A Meta-Analysis of Reading Interventions for Students with Emotional/Behavioral Disorders

A Meta-Analysis of Reading Interventions for Students with Emotional/Behavioral Disorders | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Meta-Analysis of Reading Interventions for Students with Emotional/Behavioral Disorders Argnue Chitiyo, Maria Sciuchetti, Holmes W Finch, Goodson Dzenga This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5362938/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Sep, 2025 Read the published version in Journal of Behavioral Education → Version 1 posted 7 You are reading this latest preprint version Abstract Students identified with Emotional/Behavioral Disorders (EBD) often exhibit behavioral challenges that negatively affect their academic performance. One of their weaker areas of academic achievement lies in reading. Although numerous research on interventions to improve their reading outcomes has been conducted, some students continue to lag behind. The use of instructional practices not supported by strong empirical evidence may deter students’ reading outcomes from improving. To address the reading performance gap, educators are encouraged to use evidence-based practices (EBP) supported by strong empirical research. Meta analyses play an important role in identifying and disseminating EBP. This meta-analysis evaluated reading intervention studies (n = 27) for students with emotional and behavioral disorders (EBD) using the Between Case Hedges’ g effect size. A random effects meta-analysis of 27 studies indicated that, overall, reading interventions increased reading outcomes of students with EBD by a large margin [ g = .747, SE = .102, CI (0.547, 0.947), p < .05]. A meta-regression analysis consisting of six moderators resulted in statistically significant effects of study design, intervention agent, and type of dependent variable. Implications of study findings and suggestions for future research are discussed. Meta-analysis emotional disorder behavioral disorder reading intervention reading outcome Figures Figure 1 Figure 2 Introduction Few would argue against the critical importance of reading proficiency for both academic achievement and postschool outcomes. Yet, according to data from the Program for the International Assessment of Adult Competencies (PIAAC), approximately half of US adults between the ages of 16 and 65 read below an 8th grade level. Additionally, 1 in 5 reads at Level 1 (i.e., simple, everyday literacy activities) or below Level 1 (i.e., the most simple and concrete literacy skills; Mamedova & Pawlowski, 2019 ), which is below a fourth-grade level. Among the population of incarcerated adults in the United States, more than 70% also read at or below a fourth-grade level (Greenberg et al., 2007 ), and 24% reported having attended special education classes at some point in their schooling (Maruschak et al., 2021 ). Students with disabilities are nearly 3 times as likely to be arrested than their peers without disabilities (Whitaker et al., 2019 ). Among incarcerated youth, studies suggest that anywhere from 20% to upwards of 80% have a disability (National Center on Criminal Justice and Disability, 2016), including learning disabilities and emotional disturbance (Mendoza et al., 2020 ; Taylor, 2016 ). Emotional disturbance, or emotional and behavioral disorders (EBD), is one of 13 disability categories outlined in the Individuals with Disabilities Act (IDEA; Public Law 94–142) that may qualify a student for special education services in the U.S. Youth with EBD characteristically engage in externalizing (e.g., aggression, defiance, property destruction, vindictiveness) and/or internalizing (e.g., anxiety, depression, social withdrawal) behaviors that negatively impact their educational performance. According to IDEA, the student to qualify for special education services, these behaviors must have been exhibited “over a long period of time and to a marked degree” (34 C.F.R. § 300.8(c)(4)(i)). Students identified with EBD often have difficulty maintaining attention during academic activities, problem-solving skills deficits, frequent school absenteeism, and difficulties following school rules and routines (Kremer et al., 2016 ). Therefore, it is unsurprising that youth with EBD are at increased risk for negative school outcomes including lower academic achievement, poor school attendance, and school dropout. Students with EBD are also more likely to engage in risky behaviors and experience dismal post-secondary outcomes when compared to youth with and without other disabilities (Gage et al., 2014; Wagner, 2014). For example, youth with EBD are at greater risk for substance abuse and incarceration within three years of leaving school (Gage et al., 2014). Academic and Reading Achievement of Students with Disabilities Compared to same-age peers, students with disabilities tend to experience more academic challenges and lower achievement across many domains. According to a National Longitudinal Transition Study-2 (NLTS2), the average grade level gap between tested reading and math ability and actual grade level was at least two years (Wagner et al., 2004). Further analysis revealed that 83% of youth with disabilities performed below the mean on reading comprehension while 50% of students without disabilities performed below the mean (Wagner et al., 2006). Perhaps even more disconcerting is that these data indicate 24% of youth with disabilities score more than two standard deviations below the mean on reading comprehension compared to only 2% of their peers without disabilities. These findings were also observed by Gilmour and colleagues (2019) who found students with disabilities showed a wider reading achievement gap of about 1.17 standard deviations lower than their nondisabled peers. When evaluating the academic performance of youth with emotional and behavioral disorders (EBD) specifically, NLTS2 data indicate these students performed better in science and applied math when compared to their reading comprehension performance, findings echoed in the literature. Students with EBD have been found to perform 1 to 2 years below grade level (Nelson et al., 2004 ). Nelson and colleagues ( 2004 ) reported that 83% of their study’s sample of children with EBD scored below students in the typical group on a standardized measure of reading skills. Mattison (2008) concluded these students demonstrate weakness in early literacy skills, specifically phonological awareness, and comprehension. Similarly, Reid and colleagues ( 2004 ) noted that students with EBD performed lower in math and reading when compared to other students with disabilities and displayed greater rates of school failure. Despite these well document academic deficits, intervention research for students with EBD continues to focus mostly on their social, behavioral, and adaptive skills (McKenna et al., 2022 ; Webby et al., 2003). Given the academic deficits and post-school outcomes for youth with EBD, it becomes imperative to identify academic interventions that target and improve the specific skills weaknesses characteristic of these students. Evidence based reading interventions for youth with EBD There is a growing body of literature on reading interventions for students with EBD. While the majority of studies focus on elementary level students (Rivera et al., 2006 ), studies with middle and high school students are increasing (Garwood, 2018 ). Researchers have targeted a range of literacy skills such as phonemic awareness (e.g., Lane et al., 2007 ), oral reading fluency (e.g., McDaniel et al., 2013), and comprehension (e.g., Garwood et al., 2014 ). In addition, studies have evaluated the efficacy of corrective reading (e.g., Lingo et al., 2006 ), peer assisted learning (e.g., Faulk & Webby, 2001; Fuchs et al., 2009), and repeated reading (e.g., Escarpio & Barbetta, 2016 ) strategies for these students. Findings from these studies suggest students with EBD can be responsive to and benefit from such interventions; yet they continue to lag behind their peers. As such, there has been an increased emphasis for educators to use evidence based instructional strategies, or evidence-based practices (EBP). EBPs are interventions that have been demonstrated to be effective through rigorous experimental research (Cook et al., 2008 ; Cook et al., 2009). Many accreditation boards in education have established standards outlining the procedures for identifying a practice as evidence based. According to the guidelines set forth by the Council for Exceptional Children (CEC), identifying EBPs may constitute four procedural criteria which include (1) studies are conducted using experimental designs demonstrating functional relationships between interventions and outcomes, (2) studies are of high quality, (3) each practice is supported by a larger quantity of studies, and (4) effects are synthetized across multiple studies. Regarding the use of experimental design, EBP approach requires researchers to use experimental designs that demonstrate strong correlations between interventions and students’ outcomes. In special education, these designs constitute group and single case designs (Horner et al., 2005 ). Regarding the quality of studies, EBP requires that methodologies address all potential threats to internal and external validity. The What Works Clearinghouse (WWC) and CEC have established with quality indicators to guide assessment of the methodological rigor of interventions’ research. The CEC quality indicators consist of 8 methodological domains, which are context and setting, participants, intervention agents, description of practice, implementation fidelity, internal validity, outcome measures/dependent variables , and data analysis (Cook et al., 2015 ). The dimension of quantity of research requires that a single intervention be supported by at least 2 experimental studies of high quality. The aim is that intervention effectiveness is demonstrated by replication of the study and findings across more participants in different settings. Finally, studies should demonstrate, in quantitative terms, the magnitude of effects of the interventions on outcomes of interest using standard effect sizes. Systematic reviews and meta-analyses are commonly used to synthesize effects of interventions across multiple studies (Maggin et al., 2011 ; Talbott et al., 2018 ). Role of Meta Analyses in Identifying EBP in reading Meta analyses have traditionally been used to quantitatively synthesize effects across multiple studies and to identify factors that moderate effectiveness of interventions when differences in effects cannot be limited to differences in individual characteristics. Meta-analyses therefore contribute to EBP by summarizing effects and showing interventions yielding the largest gains across multiple participants and settings (Maggin et al., 2011 ; McNamara & Scales, 2011 ). Several meta-analyses on reading interventions for EBD have been conducted. Benner and colleagues ( 2010 ) conducted a meta-analysis of studies published between 1970–2010 on the effects of reading instruction on reading skills for students with or at risk for EBD. They computed a Rosenthal’s correlation (1994) between baseline and treatment conditions for SCD and Hedge’s g effect sizes for Group designs. They reported a positive effect of the reading instruction on the reading outcomes of students with or at risk for EBD. Although this study shed some important information regarding the effectiveness of the interventions assessed, it did not conduct a meta regression to assess the role of potential moderators on the effect sizes. Secondly, the study did not aggregate effect sizes within studies, thereby not adequately accounting for the shared variance within individual studies. Garwood and colleagues ( 2014 ) reviewed instructional interventions targeting reading comprehension for middle and high school students outside of general education settings. They computed Improvement Rate Difference (IRD) effect sizes to calculate the effects. They reported moderate to large effects across studies. The study presents some notable limitations. Firstly, the use of IRD effect size method encourages the removal of overlapping data points in baseline and intervention phases from the analysis thereby eliminating numerous data that may show important variances within individual participants (Parker et al., 2011 ). Secondly, as a non-overlap method, IRD does not really show a change in the magnitude for the target outcomes. Instead, it shows the percentages of data points in intervention phases that do not overlap with data points in baseline phases but without really showing a magnitude of effect. Finally, this study included only single case designs, thereby limiting the pool of potential important group experimental studies in the analysis. Other studies also synthesized research on reading interventions for EDB (e.g., Dunn et al., 2017 ; Burke et al., 2015 ; McKenna et al., 2019 ), showing potential effectiveness of the interventions on reading outcomes in EBD. Dunn et al ( 2017 ) evaluated studies on peer mediated interventions (PMI) targeting academic achievement for students with EBD. They computed PND, SMD, and Tau U for the 24 studies, from which they reported moderate effects of PMI across all academic subject areas. The mean effect sizes for reading outcomes were lower than those for spellings, English, and social studies. Burke and colleagues ( 2015 ) computed Tau U effect sizes for the 11 studies assessing reading interventions for middle and secondary students with EBD. The study reported overall positive effects across the 11 studies. However, the limited studies included in the review make it difficult to generalize findings to all students with EBD. As shown in a majority of the previous meta-analyses for reading interventions in EBD, researchers mostly used non-overlap statistics like IRD and Tau U to calculate effect sizes. Although these methods provide important tools for quantitatively synthesizing findings across single case studies, most non-overlap methods do not enable comparison of effect across different study designs or studies whose outcomes are expressed in different metrics (e.g. percent intervals in which behavior occurred versus frequency count; Hedges et al., 2012; Shadish et al., 2015). As a result, most of these studies have included only SCD studies in their reviews, yet several studies in educational research have traditionally been conducted using group experimental designs. Combining group and SCDs in meta-analyses can create a larger pool of studies from which to meta-analyze results with greater precision. Since most research in special education is conducted using both group and SCDs, synthesizing effects across different study designs may enable researchers to draw more overarching conclusions pertaining effectiveness of interventions (Shadish et al., 2015). Most non-overlap methods commonly used in meta-analyzing SCD studies fail to adequately account for trend effects of the time series nature of SCDs data and data autocorrelation/serial dependency (Shadish et al., 2015). Autocorrelation occurs when a participant’s rate of responding on one observation day correlates to their responding on a previous observation session. A change in the behavior on the observation day may therefore be attributed to student’s exposure to certain, non-treatment related factors on the previous observation days. Such a situation violates the