" firstref="ref19"></nolink> <nolink nlid="nl18" bibid="bib37" firstref="ref20"></nolink> <nolink nlid="nl19" bibid="bib18" firstref="ref22"></nolink> <nolink nlid="nl20" bibid="bib10" firstref="ref24"></nolink> <nolink nlid="nl21" bibid="bib53" firstref="ref26"></nolink> <nolink nlid="nl22" bibid="bib27" firstref="ref27"></nolink> <nolink nlid="nl23" bibid="bib11" firstref="ref28"></nolink> <nolink nlid="nl24" bibid="bib29" firstref="ref29"></nolink> <nolink nlid="nl25" bibid="bib31" firstref="ref30"></nolink> <nolink nlid="nl26" bibid="bib33" firstref="ref31"></nolink> <nolink nlid="nl27" bibid="bib65" firstref="ref32"></nolink> <nolink nlid="nl28" bibid="bib75" firstref="ref33"></nolink> <nolink nlid="nl29" bibid="bib91" firstref="ref34"></nolink> <nolink nlid="nl30" bibid="bib66" firstref="ref40"></nolink> <nolink nlid="nl31" bibid="bib12" firstref="ref45"></nolink> <nolink nlid="nl32" bibid="bib39" firstref="ref52"></nolink> <nolink nlid="nl33" bibid="bib77" firstref="ref53"></nolink> <nolink nlid="nl34" bibid="bib41" firstref="ref64"></nolink> <nolink nlid="nl35" bibid="bib42" firstref="ref65"></nolink> <nolink nlid="nl36" bibid="bib54" firstref="ref66"></nolink> <nolink nlid="nl37" bibid="bib50" firstref="ref70"></nolink> <nolink nlid="nl38" bibid="bib71" firstref="ref71"></nolink> <nolink nlid="nl39" bibid="bib47" firstref="ref73"></nolink> <nolink nlid="nl40" bibid="bib24" firstref="ref74"></nolink> <nolink nlid="nl41" bibid="bib15" firstref="ref75"></nolink> <nolink nlid="nl42" bibid="bib55" firstref="ref76"></nolink> <nolink nlid="nl43" bibid="bib32" firstref="ref82"></nolink> <nolink nlid="nl44" bibid="bib61" firstref="ref83"></nolink> <nolink nlid="nl45" bibid="bib14" firstref="ref87"></nolink> <nolink nlid="nl46" bibid="bib13" firstref="ref88"></nolink> <nolink nlid="nl47" bibid="bib28" firstref="ref91"></nolink> <nolink nlid="nl48" bibid="bib64" firstref="ref92"></nolink> <nolink nlid="nl49" bibid="bib60" firstref="ref93"></nolink> <nolink nlid="nl50" bibid="bib81" firstref="ref95"></nolink> <nolink nlid="nl51" bibid="bib51" firstref="ref99"></nolink> <nolink nlid="nl52" bibid="bib88" firstref="ref102"></nolink> <nolink nlid="nl53" bibid="bib56" firstref="ref112"></nolink> <nolink nlid="nl54" bibid="bib68" firstref="ref118"></nolink> <nolink nlid="nl55" bibid="bib36" firstref="ref127"></nolink> <nolink nlid="nl56" bibid="bib83" firstref="ref139"></nolink>
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: A Meta-Analysis of Interventions to Improve Reading Comprehension Outcomes for Adolescents with Reading Difficulties
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Sohn%2C+Hyojong%22">Sohn, Hyojong</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0300-2437">0000-0002-0300-2437</externalLink>)<br /><searchLink fieldCode="AR" term="%22Acosta%2C+Kelly%22">Acosta, Kelly</searchLink><br /><searchLink fieldCode="AR" term="%22Brownell%2C+Mary+T%2E%22">Brownell, Mary T.</searchLink><br /><searchLink fieldCode="AR" term="%22Gage%2C+Nicholas+A%2E%22">Gage, Nicholas A.</searchLink><br /><searchLink fieldCode="AR" term="%22Tompson%2C+Eilish%22">Tompson, Eilish</searchLink><br /><searchLink fieldCode="AR" term="%22Pudvah%2C+Carolyn%22">Pudvah, Carolyn</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Learning+Disabilities+Research+%26+Practice%22"><i>Learning Disabilities Research & Practice</i></searchLink>. May 2023 38(2):85-103.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 19
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2023
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Information Analyses
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Junior+High+Schools%22">Junior High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Middle+Schools%22">Middle Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Elementary+Education%22">Elementary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+6%22">Grade 6</searchLink><br /><searchLink fieldCode="EL" term="%22Intermediate+Grades%22">Intermediate Grades</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+7%22">Grade 7</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+8%22">Grade 8</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+9%22">Grade 9</searchLink><br /><searchLink fieldCode="EL" term="%22High+Schools%22">High Schools</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+10%22">Grade 10</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+11%22">Grade 11</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+12%22">Grade 