parametric assumption of independence of error terms (i.e. the idea that scores on one observation day are independent of influence from previous observation), and failure to account for it can result to biased parameters (Shadish, Rindskopf, Hedges, & Sullivan, 2013). Finally, most non-overlap methods calculate proportion of data points in the intervention phases that do not overlap with data points in the baseline phases. Thus, non-overlap methods do not show the magnitude of change in the behaviors of interest associated with the interventions (Parker et al., 2011 ). Of all non-overlap methods, Tau-U and ECL are the only two that control for baseline trend. Both methods however suffer the disadvantage of excessive sensitivity to changes in phase lengths (Parker et al., 2011 ). Between Case Effect Size In order to address some of the previously mentioned challenges associated with traditional SCD effect sizes, novel approaches for synthesizing effects across both group and SCDs have been developed (Shadish et al., 2015). Shadish and colleagues (2015) identified three between case effect sizes measures (BCES: Hedges, Pustejovsky, & Shadish, 2012; Pustejovsky et al., 2014; Swaminathan, Rogers, & Horner, 2014) that address most of the limitations stated earlier. All three methods enable researchers to synthesize results across different study designs. Effect sizes are based off standardized mean differences between baseline and treatment scores. They all assume normality of error terms about phase means, and treatment effects to be constant across cases. It has additional assumptions of linear time trend and heterogeneous coefficients of effects across cases (i.e. treatment effects are allowed to vary across cases). BCESs also address data trend effects associated with most non-overlap methods commonly used for SCDs. Hedges et al. (2012) provide procedures for testing data for trend effects and SPSS software options for detrending data prior to computing d statistics. All three BCESs also control for autocorrelation/serial dependency either by estimating autocorrelation as part of the procedures for computing effect sizes, or by using randomized effects procedures described by Gurka, Edwards, and Muller (2011). Furthermore, the methods assume normality of error terms about phase means within cases (Shadish et al., 2015). However, Shadish and colleagues (2014) note that non-normality may not be a big concern since a corrected g statistic is considered to be robust to violation of the normality assumption. That is, even if baseline and intervention data across participants are not normally distributed, there is a very insignificant effect on the calculated effect sizes. If BCESs are properly applied to meta-analyses of group and SCDs, they may result in more accurate effect sizes, thereby giving a better reflection of efficacy of interventions (Shadish et al., 2015). Furthermore, BCESs are most ideal for populations in which a large number of group and SCDs exist. One such population is students with emotional and behavioral disorders (EBD). This population represents an ideal population to examine efficacy of academic interventions because they respond to treatment differentially from other learning disability categories (Reid, Gonzalez, Nordness, Trout, & Epstein, 2004 ). Research also shows acute academic deficits for children with EBD relative to students without disabilities (Lane, Barton, Nelson, & Wehby, 2008; Reid et al., 2004 ). This study seeks to answer two research questions: 1) What is the effect of reading interventions on reading outcomes for students identified with EBD? 2) What factors moderate the effectiveness of reading interventions for students with EBD? Methods Literature Search In order to locate studies on reading interventions for EBD, we followed a three steps process consisting of database search, ancestral search, and hand-search. In the first step, we conducted an electronic databases search consisting of the following databases: PsycInfo, PsycArticles, CINAHL, ERIC, and EBSCO Management Collection. The search was limited to studies published from 2000 to present. The following search terms were entered into the search engine: (AB ("emotion* behavior* dis*" OR "ebd" OR "behavior* dis*" OR "emotion* dis*")) AND (AB ("read*" OR "litera*" OR "comprehen*" OR "phon*" OR "alphabet*" OR "fluen*" OR "vocabulary")). The electronic database search resulted in a total of 5737 returned abstracts, consisting of 4211 journal articles. The rest were dissertations (895), books (564), magazines (24), CEUs (13), trade publications (4), and newspaper articles (n = 4). After removing duplicates, 3848 abstracts remained for screening. The lead author and a graduate research assistant trained on the inclusion and exclusion criteria read the titles and abstracts of retrieved peer reviewed articles independently to determine the studies meeting the initial inclusion criteria. If criteria for inclusion/exclusion could not be ascertained from reading the title or abstract, a full text of the article was extracted. The two reviewers independently coded the titles/abstracts for inclusion and compared their findings upon completion. The two reviewers agreed on 98% of the titles/abstracts. Articles on which the reviewers differed were discussed and a final decision for inclusion/exclusion was made in consultation. A total of 21 articles met criteria for inclusion. All studies that did not meet the initial selection criteria were discarded. Next, an ancestral search was conducted. The primary author read through the references lists of literature reviews, meta-analyses, and other primary studies identified in the database search phase to locate any primary studies that may have been missed in the database search. An additional 3 articles were identified. Finally, a hand search of three top journals from which most studies on reading interventions for EBD were located was conducted for the years 1996 to 2023. These were Behavioral Disorders, Remedial and Special Education , and Exceptional Children . The hand-search did not yield any additional articles. A spreadsheet listing all the articles identified across the three phases was prepared. Following application of inclusion criteria, a total of 24 peer-reviewed journal articles were retained. Inclusion Criteria Two independent coders reviewed the articles to make sure that they met the following inclusion criteria: (1) The studies evaluated interventions targeting reading outcomes of students with or at risk of E/BD. If studies mentioned an academic intervention without specifically targeting reading outcomes for EBD, they were excluded; (2) Studies had to be published in peer reviewed journals; (3) The studies were published between 2000 and 2020. This period covers a time frame during which major reforms on educational policies and practices were instituted (e.g., NCLB, 2002; Race to Top, 2015), which resulted in significant changes in approaches to educational research. Educational Research Standards reforms put forth by the What Works Clearinghouse (Kratochwill et al., 2010 ) also suggest that research older than 20 years may be discarded from the EBP reviews owing to changes that have taken place over the years; (4) The participants in the studies were identified as having or at-risk for emotional and behavioral disabilities, behavioral disorders, or behavioral problems; (5) Studies used SCDs or group experimental designs. Studies using non-experimental designs (e.g., qualitative approaches) were excluded. (6) Studies were conducted in English. Coder Training This study is an extension of a prior study evaluating the same body of literature for CEC quality indicators (Chitiyo et al., 2023). A graduate assistant was trained on the application of the study characteristics codes. Following the coding training, the lead author and the graduate student coded the studies for study characteristics and methodological quality using the CEC quality indicators (Cook et al., 2015 ; See Chitiyo et al., 2023). A detailed description of the coder training procedures, inter observer agreement data, and coding procedures for studies’ demographic characteristics and data extraction are thoroughly described in the preceding study (Chitiyo et al., 2023). Summary of Coding Procedures Two separate codebooks were created to extract data from the primary studies. The first codebook denoted study descriptive characteristics, which include research designs, participants’ demographic information (age, gender, race), independent variables (type, duration, agent, and treatment fidelity), and dependent variables (types, IOA). The second codebook denoted the variables for running a meta-analysis in R statistical software. The variables included study identification number (SID), case identification number (PID), dependent variable identification number (DVID), Direction of dependent variable change (DVDir; 0 = outcome decreases if treatment works or 1 = increases if treatment works), session number (SessIDX), outcome variable on the y-axis (DVY), Phase denotation (PhaseBTM: 0 = baseline, 1 = treatment, 2 = any other phase), phase number (NumPh), journal identification number (JID), design variable (DesVar; 0 = multiple baseline, 1 = reversal design). Codes denoting moderators included research design (SCD = 0, Group Design = 1), instructional format (curriculum-based = 0, instruction-based = 1), intervention agent (teacher(s) = 0, non-teachers = 1), setting (self-contained/special education school or classroom = 0, other = 1), class size (one-to-one = 0, group = 1), and dependent variable type (Phon awareness = 0, reading fluency = 1, comprehension = 2. Demographic characteristics codes included gender (male = 0, female = 1), grades (K-3 = 0, 4–8 = 1, and 9–12 = 2), Data Extraction The lead author and a graduate assistant extracted raw data from the SCD studies using a free, open source, web-based data digitization software called Digitizeit (Rohatgi, 2017). Data from group design studies were extracted manually and stored on an Excel Spreadsheet. The first author went through each group design study and examined to see if data enabling calculation of effect sizes were provided. Such data included dependent variables’ pre and posttest mean scores and standard deviations. For studies involving independent samples, data were extracted for both control and experimental groups. Group experimental designs with no control group were excluded (see Cook et al., 2014). Since some studies included other dependent variables other than reading, only data pertaining to reading outcomes were collected. A spreadsheet containing all data collected from all group design studies meeting inclusion criteria for meta-analysis was created. Effect Size Calculations Using the Hierarchical Linear Models for Single Case Designs (SCDHLM; Pustejovsky et al., 2023) R software and procedures described by Pustejovsky (2013), we computed the Hedges’ g effect sizes and their respective standard errors for all SCD. The Hedges’ g is the sample size bias corrected version for Hedges’ d (Shadish et al., 2014). It is based on a number of assumptions, including: 1). Studies only used reversal or multiple baseline designs with at least three participants evaluated on each outcome case, 2) Dependent variables are measured on a continuous scale that is common across all participants in a single case, 3) Stable baselines not exhibiting trends, 4) Homogeneous effects of interventions across all participants in a single case, and 5) Normal distribution of outcomes about case and phase means (Shadish et al., 2015). Hedges’ g effect sizes for group designs were computed using an excel template with formulas for Hedges’ g effect sizes (DeFife, 2018). Next, we computed the aggregate effect sizes for individual studies using the inverse variance weighting method (Shadish et al., 2014) and a pooled Hedge’s g effect size from across the 28 studies. A three-level modelling analysis was conducted consisting of three forms of variance: random sampling variance for observed individual effect sizes (level 1), variances within studies (level 2), and variances between studies (level 3). Meta-Analysis Following the computation of effect sizes, we used a three-level meta-analysis model to carry out a meta regression analysis to assess the effects of potential moderators identified in the literature. These were setting, grade, format of instruction, class size, and type of dependent variables. The Cochrane’s Q test of homogeneity of effects was used to assess whether effect sizes across studies differed significantly to conclude that samples were not from the same population. The results from Cochrane’s Q were used to determine whether to interpret results using the fixed or random effects model. One group design study included in the initial phase (Rogevich & Perin, 2008) was excluded from the meta-analysis because of extremely large individual effect sizes. Results The literature search resulted in the identification of 27 studies meeting inclusion criteria. Most studies (79%; n = 21) used SCD, whereas 2% ( n = 6) used group design. Researchers using SCDs used mostly multiple baseline design across participants (MBD-P; 90%, n = 19) followed by multiple probe design (MPD; 10%, n = 2). A more detailed description of the study characteristics is found in the preceding study (Chitiyo et al., 2023) Participants’ demographics. There were on average, 4 participants across the 21 SCDs ( R = 1–7), and 48 participants in the group designs ( R = 5–171). In studies that reported participants’ sex (93%; n = 25), participants were mostly male (80%, n = 121) compared to females (20%, n = 38). A fair number of the studies (30%, n = 8) featured only male participants, whereas only one study featured female participants only. Information regarding participants’ gender and age was not reported in 19% ( n = 5) and 22% ( n = 6) of the studies respectively. Information regarding participants’ grade levels was reported in 78% ( n = 21) of the studies, and of these, 38% ( n = 8) targeted k – 3, whereas 57% ( n = 12) targeted grades 4 and above. Although this review primarily targeted studies involving students with or at risk for ED/EBD, some studies also featured participants with comorbid conditions. Studies that included participants with or at risk of ED/EBD only constituted 70% ( n = 21) of the studies, whereas 33% ( n = 10) of the studies featured comorbid disabilities. Comorbid diagnosis that featured more frequently included speech language impairment (SLI; 40%, n = 4), ADHD (50%, n = 5), OHI (30%, n = 3), and LD (20%, n = 2). Settings and intervention agents. Interventions in 63% of the studies ( n = 19) were administered in self-contained or special education classrooms, whereas interventions in 13% of the studies ( n = 4) were conducted in general education classrooms. Interventions in 27% ( n = 8) of the studies were conducted in settings other than self-contained or special and general education classrooms. These included juvenile correctional facilities and specialized or residential treatment centers. Regarding intervention agents, teachers, tutors or instructors served as the treatment agents in 67% ( n = 20) of the studies, whereas researchers and graduate students delivered instruction in 23% ( n = 7) and 13% ( n = 4) of the studies respectively. Effect Sizes for Individual Outcomes Across Studies A total of 22 Hedges’ g individual effect sizes were computed from the 6 group experimental studies (Table 4.4). On average, each study constituted of 5 effect sizes ( R = 2–6) calculated for each dependent variable. The effect sizes ranged from 0.10 to 2.23, both in Nelson, Stage et al ( 2005 ). Majority of the effect sizes (74%, n = 25) were computed for phonological awareness related skills including nonsense letter naming/rapid naming (18%, n = 6), phonemic segmentation/processing (24%, n = 8), word attack/identification (15%, n = 5), nonsense word fluency (12%, n = 4), and initial sound fluency (6%, n = 2). Reading comprehension and oral reading fluency each constituted 12% ( n = 4) of the effect sizes respectively. A total of 36 individual effect sizes were calculated for the 17 SCDs that met inclusion criteria for the meta-analysis (Table 4.5). The effect sizes ranged from − .034 (phonemic awareness) to 3.24 (oral reading fluency). Each study featured on average, 3 effect sizes ( R = 1 to 4). Majority of the effect sizes (50%, n = 18) were computed for oral reading fluency, whereas 31% ( n = 11) and 19% ( n = 7) were computed for phonological awareness and comprehension respectively. One effect size was negative (Phonemic awareness; Balluch, 2016). Of the 36 effect sizes, 42% ( n = 15) were statistically significant. Six percent of the effect sizes ( n = 2) were negative, whereas 28% ( n = 10) lay within the small effect size range (Cohen, 1988). Twenty-eight percent ( n = 10) and 38% ( n = 14) lay within the moderate and large effect size ranges respectively. Aggregated Effect Sizes Using the weighted variance effect method (Shadish et al., 2014), we computed an aggregate effect size for each study, producing 27 effect sizes in total. A random effects meta-analysis was conducted, combining both group and SCDs, with a total of 27 study average ESs. The random effects meta-analysis yielded a statistically significant aggregated mean ES of 0.817 [ p < .05, CI (.5089, 1.1249)]. Using the magnitude of effects guidelines for interpreting Hedges’ g (Cohen, 1998), reading interventions yielded large effects. On average, the use of reading interventions improved reading outcomes by .817 standard deviation points. If variations in sample differences were accounted for, the true effect size of the reading interventions would then be expected to lie between .509 to 1.125. A Q test of homogeneity of effects indicated a statistically significant Q value ( Q = 55.6923, p < .05), resulting in the conclusion that effect sizes across the 25 studies differed by more than just sampling error. Moderator analysis Table 3 shows the results from the meta regression analysis. Six moderators identified in the literature were assessed to see if they contributed to differences in effect sizes across studies. These were participants’ grades, intervention agents, type of study design, setting, instructional format , and class size . The limited number of studies included in the meta-analysis made it difficult to categorize effects according to intervention type. The analysis resulted to statistically significant effects of study design [g = .75, CI (0.59, 0.92), Q = 3.178, p < .05], intervention agent [ g = 0.651, CI (0.50, 0.80), p < .05], and dependent variable [ g = .71, CI (0.56, 0.86), p < .05]. This means that the effectiveness of the interventions depended on participants’ age group, whether they were administered by teacher(s) or non-teacher(s), and the instructional format used. The other three moderators were statistically insignificant, and these were setting [ g = .746, CI (0.58, 0.91), p > .05], grade level [ g = .747, CI (0.59, 0.90), p > .05], instructional format [ g = 0.722, CI (0.55, 0.89), p > .05], and class size [ g = .74, CI (0.57, 0.91), p > .05]. Aggregation of effect sizes according to participants’ grades indicated that, overall, interventions used for students in grades 4–12 were more effective ( g = 1.17) compared to those used for students in grades K-3 ( g = 0.69). When effect sizes were aggregated according to the intervention agent, studies in which interventions were administered by teachers yielded greater effect sizes ( g = 1.06) compared to studies in which interventions were administered by researchers or other non-teacher personnel ( g = .69). A surprising outcome from the moderation analysis was that of instructional format. Although the p -value was significant, the aggregated effect sizes for both curriculum-based and instruction-based methods were identical (i.e. g = .90). Discussion The increased emphasis for teachers to use EBP is often met with challenges on how to find the EBP. The CEC guidelines for EBP suggests 4 criterial to facilitate this process. Meta-analyses contribute to the identification of EBP by synthesizing interventions’ research on topics of interest. This meta-analysis analyzed the effects of reading interventions on the reading outcomes of students identified with emotional/behavioral disorders. Twenty-seven studies consisting of both group and single case designs met criteria for inclusion. A Hedge’s g Between Case Effect Size was computed to determine the effects of the reading interventions on the target outcomes. The study is an extension of a prior study which assessed the methodological rigor of the studies included in this review. The current meta-analysis differs from other previous meta-analyses on reading interventions for EBD on two major aspects: (1) the inclusion of both group and SCD, and (2) the use of a novel BCES that enables comparison of studies expressed in different metrics (like group and SCD). As discussed earlier, SCDs were initially omitted from meta-analyses primarily because researchers were using visual analyses to assess intervention effectiveness. In most cases, researchers tend to not report statistical analyses. A standardized mean difference approach, such as BCES, remedies several limitations previously cited against SCD data and use of parametric measures of effect (Shadish et al., 2015). These effect sizes have been applied in a few studies now (e.g., Luo et al., 2022). This study therefore marks a very important contribution to the generation of new knowledge regarding this novel effect size and how it facilitates the identification of EBP particularly in special education where SCDs are mostly used. From the statistical analysis, this study shows that most interventions are generally effective at improving reading outcomes in students identified with EBD. Many interventions featured across many studies, including phonological awareness training, repeated reading, corrective reading, story mapping instruction, direct instruction, and peer assisted strategies. Corrective reading and repeated reading appeared mostly in studies targeting oral reading fluency. Concept and story mapping approaches appeared frequently in studies assessing comprehension. Peer assisted strategies were also very common, appearing in 19% (n = 5) of the studies. Although we were not able to run a meta regression on type of reading instruction due to varied components included in individual studies, most interventions generally resulted in moderate to strong effects in reading outcomes. The Q test of homogeneity of effects was statistically significant, showing that effect sizes across the 25 studies differed by more than just sampling error. As a resulted, a moderator analysis was conducted to assess the effects of six moderators identified in the literature (Scammacca et al., 2015; Wanzek et al., 2014 ). The six moderators were participants’ grades, intervention agent, study design, setting, instructional format , and class size. A moderation analysis of six variables including study design, intervention agent, setting, grade, instructional format, class size, and dependent variables was run to assess if each of these factors moderated the effect sizes. Three out of the 7 moderators were statistically significant. Firstly, group design studies yielded larger effect sizes compared to SCDs. For intervention agent, studies in which interventions were administered by researchers yielded significantly higher effect sizes compared to studies in which interventions were administered by teachers. For the dependent variables, studies that assessed comprehension yielded significantly higher effect sizes compared to studies that assessed reading fluency (g = .66) and phonological awareness (g = .53). participants’ grades indicated that grade moderated effectiveness of interventions. Although studies featuring students in grades 4–12 yielded greater effects compared to studies that featured students in K-3, the aggregated effect size for K-3 was moderate, close to large. Closer examination of intervention types that featured in the two grade groups showed that most studies with story mapping applied to older grades, yet story mapping had the largest study average effect sizes compared to the other groups of interventions. Although previous studies are not so clear regarding how comprehension skills of students with EBD respond to reading interventions, this finding suggests that they respond very well to story mapping strategies. Unfortunately, story mapping strategies are not typically used for younger readers as they target comprehension skills relative to early literacy skills. Furthermore, findings from previous research suggest that students with EBD exhibit stunted growth in reading skills during early years of school regardless of application of literacy instruction (Anderson, Kutash, & Duchnowski, 2001 ; Mattison, Hooper, & Glassberg, 2002). Coincidentally, all except one study (Browder & Shear) featuring early literacy instruction applied to grades K-3. The large, aggregated effect size for early literacy interventions was a result of a very large effect size for the only early literacy instruction study targeting students in grades 4–9 (Browder & Shear, 1996). If all this is considered, this finding suggests that younger students with EBD do not respond very well to early literacy interventions. Directions for Future Research Include behavioral outcomes in the studies Limitations This meta-analysis was not without limitations. Firstly, we were unable to run a meta regression on other potentially important variables such as race and type of intervention. Most studies did not report on the participants’ ethnic identities. Prior research has consistently shown an overrepresentation of certain groups of students in special education and their increased odds of lower responsiveness to intervention. A moderation analysis based on this characteristics would have helped to shed important information in this regard. Secondly, most studies included multiple components in their interventions, making it difficult to distinguish one intervention from another. For example, some studies combined corrective reading and repeated reading, direct instruction and other different practices (e.g., PALS), or corrective reading and phonological awareness instruction. Additionally, given the different types of interventions that appeared across the 27 studies, splitting these into subgroups would have left multiple categories with one or two studies. As a results, we were also unable to run a meta regression analysis of the different types of interventions. Finally, most studies did not include behaviors dependent variables in their analyses. Although reading intervention is important for addressing reading outcomes among scholars, since studies targeted students with EBD, they should have included the behavioral outcomes in their analyses. Implications This meta-analysis showed that overall, reading interventions targeting students with emotional/behavioral disorders were generally effective at increasing the targeted reading skills. Although no meta regression was conducted by type of intervention, certain practices appeared to have moderate to large effects across studies. These were repeated readings, corrective reading, steppingstones to literacy, and concept mapping. When the interventions are used in combination with other practices, they tend to result in improved reading outcomes for students with EBD. According to the results from meta regression of specific reading outcomes, comprehension yielded the largest effects compared to oral reading fluency and phonological awareness. Studies in which interventions were administered by researchers yielded larger effect sizes compared to those in which teachers administered the interventions. This may partly be due to stricter adherence to the implementation procedures by researchers relative to teachers. Maybe if teachers participate ore in research, it may also improve the effectiveness with which they implement the interventions. Conclusion Reading interventions for EBD generally effective Combining both group and SCD enables a large pool of studies Interventions need to be more targeted Declarations Author Contribution Authors in this study helped with crafting the research design, identifying literature, collecting data, analyzing data, and writing the manuscript. Argnue Chitiyo was the lead author who drafted the research idea and coordinated all other activities. Seth King helped with crafting and refining the research idea, designing the research methods, and analyzing the data. Maria Sciuchetti helped with drafting the study background and literature review. Holmes Finch helped with running the data analysis using the relevant statistical software. Goodson Dzenga helped with coding the primary studies for data analysis. All authors reveiwed the manuscript. References Alber-Morgan, A. R. (2007). 