12</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Middle+School+Students%22">Middle School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+6%22">Grade 6</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+7%22">Grade 7</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+8%22">Grade 8</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+9%22">Grade 9</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+10%22">Grade 10</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+11%22">Grade 11</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+12%22">Grade 12</searchLink><br /><searchLink fieldCode="DE" term="%22High+School+Students%22">High School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Reading+Difficulties%22">Reading Difficulties</searchLink><br /><searchLink fieldCode="DE" term="%22Intervention%22">Intervention</searchLink><br /><searchLink fieldCode="DE" term="%22Reading+Comprehension%22">Reading Comprehension</searchLink><br /><searchLink fieldCode="DE" term="%22Reading+Improvement%22">Reading Improvement</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Effectiveness%22">Instructional Effectiveness</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/ldrp.12307
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0938-8982<br />1540-5826
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This meta-analysis synthesized 97 effect sizes extracted from 37 intervention studies for students with reading difficulties (RDs) in Grades 6 to 12 published between 1982 and 2021 to identify the overall impact of reading interventions and the moderating effects of intervention characteristics and study design characteristics. Random-effects robust variance estimation (RVE) was used to account for dependencies within studies. Overall, interventions designed to improve reading comprehension outcomes for adolescents with RDs were effective (g = 0.63). Meta-regression analyses identified several significant moderators that were associated with intervention efficacy, such as text content, duration of intervention, agent of intervention, status of student, type of dependent measure, and study quality. We provide study limitations as well as implications for research and practice.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2023
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1379410
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1379410
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/ldrp.12307
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 19
        StartPage: 85
    Subjects:
      – SubjectFull: Middle School Students
        Type: general
      – SubjectFull: Grade 6
        Type: general
      – SubjectFull: Grade 7
        Type: general
      – SubjectFull: Grade 8
        Type: general
      – SubjectFull: Grade 9
        Type: general
      – SubjectFull: Grade 10
        Type: general
      – SubjectFull: Grade 11
        Type: general
      – SubjectFull: Grade 12
        Type: general
      – SubjectFull: High School Students
        Type: general
      – SubjectFull: Reading Difficulties
        Type: general
      – SubjectFull: Intervention
        Type: general
      – SubjectFull: Reading Comprehension
        Type: general
      – SubjectFull: Reading Improvement
        Type: general
      – SubjectFull: Instructional Effectiveness
        Type: general
    Titles:
      – TitleFull: A Meta-Analysis of Interventions to Improve Reading Comprehension Outcomes for Adolescents with Reading Difficulties
        Type: main
  BibRelationships:
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            NameFull: Sohn, Hyojong
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            NameFull: Acosta, Kelly
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            NameFull: Brownell, Mary T.
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            NameFull: Tompson, Eilish
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            NameFull: Pudvah, Carolyn
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          Dates:
            – D: 01
              M: 05
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 0938-8982
            – Type: issn-electronic
              Value: 1540-5826
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              Value: 38
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            – TitleFull: Learning Disabilities Research & Practice
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