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(2005) 6 (4M, 2F), 8 3 EBD SE DI: Horizons Fast Track, PALS Phon A, ORF Teachers MBD-P Benner 10 (9M, 1F) 3 - 8 EBD SE CR ORF, Compr Teachers Group Blankenship et al. (2005) 3 (2M, 1F), 15 NM EBD SE Computer-Based Cognitive Mapping Compr Teachers MBD-B Council et al. (2016) 2 (1M, 1F), 13 7 BD SE Choice Antecedent Instruction, Rewards, and Performance Feedback ORF Researcher MPD-P Falk & Wehby (2001) 6 (6M), 5–6 K EBD, SLI, ADHD SE PALS K-PALS: Phon A Teacher & Peer directed MBD-P Lane et al. (2001) 7 (5M, 2F), 6–7 1 At Risk for EBD GE PA Training for Reading PA Graduate students MBD-P Lane, Little et al. (2007) 7 (4M, 3F), 6–7 1 BD & Reading Problems GE Teacher Led Instruction & PALS ORF, PA Teacher MBD-P Table 1 (Continued) Author Participants N, Sex, Age Grade Diagnosis Setting Intervention Dependent Variables Intervention Agent Design Lane & Fletcher (2007) * 24, 18M, 6F), 6 1 At Risk for EBD Private Room PA Training for Reading PA, NWF Paraprofessionals Group Lingo (2006) 7 (6M, 1F), 11–14 6–7 2 EBD, 4 OHI, 1 LD SE CR & RR ORF Teachers MPD-P McDaniel et al. (2011) * 31 (27M, 4F), NM 4–8 E/BD SE CR ORF Teachers Group Nelson, Benner et al. (2005) * 63 (47M, 16F), 5 K BD, Reading Difficulties GE Stepping-Stones to Literacy ELI Phon A Researchers Group Nelson, Stage et al. (2005) * 36 (34M, 2F), 2 K ED, Reading Problems GE Stepping-Stones to Literacy ELI Phon A Teachers Group Oakes et al. (2010) 9 (6M, 3F) 2 BD GE Phon A & DI ORF Teachers MBD-P Palmer et al. (2014) 4 (3M, 1F), 13–14 7 2 OHI, 2 EBD SE Concept Mapping, Dictionary Instruction Vocabulary Acquisition Skills Researcher ABAB Scott & Shearer-Lingo. (2002) 3 (3M), NM Middle EBD SE Reading Fluency Instruction ORF Teachers MBD-P Spilles et al. (2018) 8, NM, 7-10 2,3 BD GE Peer Tutoring ORF Teacher MPD-P Staubitz et al. (2005) 6 (4M, 2F), 9–11 4–5 ED, ED + ADHD SE RR Instruction, PALS ORF Researcher MBD-P Table 1. (Continued) Author Participants N, Sex, Age Grade Diagnosis Setting Intervention Dependent Variables Intervention Agent Type of Design Stone et al. (2008) 4 (NM), 15 9 EBD SE Text Mapping Compr Instructor MBD-P Strong (2004) * 6 (6M), 12–14 7–8 ED, ED + OHI + LD, ED +LSI SE CR & RR ORF Researcher MBD-P Sutherland & Snyder, (2007) * 4 (NM), 11–13 6–8 ED, ED + SLI SE PALS Compr, ORF Teacher MBD-P Wehby Falk, et al. (2005) 8 (8B), 7 - 9 2-4 EBD, LD, OHI SE Open Court Reading Curriculum, PALS Compre, Phon A Teachers MBD-P Wehby, Lane, et al. (2005) 4 (NM), 5–6 K At Risk for EBD SE Scott Foresman Kindergarten Reading Instruction Phon A, ORF Teachers MBD-P Wexler et al. (2018) 4 (4M), 16-18 12 BD JC PALS Compr Researchers MBD-P Wills et al. (2010) 171 (NM), NM NM At Risk for EBD NM Small Group Instruction, DI DIBELS & WRMT-R: Grade NWF Teachers Group Notes: M = Male, F = Female, NM = Not Mentioned, EBD = Emotional Behavioral Disorder, SLI = Speech Language Impairment, OHI = Other Health Impairment, LD = Learning Disability, ADHD = Attention Deficit Hyperactive Disorder, Dynamic Indicators of Basic Early Literacy Skills, CTOPP = Comprehensive Test of Phonological Processing, FAST = Formative Assessment System for Teachers, WRMT-R = Woodcock Reading Mastery Test Revised, CBM = Curriculum Based Measurement, GORT-4 = Gray Oral Reading Test 4th Edition, , WJ-III = Woodcock Johnson Test of Achievement, CR = Corr`3ective Reading, RR = Repeated Reading, DI = Direct Instruction, ELI = Early Literacy Instruction, PALS = Peer Assisted Learning Strategies ORF = Oral Reading Fluency, PA = Phonological Awareness, NWF = Nonsense Word Fluency, ISF = Initial Sound Fluency, LNF = Letter Naming Fluency, PSF = Phonemic Segmentation Fluency, PB = Phonemic Blending, SCD = Single Case Design, MBD-P = Multiple Baseline Design Across Participants, MBD-B = Multiple Baseline Design Across Behaviors, ATD = Alternating Treatment Design, ABAB = Reversal Design. Studies with asterisk qualified for inclusion in meta-analysis. Table 2. Summary of Effects for Group Designs Study Intervention DV G Lower CI Upper CI Benner (2003) Corrective Reading WJ-III Basic Read Skills 0.73 -0.14 1.59 Letter Word Identification 0.45 -0.39 1.31 Word Attack 0.91 0.02 1.79 DIBELS ORF 0.70 -0.17 1.56 Lane & Fletcher (2007) PATR Phon Awareness 0.93 0.09 1.78 NWF 0.39 -0.41 1.21 McDaniel et al. (2011) Corrective Reading LWI 0.39 -0.09 0.89 ORF 0.65 0.15 1.16 Comprehension 0.45 -0.05 0.95 Word Attack 0.54 0.04 1.04 Nelson & Benner et al. (2005) SSTL Phon Awareness Phon Awareness 1.02 0.33 1.72 ISF 0.66 -0.01 1.33 Phon Segmentation Fluency 0.50 -0.16 1.16 NWF 0.88 0.19 1.56 RNF 0.60 -0.06 1.27 LNF 0.70 0.02 1.37 Nelson & Stage et al. (2005) SSTL Phon Awareness Phon Awareness 0.56 -0.02 1.14 Word Identification 0.99 0.40 1.59 Word Attack 0.95 0.36 1.54 LNF 0.89 0.30 1.48 Rapid Automatic Naming 0.07 -0.45 0.64 Wills (2010) EIR & Reading Mastery 1 st Grade NWF 0.69 -0.05 1.43 1 st Grade ORF 0.42 -0.52 1.37 2 nd Grade NWF 0.33 -0.38 1.04 3 rd Grade ORF 0.65 -0.09 1.14 DIBELS = Dynamic Indicators of Basic Early Literacy Skills, CTOPP = Comprehensive Test of Phonological Processing, PATR = Phonological Awareness Training for Reading, FAST = Formative Assessment System for Teachers, WRMT-R = Woodcock Reading Mastery Test Revised, GMRT = Gates-MacGinitie Reading Tests ORF = Oral Reading Fluency, BD = Behavioral Disorder, ADHD = Attention Deficit Hyperactive Disorder, Phon Awareness = Phonological Awareness Table 3. Summary of Effects for Single Case Designs Study Intervention DV G Std Error Lower CI Upper CI Alber-Morgan et al. (2007) Repeated Reading, Error Correct Performance feedback Compre (Inferential) 1.38 0.25 0.89 1.87 Compre (literal) 1.55 0.24 1.06 2.04 ORF (CWR/M) 1.74 0.55 0.50 2.99 ORF (Errors/M) -0.84 0.25 -1.35 -0.34 Allen-De Boer et al. (2006) Correct reading ORF 0.51 0.10 -0.12 1.13 Babyak (2010) Story mapping instruction Comprehension 1.65 0.27 1.11 2.20 ORF 2.48 0.30 1.88 3.08 Barton et al. (2005) Direct Instruction PALS NWF 0.27 0.33 -0.44 0.99 ORF_CWR/M 0.34 0.31 -0.55 1.22 Phon Blending 0.64 0.43 -0.60 1.89 Word Reading 0.74 0.37 -0.17 1.66 Phon Segmentation 1.21 0.37 0.42 2.00 Blankenship (2005) Computer based cognitive mapping Reading Compr 1.27 0.43 0.83 1.70 Council et al (2016 AimsWeb 0.55 0.43 -0.45 1.54 Cold Read 0.45 0.36 -0.53 1.42 AIMaze 0.74 0.37 -0.17 1.66 CRMaze 0.74 0.42 -0.16 1.64 Falk & Wehby (2001) PALS Letter Sounds 0.32 0.32 -0.49 1.13 Word Blending 0.11 0.26 -0.60 0.83 Word Segmentation -0.18 0.26 -0.89 0.54 Lane & Little DI & PALS NWF 1.27 0.25 0.77 1.76 ORF 1.35 0.24 0.87 1.84 Lane & O’Shaughness ORF 0.86 0.26 0.34 1.38 Lingo et al. (2006) CR and RR ORF_Err/M -0.92 0.43 -1.98 0.15 ORF_CWR/M 0.46 0.28 -0.34 1.25 read -1.51 0.55 -2.79 -0.24 Oakes et al. (2010) ORF 1.03 0.37 0.19 1.87 Palmer et al. (2014) Concept Mapping Comprehension 1.78 0.64 0.25 3.31 Scott et al. Direct Instruction ORF_CWR/M 0.22 0.29 -0.47 0.91 Spilles et al. (2019) GBV + Peer Tutoring ORF 0.19 0.19 -0.31 0.69 Staubitz et al. (2005) Repeated Reading ORF_CWR/M 1.10 0.56 -0.32 2.52 ORF_CWR/M -0.12 0.48 -1.15 0.91 Stone et al. (2008) Concept Mapping Comprehension 1.80 0.74 -0.05 3.66 Strong et al. (2004) Corrective Reading ORF_CWR/M 0.51 0.33 -0.37 1.39 Repeated Reading ORF_CWR/M_Grade 0.50 0.35 -0.40 1.41 Sutherland et al. (2007) PALS ORF_Err/M -1.00 0.48 -2.14 0.15 Self-Graphing ORF_CWR/M 0.48 0.35 -0.41 1.37 Wehby & Falk (2003) OCRP + PALS NWF 0.14 0.27 -0.50 0.78 Word Blending 0.41 0.29 -0.33 1.16 Letter Sounds 0.22 0.19 -0.34 0.78 Sight Words 0.12 0.12 -0.25 0.48 Wehby, Lane, & Falk (2005) PATR LNF 0.68 0.36 -0.08 1.44 NWF 0.23 0.26 -0.37 0.82 Onset Fluency 0.52 0.27 -0.08 1.13 Wexler (2018) PALS Comprehension 1.64 0.36 0.91 2.37 ARI = Analytical Reading Inventory, Dynamic Indicators of Basic Early Literacy Skills, FAST = Formative Assessment System for Teachers, CBM = Curriculum Based Measurement, GORT-4 = Gray Oral Reading Test 4th Edition, CTOPP = Comprehensive Test of Phonological Processing, WJ-III = Woodcock Johnson Test of Achievement Version 3, WRMT-R = Woodcock Reading Mastery Test Revised, Phon Awareness = Phonemic Awareness, ORF = Oral Reading Fluency Table 4. Moderation Analysis Variable # of ES BCES Q Statistic p-Value Study Design 69 0.754 (0.60, 0.90) 2.873 0.075 SCD 46 0.626 (0.42, 0.84) Group 23 0.886 (0.67, 1.10) Intervention Agent 69 0.656 (0.51, 0.80) 4.683 0.03** Teacher 38 0.556 (0.39, 0.73) Researcher 31 0.913 (0.64, 1.19) Setting 69 0.739 (0.58, 0.90) 2.079 0.149 General Ed 29 0.847 (0.63, 1.06) Special Ed 40 0.614 (0.38, 0.85) Grade 69 0.735 (0.59, 0.88) 3.372 0.217 K - 4 40 0.719 (0.55, 0.89) 5 - 8 23 0.588 (0.21, 0.96) 9 - 12 6 1.134 (0.63, 1.64) Instructional Format 69 0.718 (0.56, 0.88) 0.477 0.490 Curriculum 31 0.652 (0.40, 0.90) Instructional 38 0.766 (0.56, 0.98) Class Size 69 0.737 (0.58, 0.89) 0.839 0.360 Group 52 0.764 (0.60, 0.93) One-on-One 17 0.544 (0.10, 0.99) Dependent Variable 69 0.712 (0.57, 0.85) 9.366 0.009* Phon Awareness 18 0.538 (0.33, 0.75) Reading Fluency 37 0.662 (0.42, 0.90) Comprehension 14 1.079 (0.80, 1.36) Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Sep, 2025 Read the published version in Journal of Behavioral Education → Version 1 posted Editorial decision: Revision requested 17 Jan, 2025 Reviews received at journal 20 Nov, 2024 Reviewers agreed at journal 18 Nov, 2024 Reviewers invited by journal 17 Nov, 2024 Editor assigned by journal 02 Nov, 2024 Submission checks completed at journal 02 Nov, 2024 First submitted to journal 30 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5362938","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":373238340,"identity":"37a04b26-60b5-4f22-b367-fe58cb91a6c9","order_by":0,"name":"Argnue Chitiyo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYFACHoYDDAYMDPwSYB4zkVoOALVIziBFC9AaBgaDG8Rq4W8/e/Dwh4LDcsa3259JMFRYJzYQ0iJxJi8B6LDDxmZ3zphJMJxJJ6yF4QaPAUhL4rYbOWwSjG2HCWuRh2nZPCP9mQTjPyK0GMC0bJBIMJNgbCBCi+GZHIMDZwzSjSXunDG2SDiWbkxQi9zxM8YfKv5Yy/HPbn9440ONtSxBLVDQDKESiFQOAnUkqB0Fo2AUjIIRBwC/7kQXV36EBgAAAABJRU5ErkJggg==","orcid":"","institution":"Ball State University","correspondingAuthor":true,"prefix":"","firstName":"Argnue","middleName":"","lastName":"Chitiyo","suffix":""},{"id":373238341,"identity":"df4a6cc7-d19a-4070-bc30-0efa43b1221e","order_by":1,"name":"Maria Sciuchetti","email":"","orcid":"","institution":"Ball State University","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Sciuchetti","suffix":""},{"id":373238342,"identity":"ba14a5f3-6984-423e-b912-a57ebb15f6c1","order_by":2,"name":"Holmes W Finch","email":"","orcid":"","institution":"Ball State University","correspondingAuthor":false,"prefix":"","firstName":"Holmes","middleName":"W","lastName":"Finch","suffix":""},{"id":373238343,"identity":"377c3ff9-4d85-4b67-856f-e8e75006bcc7","order_by":3,"name":"Goodson Dzenga","email":"","orcid":"","institution":"University of Montana Western","correspondingAuthor":false,"prefix":"","firstName":"Goodson","middleName":"","lastName":"Dzenga","suffix":""}],"badges":[],"createdAt":"2024-10-30 18:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5362938/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5362938/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10864-025-09597-5","type":"published","date":"2025-09-13T15:56:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70286097,"identity":"0bdd4216-4be0-45f3-bf9d-cd3f80943c5b","added_by":"auto","created_at":"2024-12-01 16:29:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83984,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLiterature Search\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5362938/v1/5cb37c0e16e432bebb8af61d.png"},{"id":70286098,"identity":"e36d81a3-4b20-4061-b111-d7e3456fbe55","added_by":"auto","created_at":"2024-12-01 16:29:10","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":658026,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5362938/v1/b110b82674516b212dc70166.jpeg"},{"id":91817671,"identity":"ff4229ef-35b8-43b6-8846-87336ca23010","added_by":"auto","created_at":"2025-09-22 07:00:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2224604,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5362938/v1/449b6a7c-1b97-41d4-9e0e-3d6858948345.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Meta-Analysis of Reading Interventions for Students with Emotional/Behavioral Disorders","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFew would argue against the critical importance of reading proficiency for both academic achievement and postschool outcomes. Yet, according to data from the Program for the International Assessment of Adult Competencies (PIAAC), approximately half of US adults between the ages of 16 and 65 read below an 8th grade level. Additionally, 1 in 5 reads at Level 1 (i.e., simple, everyday literacy activities) or below Level 1 (i.e., the most simple and concrete literacy skills; Mamedova \u0026amp; Pawlowski, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which is below a fourth-grade level. Among the population of incarcerated adults in the United States, more than 70% also read at or below a fourth-grade level (Greenberg et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and 24% reported having attended special education classes at some point in their schooling (Maruschak et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudents with disabilities are nearly 3 times as likely to be arrested than their peers without disabilities (Whitaker et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Among incarcerated youth, studies suggest that anywhere from 20% to upwards of 80% have a disability (National Center on Criminal Justice and Disability, 2016), including learning disabilities and emotional disturbance (Mendoza et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Taylor, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Emotional disturbance, or emotional and behavioral disorders (EBD), is one of 13 disability categories outlined in the Individuals with Disabilities Act (IDEA; Public Law 94\u0026ndash;142) that may qualify a student for special education services in the U.S. Youth with EBD characteristically engage in externalizing (e.g., aggression, defiance, property destruction, vindictiveness) and/or internalizing (e.g., anxiety, depression, social withdrawal) behaviors that negatively impact their educational performance. According to IDEA, the student to qualify for special education services, these behaviors must have been exhibited \u0026ldquo;over a long period of time and to a marked degree\u0026rdquo; (34 C.F.R. \u0026sect;\u0026nbsp;300.8(c)(4)(i)). Students identified with EBD often have difficulty maintaining attention during academic activities, problem-solving skills deficits, frequent school absenteeism, and difficulties following school rules and routines (Kremer et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Therefore, it is unsurprising that youth with EBD are at increased risk for negative school outcomes including lower academic achievement, poor school attendance, and school dropout. Students with EBD are also more likely to engage in risky behaviors and experience dismal post-secondary outcomes when compared to youth with and without other disabilities (Gage et al., 2014; Wagner, 2014). For example, youth with EBD are at greater risk for substance abuse and incarceration within three years of leaving school (Gage et al., 2014).\u003c/p\u003e\n\u003ch3\u003eAcademic and Reading Achievement of Students with Disabilities\u003c/h3\u003e\n\u003cp\u003eCompared to same-age peers, students with disabilities tend to experience more academic challenges and lower achievement across many domains. According to a National Longitudinal Transition Study-2 (NLTS2), the average grade level gap between tested reading and math ability and actual grade level was at least two years (Wagner et al., 2004). Further analysis revealed that 83% of youth with disabilities performed below the mean on reading comprehension while 50% of students without disabilities performed below the mean (Wagner et al., 2006). Perhaps even more disconcerting is that these data indicate 24% of youth with disabilities score more than two standard deviations below the mean on reading comprehension compared to only 2% of their peers without disabilities. These findings were also observed by Gilmour and colleagues (2019) who found students with disabilities showed a wider reading achievement gap of about 1.17 standard deviations lower than their nondisabled peers.\u003c/p\u003e \u003cp\u003eWhen evaluating the academic performance of youth with emotional and behavioral disorders (EBD) specifically, NLTS2 data indicate these students performed better in science and applied math when compared to their reading comprehension performance, findings echoed in the literature. Students with EBD have been found to perform 1 to 2 years below grade level (Nelson et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Nelson and colleagues (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) reported that 83% of their study\u0026rsquo;s sample of children with EBD scored below students in the typical group on a standardized measure of reading skills. Mattison (2008) concluded these students demonstrate weakness in early literacy skills, specifically phonological awareness, and comprehension. Similarly, Reid and colleagues (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) noted that students with EBD performed lower in math and reading when compared to other students with disabilities and displayed greater rates of school failure. Despite these well document academic deficits, intervention research for students with EBD continues to focus mostly on their social, behavioral, and adaptive skills (McKenna et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Webby et al., 2003). Given the academic deficits and post-school outcomes for youth with EBD, it becomes imperative to identify academic interventions that target and improve the specific skills weaknesses characteristic of these students.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eEvidence based reading interventions for youth with EBD\u003c/h2\u003e \u003cp\u003eThere is a growing body of literature on reading interventions for students with EBD. While the majority of studies focus on elementary level students (Rivera et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), studies with middle and high school students are increasing (Garwood, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Researchers have targeted a range of literacy skills such as phonemic awareness (e.g., Lane et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), oral reading fluency (e.g., McDaniel et al., 2013), and comprehension (e.g., Garwood et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In addition, studies have evaluated the efficacy of corrective reading (e.g., Lingo et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), peer assisted learning (e.g., Faulk \u0026amp; Webby, 2001; Fuchs et al., 2009), and repeated reading (e.g., Escarpio \u0026amp; Barbetta, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) strategies for these students. Findings from these studies suggest students with EBD can be responsive to and benefit from such interventions; yet they continue to lag behind their peers. As such, there has been an increased emphasis for educators to use evidence based instructional strategies, or evidence-based practices (EBP).\u003c/p\u003e \u003cp\u003eEBPs are interventions that have been demonstrated to be effective through rigorous experimental research (Cook et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Cook et al., 2009). Many accreditation boards in education have established standards outlining the procedures for identifying a practice as evidence based. According to the guidelines set forth by the Council for Exceptional Children (CEC), identifying EBPs may constitute four procedural criteria which include (1) studies are conducted using experimental designs demonstrating functional relationships between interventions and outcomes, (2) studies are of high quality, (3) each practice is supported by a larger quantity of studies, and (4) effects are synthetized across multiple studies. Regarding the use of experimental design, EBP approach requires researchers to use experimental designs that demonstrate strong correlations between interventions and students\u0026rsquo; outcomes. In special education, these designs constitute group and single case designs (Horner et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Regarding the quality of studies, EBP requires that methodologies address all potential threats to internal and external validity. The What Works Clearinghouse (WWC) and CEC have established with quality indicators to guide assessment of the methodological rigor of interventions\u0026rsquo; research. The CEC quality indicators consist of 8 methodological domains, which are \u003cem\u003econtext and setting, participants, intervention agents, description of practice, implementation fidelity, internal validity, outcome measures/dependent variables\u003c/em\u003e, and \u003cem\u003edata analysis\u003c/em\u003e (Cook et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The dimension of quantity of research requires that a single intervention be supported by at least 2 experimental studies of high quality. The aim is that intervention effectiveness is demonstrated by replication of the study and findings across more participants in different settings. Finally, studies should demonstrate, in quantitative terms, the magnitude of effects of the interventions on outcomes of interest using standard effect sizes. Systematic reviews and meta-analyses are commonly used to synthesize effects of interventions across multiple studies (Maggin et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Talbott et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRole of Meta Analyses in Identifying EBP in reading\u003c/h3\u003e\n\u003cp\u003eMeta analyses have traditionally been used to quantitatively synthesize effects across multiple studies and to identify factors that moderate effectiveness of interventions when differences in effects cannot be limited to differences in individual characteristics. Meta-analyses therefore contribute to EBP by summarizing effects and showing interventions yielding the largest gains across multiple participants and settings (Maggin et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; McNamara \u0026amp; Scales, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Several meta-analyses on reading interventions for EBD have been conducted. Benner and colleagues (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) conducted a meta-analysis of studies published between 1970\u0026ndash;2010 on the effects of reading instruction on reading skills for students with or at risk for EBD. They computed a Rosenthal\u0026rsquo;s correlation (1994) between baseline and treatment conditions for SCD and Hedge\u0026rsquo;s g effect sizes for Group designs. They reported a positive effect of the reading instruction on the reading outcomes of students with or at risk for EBD. Although this study shed some important information regarding the effectiveness of the interventions assessed, it did not conduct a meta regression to assess the role of potential moderators on the effect sizes. Secondly, the study did not aggregate effect sizes within studies, thereby not adequately accounting for the shared variance within individual studies.\u003c/p\u003e \u003cp\u003eGarwood and colleagues (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) reviewed instructional interventions targeting reading comprehension for middle and high school students outside of general education settings. They computed Improvement Rate Difference (IRD) effect sizes to calculate the effects. They reported moderate to large effects across studies. The study presents some notable limitations. Firstly, the use of IRD effect size method encourages the removal of overlapping data points in baseline and intervention phases from the analysis thereby eliminating numerous data that may show important variances within individual participants (Parker et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Secondly, as a non-overlap method, IRD does not really show a change in the magnitude for the target outcomes. Instead, it shows the percentages of data points in intervention phases that do not overlap with data points in baseline phases but without really showing a magnitude of effect. Finally, this study included only single case designs, thereby limiting the pool of potential important group experimental studies in the analysis.\u003c/p\u003e \u003cp\u003eOther studies also synthesized research on reading interventions for EDB (e.g., Dunn et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Burke et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; McKenna et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), showing potential effectiveness of the interventions on reading outcomes in EBD. Dunn et al (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) evaluated studies on peer mediated interventions (PMI) targeting academic achievement for students with EBD. They computed PND, SMD, and Tau U for the 24 studies, from which they reported moderate effects of PMI across all academic subject areas. The mean effect sizes for reading outcomes were lower than those for spellings, English, and social studies. Burke and colleagues (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) computed Tau U effect sizes for the 11 studies assessing reading interventions for middle and secondary students with EBD. The study reported overall positive effects across the 11 studies. However, the limited studies included in the review make it difficult to generalize findings to all students with EBD.\u003c/p\u003e \u003cp\u003eAs shown in a majority of the previous meta-analyses for reading interventions in EBD, researchers mostly used non-overlap statistics like IRD and Tau U to calculate effect sizes. Although these methods provide important tools for quantitatively synthesizing findings across single case studies, most non-overlap methods do not enable comparison of effect across different study designs or studies whose outcomes are expressed in different metrics (e.g. percent intervals in which behavior occurred versus frequency count; Hedges et al., 2012; Shadish et al., 2015). As a result, most of these studies have included only SCD studies in their reviews, yet several studies in educational research have traditionally been conducted using group experimental designs. Combining group and SCDs in meta-analyses can create a larger pool of studies from which to meta-analyze results with greater precision. Since most research in special education is conducted using both group and SCDs, synthesizing effects across different study designs may enable researchers to draw more overarching conclusions pertaining effectiveness of interventions (Shadish et al., 2015).\u003c/p\u003e \u003cp\u003eMost non-overlap methods commonly used in meta-analyzing SCD studies fail to adequately account for trend effects of the time series nature of SCDs data and data autocorrelation/serial dependency (Shadish et al., 2015). Autocorrelation occurs when a participant\u0026rsquo;s rate of responding on one observation day correlates to their responding on a previous observation session. A change in the behavior on the observation day may therefore be attributed to student\u0026rsquo;s exposure to certain, non-treatment related factors on the previous observation days. Such a situation violates the parametric assumption of independence of error terms (i.e. the idea that scores on one observation day are independent of influence from previous observation), and failure to account for it can result to biased parameters (Shadish, Rindskopf, Hedges, \u0026amp; Sullivan, 2013).\u003c/p\u003e \u003cp\u003eFinally, most non-overlap methods calculate proportion of data points in the intervention phases that do not overlap with data points in the baseline phases. Thus, non-overlap methods do not show the magnitude of change in the behaviors of interest associated with the interventions (Parker et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Of all non-overlap methods, Tau-U and ECL are the only two that control for baseline trend. Both methods however suffer the disadvantage of excessive sensitivity to changes in phase lengths (Parker et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eBetween Case Effect Size\u003c/h3\u003e\n\u003cp\u003eIn order to address some of the previously mentioned challenges associated with traditional SCD effect sizes, novel approaches for synthesizing effects across both group and SCDs have been developed (Shadish et al., 2015). Shadish and colleagues (2015) identified three between case effect sizes measures (BCES: Hedges, Pustejovsky, \u0026amp; Shadish, 2012; Pustejovsky et al., 2014; Swaminathan, Rogers, \u0026amp; Horner, 2014) that address most of the limitations stated earlier. All three methods enable researchers to synthesize results across different study designs. Effect sizes are based off standardized mean differences between baseline and treatment scores. They all assume normality of error terms about phase means, and treatment effects to be constant across cases. It has additional assumptions of linear time trend and heterogeneous coefficients of effects across cases (i.e. treatment effects are allowed to vary across cases).\u003c/p\u003e \u003cp\u003eBCESs also address data trend effects associated with most non-overlap methods commonly used for SCDs. Hedges et al. (2012) provide procedures for testing data for trend effects and SPSS software options for detrending data prior to computing \u003cem\u003ed\u003c/em\u003e statistics. All three BCESs also control for autocorrelation/serial dependency either by estimating autocorrelation as part of the procedures for computing effect sizes, or by using randomized effects procedures described by Gurka, Edwards, and Muller (2011). Furthermore, the methods assume normality of error terms about phase means within cases (Shadish et al., 2015). However, Shadish and colleagues (2014) note that non-normality may not be a big concern since a corrected \u003cem\u003eg\u003c/em\u003e statistic is considered to be robust to violation of the normality assumption. That is, even if baseline and intervention data across participants are not normally distributed, there is a very insignificant effect on the calculated effect sizes.\u003c/p\u003e \u003cp\u003eIf BCESs are properly applied to meta-analyses of group and SCDs, they may result in more accurate effect sizes, thereby giving a better reflection of efficacy of interventions (Shadish et al., 2015). Furthermore, BCESs are most ideal for populations in which a large number of group and SCDs exist. One such population is students with emotional and behavioral disorders (EBD). This population represents an ideal population to examine efficacy of academic interventions because they respond to treatment differentially from other learning disability categories (Reid, Gonzalez, Nordness, Trout, \u0026amp; Epstein, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Research also shows acute academic deficits for children with EBD relative to students without disabilities (Lane, Barton, Nelson, \u0026amp; Wehby, 2008; Reid et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This study seeks to answer two research questions: \u003cem\u003e1) What is the effect of reading interventions on reading outcomes for students identified with EBD? 2) What factors moderate the effectiveness of reading interventions for students with EBD?\u003c/em\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eLiterature Search\u003c/h2\u003e\n\u003cp\u003eIn order to locate studies on reading interventions for EBD, we followed a three steps process consisting of database search, ancestral search, and hand-search. In the first step, we conducted an electronic databases search consisting of the following databases: PsycInfo, PsycArticles, CINAHL, ERIC, and EBSCO Management Collection. The search was limited to studies published from 2000 to present. The following search terms were entered into the search engine:\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003e\u003cem\u003e(AB (\"emotion* behavior* dis*\" OR \"ebd\" OR \"behavior* dis*\" OR \"emotion* dis*\")) AND (AB (\"read*\" OR \"litera*\" OR \"comprehen*\" OR \"phon*\" OR \"alphabet*\" OR \"fluen*\" OR \"vocabulary\")).\u003c/em\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eThe electronic database search resulted in a total of 5737 returned abstracts, consisting of 4211 journal articles. The rest were dissertations (895), books (564), magazines (24), CEUs (13), trade publications (4), and newspaper articles (n\u0026thinsp;=\u0026thinsp;4). After removing duplicates, 3848 abstracts remained for screening. The lead author and a graduate research assistant trained on the inclusion and exclusion criteria read the titles and abstracts of retrieved peer reviewed articles independently to determine the studies meeting the initial inclusion criteria. If criteria for inclusion/exclusion could not be ascertained from reading the title or abstract, a full text of the article was extracted. The two reviewers independently coded the titles/abstracts for inclusion and compared their findings upon completion. The two reviewers agreed on 98% of the titles/abstracts. Articles on which the reviewers differed were discussed and a final decision for inclusion/exclusion was made in consultation. A total of 21 articles met criteria for inclusion. All studies that did not meet the initial selection criteria were discarded.\u003c/p\u003e\n\u003cp\u003eNext, an ancestral search was conducted. The primary author read through the references lists of literature reviews, meta-analyses, and other primary studies identified in the database search phase to locate any primary studies that may have been missed in the database search. An additional 3 articles were identified. Finally, a hand search of three top journals from which most studies on reading interventions for EBD were located was conducted for the years 1996 to 2023. These were \u003cem\u003eBehavioral Disorders, Remedial and Special Education\u003c/em\u003e, and \u003cem\u003eExceptional Children\u003c/em\u003e. The hand-search did not yield any additional articles. A spreadsheet listing all the articles identified across the three phases was prepared. Following application of inclusion criteria, a total of 24 peer-reviewed journal articles were retained.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eInclusion Criteria\u003c/h2\u003e\n\u003cp\u003eTwo independent coders reviewed the articles to make sure that they met the following inclusion criteria: (1) The studies evaluated interventions targeting reading outcomes of students with or at risk of E/BD. If studies mentioned an academic intervention without specifically targeting reading outcomes for EBD, they were excluded; (2) Studies had to be published in peer reviewed journals; (3) The studies were published between 2000 and 2020. This period covers a time frame during which major reforms on educational policies and practices were instituted (e.g., NCLB, 2002; Race to Top, 2015), which resulted in significant changes in approaches to educational research. Educational Research Standards reforms put forth by the What Works Clearinghouse (Kratochwill et al., \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) also suggest that research older than 20 years may be discarded from the EBP reviews owing to changes that have taken place over the years; (4) The participants in the studies were identified as having or at-risk for emotional and behavioral disabilities, behavioral disorders, or behavioral problems; (5) Studies used SCDs or group experimental designs. Studies using non-experimental designs (e.g., qualitative approaches) were excluded. (6) Studies were conducted in English.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e\u0026nbsp;\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eCoder Training\u003c/h2\u003e\n\u003cp\u003eThis study is an extension of a prior study evaluating the same body of literature for CEC quality indicators (Chitiyo et al., 2023). A graduate assistant was trained on the application of the study characteristics codes. Following the coding training, the lead author and the graduate student coded the studies for study characteristics and methodological quality using the CEC quality indicators (Cook et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; See Chitiyo et al., 2023). A detailed description of the coder training procedures, inter observer agreement data, and coding procedures for studies\u0026rsquo; demographic characteristics and data extraction are thoroughly described in the preceding study (Chitiyo et al., 2023).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eSummary of Coding Procedures\u003c/h2\u003e\n\u003cp\u003eTwo separate codebooks were created to extract data from the primary studies. The first codebook denoted study descriptive characteristics, which include research designs, participants\u0026rsquo; demographic information (age, gender, race), independent variables (type, duration, agent, and treatment fidelity), and dependent variables (types, IOA). The second codebook denoted the variables for running a meta-analysis in R statistical software. The variables included study identification number (SID), case identification number (PID), dependent variable identification number (DVID), Direction of dependent variable change (DVDir; 0\u0026thinsp;=\u0026thinsp;outcome decreases if treatment works or 1\u0026thinsp;=\u0026thinsp;increases if treatment works), session number (SessIDX), outcome variable on the y-axis (DVY), Phase denotation (PhaseBTM: 0\u0026thinsp;=\u0026thinsp;baseline, 1\u0026thinsp;=\u0026thinsp;treatment, 2\u0026thinsp;=\u0026thinsp;any other phase), phase number (NumPh), journal identification number (JID), design variable (DesVar; 0\u0026thinsp;=\u0026thinsp;multiple baseline, 1\u0026thinsp;=\u0026thinsp;reversal design). Codes denoting \u003cstrong\u003emoderators\u003c/strong\u003e included research design (SCD\u0026thinsp;=\u0026thinsp;0, Group Design\u0026thinsp;=\u0026thinsp;1), instructional format (curriculum-based\u0026thinsp;=\u0026thinsp;0, instruction-based\u0026thinsp;=\u0026thinsp;1), intervention agent (teacher(s)\u0026thinsp;=\u0026thinsp;0, non-teachers\u0026thinsp;=\u0026thinsp;1), setting (self-contained/special education school or classroom\u0026thinsp;=\u0026thinsp;0, other\u0026thinsp;=\u0026thinsp;1), class size (one-to-one\u0026thinsp;=\u0026thinsp;0, group\u0026thinsp;=\u0026thinsp;1), and dependent variable type (Phon awareness\u0026thinsp;=\u0026thinsp;0, reading fluency\u0026thinsp;=\u0026thinsp;1, comprehension\u0026thinsp;=\u0026thinsp;2. \u003cstrong\u003eDemographic characteristics\u003c/strong\u003e codes included gender (male\u0026thinsp;=\u0026thinsp;0, female\u0026thinsp;=\u0026thinsp;1), grades (K-3\u0026thinsp;=\u0026thinsp;0, 4\u0026ndash;8\u0026thinsp;=\u0026thinsp;1, and 9\u0026ndash;12\u0026thinsp;=\u0026thinsp;2),\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eData Extraction\u003c/h2\u003e\n\u003cp\u003eThe lead author and a graduate assistant extracted raw data from the SCD studies using a free, open source, web-based data digitization software called Digitizeit (Rohatgi, 2017). Data from group design studies were extracted manually and stored on an Excel Spreadsheet. The first author went through each group design study and examined to see if data enabling calculation of effect sizes were provided. Such data included dependent variables\u0026rsquo; pre and posttest mean scores and standard deviations. For studies involving independent samples, data were extracted for both control and experimental groups. Group experimental designs with no control group were excluded (see Cook et al., 2014). Since some studies included other dependent variables other than reading, only data pertaining to reading outcomes were collected. A spreadsheet containing all data collected from all group design studies meeting inclusion criteria for meta-analysis was created.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eEffect Size Calculations\u003c/h2\u003e\n\u003cp\u003eUsing the Hierarchical Linear Models for Single Case Designs (SCDHLM; Pustejovsky et al., 2023) R software and procedures described by Pustejovsky (2013), we computed the Hedges\u0026rsquo; g effect sizes and their respective standard errors for all SCD. The Hedges\u0026rsquo; \u003cem\u003eg\u003c/em\u003e is the sample size bias corrected version for Hedges\u0026rsquo; \u003cem\u003ed\u003c/em\u003e (Shadish et al., 2014). It is based on a number of assumptions, including: 1). Studies only used reversal or multiple baseline designs with at least three participants evaluated on each outcome case, 2) Dependent variables are measured on a continuous scale that is common across all participants in a single case, 3) Stable baselines not exhibiting trends, 4) Homogeneous effects of interventions across all participants in a single case, and 5) Normal distribution of outcomes about case and phase means (Shadish et al., 2015). Hedges\u0026rsquo; \u003cem\u003eg\u003c/em\u003e effect sizes for group designs were computed using an excel template with formulas for Hedges\u0026rsquo; \u003cem\u003eg\u003c/em\u003e effect sizes (DeFife, 2018). Next, we computed the aggregate effect sizes for individual studies using the inverse variance weighting method (Shadish et al., 2014) and a pooled Hedge\u0026rsquo;s g effect size from across the 28 studies. A three-level modelling analysis was conducted consisting of three forms of variance: random sampling variance for observed individual effect sizes (level 1), variances within studies (level 2), and variances between studies (level 3).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003ch2\u003eMeta-Analysis\u003c/h2\u003e\n\u003cp\u003eFollowing the computation of effect sizes, we used a three-level meta-analysis model to carry out a meta regression analysis to assess the effects of potential moderators identified in the literature. These were setting, grade, format of instruction, class size, and type of dependent variables. The Cochrane\u0026rsquo;s Q test of homogeneity of effects was used to assess whether effect sizes across studies differed significantly to conclude that samples were not from the same population. The results from Cochrane\u0026rsquo;s Q were used to determine whether to interpret results using the fixed or random effects model. One group design study included in the initial phase (Rogevich \u0026amp; Perin, 2008) was excluded from the meta-analysis because of extremely large individual effect sizes.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe literature search resulted in the identification of 27 studies meeting inclusion criteria. Most studies (79%; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21) used SCD, whereas 2% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6) used group design. Researchers using SCDs used mostly multiple baseline design across participants (MBD-P; 90%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19) followed by multiple probe design (MPD; 10%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2). A more detailed description of the study characteristics is found in the preceding study (Chitiyo et al., 2023)\u003c/p\u003e \u003cp\u003e \u003cb\u003eParticipants\u0026rsquo; demographics.\u003c/b\u003e There were on average, 4 participants across the 21 SCDs (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1\u0026ndash;7), and 48 participants in the group designs (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5\u0026ndash;171). In studies that reported participants\u0026rsquo; sex (93%; \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;25), participants were mostly male (80%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;121) compared to females (20%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;38). A fair number of the studies (30%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8) featured only male participants, whereas only one study featured female participants only. Information regarding participants\u0026rsquo; gender and age was not reported in 19% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5) and 22% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6) of the studies respectively. Information regarding participants\u0026rsquo; grade levels was reported in 78% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21) of the studies, and of these, 38% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8) targeted k \u0026ndash; 3, whereas 57% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12) targeted grades 4 and above. Although this review primarily targeted studies involving students with or at risk for ED/EBD, some studies also featured participants with comorbid conditions. Studies that included participants with or at risk of ED/EBD only constituted 70% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21) of the studies, whereas 33% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10) of the studies featured comorbid disabilities. Comorbid diagnosis that featured more frequently included speech language impairment (SLI; 40%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4), ADHD (50%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5), OHI (30%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3), and LD (20%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSettings and intervention agents.\u003c/b\u003e Interventions in 63% of the studies (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19) were administered in self-contained or special education classrooms, whereas interventions in 13% of the studies (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4) were conducted in general education classrooms. Interventions in 27% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8) of the studies were conducted in settings other than self-contained or special and general education classrooms. These included juvenile correctional facilities and specialized or residential treatment centers. Regarding intervention agents, teachers, tutors or instructors served as the treatment agents in 67% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20) of the studies, whereas researchers and graduate students delivered instruction in 23% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7) and 13% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4) of the studies respectively.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEffect Sizes for Individual Outcomes Across Studies\u003c/h2\u003e \u003cp\u003eA total of 22 Hedges\u0026rsquo; \u003cem\u003eg\u003c/em\u003e individual effect sizes were computed from the 6 group experimental studies (Table\u0026nbsp;4.4). On average, each study constituted of 5 effect sizes (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2\u0026ndash;6) calculated for each dependent variable. The effect sizes ranged from 0.10 to 2.23, both in Nelson, Stage et al (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Majority of the effect sizes (74%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;25) were computed for phonological awareness related skills including nonsense letter naming/rapid naming (18%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6), phonemic segmentation/processing (24%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8), word attack/identification (15%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5), nonsense word fluency (12%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4), and initial sound fluency (6%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2). Reading comprehension and oral reading fluency each constituted 12% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4) of the effect sizes respectively.\u003c/p\u003e \u003cp\u003eA total of 36 individual effect sizes were calculated for the 17 SCDs that met inclusion criteria for the meta-analysis (Table\u0026nbsp;4.5). The effect sizes ranged from \u0026minus;\u0026thinsp;.034 (phonemic awareness) to 3.24 (oral reading fluency). Each study featured on average, 3 effect sizes (\u003cem\u003eR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 to 4). Majority of the effect sizes (50%, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18) were computed for oral reading fluency, whereas 31% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11) and 19% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7) were computed for phonological awareness and comprehension respectively. One effect size was negative (Phonemic awareness; Balluch, 2016). Of the 36 effect sizes, 42% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15) were statistically significant. Six percent of the effect sizes (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2) were negative, whereas 28% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10) lay within the small effect size range (Cohen, 1988). Twenty-eight percent (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10) and 38% (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;14) lay within the moderate and large effect size ranges respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eAggregated Effect Sizes\u003c/h2\u003e \u003cp\u003eUsing the weighted variance effect method (Shadish et al., 2014), we computed an aggregate effect size for each study, producing 27 effect sizes in total. A random effects meta-analysis was conducted, combining both group and SCDs, with a total of 27 study average ESs.\u003c/p\u003e \u003cp\u003eThe random effects meta-analysis yielded a statistically significant aggregated mean ES of 0.817 [\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05, CI (.5089, 1.1249)]. Using the magnitude of effects guidelines for interpreting Hedges\u0026rsquo; \u003cem\u003eg\u003c/em\u003e (Cohen, 1998), reading interventions yielded large effects. On average, the use of reading interventions improved reading outcomes by .817 standard deviation points. If variations in sample differences were accounted for, the true effect size of the reading interventions would then be expected to lie between .509 to 1.125. A Q test of homogeneity of effects indicated a statistically significant Q value (\u003cem\u003eQ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;55.6923, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05), resulting in the conclusion that effect sizes across the 25 studies differed by more than just sampling error.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eModerator analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the results from the meta regression analysis. Six moderators identified in the literature were assessed to see if they contributed to differences in effect sizes across studies. These were \u003cem\u003eparticipants\u0026rsquo; grades, intervention agents, type of study design, setting, instructional format\u003c/em\u003e, and \u003cem\u003eclass size\u003c/em\u003e. The limited number of studies included in the meta-analysis made it difficult to categorize effects according to intervention type. The analysis resulted to statistically significant effects of study design [g\u0026thinsp;=\u0026thinsp;.75, CI (0.59, 0.92), Q\u0026thinsp;=\u0026thinsp;3.178, p\u0026thinsp;\u0026lt;\u0026thinsp;.05], intervention agent [\u003cem\u003eg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.651, CI (0.50, 0.80), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05], and dependent variable [\u003cem\u003eg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.71, CI (0.56, 0.86), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05]. This means that the effectiveness of the interventions depended on participants\u0026rsquo; age group, whether they were administered by teacher(s) or non-teacher(s), and the instructional format used. The other three moderators were statistically insignificant, and these were setting [\u003cem\u003eg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.746, CI (0.58, 0.91), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05], grade level [\u003cem\u003eg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.747, CI (0.59, 0.90), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05], instructional format [\u003cem\u003eg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.722, CI (0.55, 0.89), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05], and class size [\u003cem\u003eg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.74, CI (0.57, 0.91), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05]. Aggregation of effect sizes according to participants\u0026rsquo; grades indicated that, overall, interventions used for students in grades 4\u0026ndash;12 were more effective (\u003cem\u003eg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.17) compared to those used for students in grades K-3 (\u003cem\u003eg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.69). When effect sizes were aggregated according to the intervention agent, studies in which interventions were administered by teachers yielded greater effect sizes (\u003cem\u003eg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.06) compared to studies in which interventions were administered by researchers or other non-teacher personnel (\u003cem\u003eg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.69). A surprising outcome from the moderation analysis was that of instructional format. Although the \u003cem\u003ep\u003c/em\u003e-value was significant, the aggregated effect sizes for both curriculum-based and instruction-based methods were identical (i.e. \u003cem\u003eg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.90).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe increased emphasis for teachers to use EBP is often met with challenges on how to find the EBP. The CEC guidelines for EBP suggests 4 criterial to facilitate this process. Meta-analyses contribute to the identification of EBP by synthesizing interventions\u0026rsquo; research on topics of interest. This meta-analysis analyzed the effects of reading interventions on the reading outcomes of students identified with emotional/behavioral disorders. Twenty-seven studies consisting of both group and single case designs met criteria for inclusion. A Hedge\u0026rsquo;s g Between Case Effect Size was computed to determine the effects of the reading interventions on the target outcomes. The study is an extension of a prior study which assessed the methodological rigor of the studies included in this review.\u003c/p\u003e \u003cp\u003eThe current meta-analysis differs from other previous meta-analyses on reading interventions for EBD on two major aspects: (1) the inclusion of both group and SCD, and (2) the use of a novel BCES that enables comparison of studies expressed in different metrics (like group and SCD). As discussed earlier, SCDs were initially omitted from meta-analyses primarily because researchers were using visual analyses to assess intervention effectiveness. In most cases, researchers tend to not report statistical analyses. A standardized mean difference approach, such as BCES, remedies several limitations previously cited against SCD data and use of parametric measures of effect (Shadish et al., 2015). These effect sizes have been applied in a few studies now (e.g., Luo et al., 2022). This study therefore marks a very important contribution to the generation of new knowledge regarding this novel effect size and how it facilitates the identification of EBP particularly in special education where SCDs are mostly used.\u003c/p\u003e \u003cp\u003eFrom the statistical analysis, this study shows that most interventions are generally effective at improving reading outcomes in students identified with EBD. Many interventions featured across many studies, including phonological awareness training, repeated reading, corrective reading, story mapping instruction, direct instruction, and peer assisted strategies. Corrective reading and repeated reading appeared mostly in studies targeting oral reading fluency. Concept and story mapping approaches appeared frequently in studies assessing comprehension. Peer assisted strategies were also very common, appearing in 19% (n\u0026thinsp;=\u0026thinsp;5) of the studies. Although we were not able to run a meta regression on type of reading instruction due to varied components included in individual studies, most interventions generally resulted in moderate to strong effects in reading outcomes.\u003c/p\u003e \u003cp\u003eThe Q test of homogeneity of effects was statistically significant, showing that effect sizes across the 25 studies differed by more than just sampling error. As a resulted, a moderator analysis was conducted to assess the effects of six moderators identified in the literature (Scammacca et al., 2015; Wanzek et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The six moderators were \u003cem\u003eparticipants\u0026rsquo; grades, intervention agent, study design, setting, instructional format\u003c/em\u003e, and \u003cem\u003eclass size.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eA moderation analysis of six variables including study design, intervention agent, setting, grade, instructional format, class size, and dependent variables was run to assess if each of these factors moderated the effect sizes. Three out of the 7 moderators were statistically significant. Firstly, group design studies yielded larger effect sizes compared to SCDs. For intervention agent, studies in which interventions were administered by researchers yielded significantly higher effect sizes compared to studies in which interventions were administered by teachers. For the dependent variables, studies that assessed comprehension yielded significantly higher effect sizes compared to studies that assessed reading fluency (g\u0026thinsp;=\u0026thinsp;.66) and phonological awareness (g\u0026thinsp;=\u0026thinsp;.53).\u003c/p\u003e \u003cp\u003eparticipants\u0026rsquo; grades indicated that grade moderated effectiveness of interventions. Although studies featuring students in grades 4\u0026ndash;12 yielded greater effects compared to studies that featured students in K-3, the aggregated effect size for K-3 was moderate, close to large. Closer examination of intervention types that featured in the two grade groups showed that most studies with story mapping applied to older grades, yet story mapping had the largest study average effect sizes compared to the other groups of interventions. Although previous studies are not so clear regarding how comprehension skills of students with EBD respond to reading interventions, this finding suggests that they respond very well to story mapping strategies. Unfortunately, story mapping strategies are not typically used for younger readers as they target comprehension skills relative to early literacy skills. Furthermore, findings from previous research suggest that students with EBD exhibit stunted growth in reading skills during early years of school regardless of application of literacy instruction (Anderson, Kutash, \u0026amp; Duchnowski, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Mattison, Hooper, \u0026amp; Glassberg, 2002). Coincidentally, all except one study (Browder \u0026amp; Shear) featuring early literacy instruction applied to grades K-3. The large, aggregated effect size for early literacy interventions was a result of a very large effect size for the only early literacy instruction study targeting students in grades 4\u0026ndash;9 (Browder \u0026amp; Shear, 1996). If all this is considered, this finding suggests that younger students with EBD do not respond very well to early literacy interventions.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eDirections for Future Research\u003c/h2\u003e \u003cp\u003eInclude behavioral outcomes in the studies\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis meta-analysis was not without limitations. Firstly, we were unable to run a meta regression on other potentially important variables such as race and type of intervention. Most studies did not report on the participants\u0026rsquo; ethnic identities. Prior research has consistently shown an overrepresentation of certain groups of students in special education and their increased odds of lower responsiveness to intervention. A moderation analysis based on this characteristics would have helped to shed important information in this regard. Secondly, most studies included multiple components in their interventions, making it difficult to distinguish one intervention from another. For example, some studies combined corrective reading and repeated reading, direct instruction and other different practices (e.g., PALS), or corrective reading and phonological awareness instruction. Additionally, given the different types of interventions that appeared across the 27 studies, splitting these into subgroups would have left multiple categories with one or two studies. As a results, we were also unable to run a meta regression analysis of the different types of interventions. Finally, most studies did not include behaviors dependent variables in their analyses. Although reading intervention is important for addressing reading outcomes among scholars, since studies targeted students with EBD, they should have included the behavioral outcomes in their analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cp\u003eThis meta-analysis showed that overall, reading interventions targeting students with emotional/behavioral disorders were generally effective at increasing the targeted reading skills. Although no meta regression was conducted by type of intervention, certain practices appeared to have moderate to large effects across studies. These were repeated readings, corrective reading, steppingstones to literacy, and concept mapping. When the interventions are used in combination with other practices, they tend to result in improved reading outcomes for students with EBD. According to the results from meta regression of specific reading outcomes, comprehension yielded the largest effects compared to oral reading fluency and phonological awareness. Studies in which interventions were administered by researchers yielded larger effect sizes compared to those in which teachers administered the interventions. This may partly be due to stricter adherence to the implementation procedures by researchers relative to teachers. Maybe if teachers participate ore in research, it may also improve the effectiveness with which they implement the interventions.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eReading interventions for EBD generally effective\u003c/p\u003e \u003cp\u003eCombining both group and SCD enables a large pool of studies\u003c/p\u003e \u003cp\u003eInterventions need to be more targeted\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthors in this study helped with crafting the research design, identifying literature, collecting data, analyzing data, and writing the manuscript. Argnue Chitiyo was the lead author who drafted the research idea and coordinated all other activities. Seth King helped with crafting and refining the research idea, designing the research methods, and analyzing the data. Maria Sciuchetti helped with drafting the study background and literature review. Holmes Finch helped with running the data analysis using the relevant statistical software. Goodson Dzenga helped with coding the primary studies for data analysis. All authors reveiwed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlber-Morgan, A. R. (2007). Effects of repeated readings, error correction, and performance feedback on the fluency and comprehension of middle school students with behavior problems. \u003cem\u003eThe Journal of Special Education\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(1), 17\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAllen-De Boer, R. A., Malgren, K. W., \u0026amp; Glass, M. E. (2006). 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Classroom observations and effects of reading interventions for students at risk for emotional and behavioral disorders. \u003cem\u003eBehavioral Disorders\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(2), 103\u0026ndash;119.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e \u003cem\u003eSummary of Studies\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"900\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipants N, Sex, Age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.66667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSetting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3333%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAgent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003eAlber-Morgan et al. (2007) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6667%;\"\u003e\n \u003cp\u003e4 (3M, 1F), 12\u0026ndash;15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.66667%;\"\u003e\n \u003cp\u003e6, 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003eEBD, BP, LD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3333%;\"\u003e\n \u003cp\u003eRR, CR, \u0026amp; Performance Feedback\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eORF,\u003c/p\u003e\n \u003cp\u003eCompr\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003eGraduate Students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003eMBD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003eAllen-De Boer et al. (2006) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6667%;\"\u003e\n \u003cp\u003e4 (4M), 16\u0026ndash;18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.66667%;\"\u003e\n \u003cp\u003e4, 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003eEBD, ADHD, CD, PPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003eJCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3333%;\"\u003e\n \u003cp\u003eCR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003eGraduate Students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003eMBD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003eBabyak et al. (2000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6667%;\"\u003e\n \u003cp\u003e4 (4M), NM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.66667%;\"\u003e\n \u003cp\u003e4, 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003eBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3333%;\"\u003e\n \u003cp\u003eStory Mapping Instruction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCompr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003eTeacher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003eMBD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003eBarton et al. (2005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6667%;\"\u003e\n \u003cp\u003e6 (4M, 2F), 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.66667%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003eEBD\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3333%;\"\u003e\n \u003cp\u003eDI: Horizons Fast Track, PALS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePhon A, ORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003eTeachers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003eMBD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003eBenner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6667%;\"\u003e\n \u003cp\u003e10 (9M, 1F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.66667%;\"\u003e\n \u003cp\u003e3 - 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003eEBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3333%;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eORF, Compr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003eTeachers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003eBlankenship et al. (2005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6667%;\"\u003e\n \u003cp\u003e3 (2M, 1F), 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.66667%;\"\u003e\n \u003cp\u003e\u0026nbsp;NM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003eEBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3333%;\"\u003e\n \u003cp\u003eComputer-Based Cognitive Mapping\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eCompr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003eTeachers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003eMBD-B\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003eCouncil et al. (2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6667%;\"\u003e\n \u003cp\u003e2 (1M, 1F), 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.66667%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003eBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3333%;\"\u003e\n \u003cp\u003eChoice Antecedent Instruction, Rewards, and Performance Feedback\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003eResearcher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003eMPD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003eFalk \u0026amp; Wehby (2001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6667%;\"\u003e\n \u003cp\u003e6 (6M), 5\u0026ndash;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.66667%;\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003eEBD, SLI, ADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3333%;\"\u003e\n \u003cp\u003ePALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eK-PALS: Phon A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003eTeacher \u0026amp; Peer directed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003eMBD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003eLane et al. (2001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6667%;\"\u003e\n \u003cp\u003e7 (5M, 2F), 6\u0026ndash;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.66667%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003eAt Risk for EBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003eGE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3333%;\"\u003e\n \u003cp\u003ePA Training for Reading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003ePA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003eGraduate students\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003eMBD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.6667%;\"\u003e\n \u003cp\u003eLane, Little et al. (2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.6667%;\"\u003e\n \u003cp\u003e7 (4M, 3F), 6\u0026ndash;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.66667%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10%;\"\u003e\n \u003cp\u003eBD \u0026amp; Reading Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6%;\"\u003e\n \u003cp\u003eGE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.3333%;\"\u003e\n \u003cp\u003eTeacher Led Instruction \u0026amp; PALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6667%;\"\u003e\n \u003cp\u003eORF, PA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12%;\"\u003e\n \u003cp\u003eTeacher\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8%;\"\u003e\n \u003cp\u003eMBD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 1\u003c/strong\u003e \u003cem\u003e(Continued)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"894\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipants N, Sex, Age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSetting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention Agent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eLane \u0026amp; Fletcher (2007) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e24, 18M, 6F), 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eAt Risk for EBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePrivate Room\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ePA Training for Reading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003ePA, NWF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eParaprofessionals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eLingo (2006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e7 (6M, 1F), 11\u0026ndash;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e6\u0026ndash;7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2 EBD, 4 OHI, 1 LD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eCR \u0026amp; RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eORF\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTeachers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eMPD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eMcDaniel et al. (2011) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e31 (27M, 4F), NM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e4\u0026ndash;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eE/BD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTeachers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNelson, Benner et al. (2005) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e63 (47M, 16F), 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eBD, Reading Difficulties\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eGE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eStepping-Stones to Literacy ELI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003ePhon A\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eResearchers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNelson, Stage et al. (2005) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e36 (34M, 2F), 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eED, Reading Problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eGE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eStepping-Stones to Literacy ELI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003ePhon A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTeachers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eOakes et al. (2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e9 (6M, 3F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eGE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ePhon A \u0026amp; DI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTeachers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eMBD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003ePalmer et al. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e4 (3M, 1F), 13\u0026ndash;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2 OHI, 2 EBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eConcept Mapping, Dictionary Instruction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eVocabulary Acquisition Skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eResearcher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eABAB\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eScott \u0026amp; Shearer-Lingo. (2002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e3 (3M), NM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eMiddle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eEBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eReading Fluency Instruction\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTeachers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eMBD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eSpilles et al. (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e8, NM, 7-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e2,3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eGE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003ePeer Tutoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTeacher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eMPD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eStaubitz et al. (2005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e6 (4M, 2F), 9\u0026ndash;11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e4\u0026ndash;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eED, ED + ADHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eRR Instruction, PALS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eORF\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eResearcher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eMBD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 1.\u003c/strong\u003e \u003cem\u003e(Continued)\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"864\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8889%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1944%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParticipants N, Sex, Age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.63889%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.94444%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSetting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1944%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8056%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDependent Variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention Agent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eType of Design\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8889%;\"\u003e\n \u003cp\u003eStone et al. (2008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1944%;\"\u003e\n \u003cp\u003e4 (NM), 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.63889%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eEBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.94444%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1944%;\"\u003e\n \u003cp\u003eText Mapping\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8056%;\"\u003e\n \u003cp\u003eCompr\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eInstructor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eMBD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8889%;\"\u003e\n \u003cp\u003eStrong (2004) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1944%;\"\u003e\n \u003cp\u003e6 (6M), 12\u0026ndash;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.63889%;\"\u003e\n \u003cp\u003e7\u0026ndash;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eED, ED + OHI + LD, ED +LSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.94444%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1944%;\"\u003e\n \u003cp\u003eCR \u0026amp; RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8056%;\"\u003e\n \u003cp\u003eORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eResearcher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eMBD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8889%;\"\u003e\n \u003cp\u003eSutherland \u0026amp; Snyder, (2007) *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1944%;\"\u003e\n \u003cp\u003e4 (NM), 11\u0026ndash;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.63889%;\"\u003e\n \u003cp\u003e6\u0026ndash;8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eED, ED + SLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.94444%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1944%;\"\u003e\n \u003cp\u003ePALS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8056%;\"\u003e\n \u003cp\u003eCompr, ORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eTeacher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eMBD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8889%;\"\u003e\n \u003cp\u003eWehby Falk, et al. (2005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1944%;\"\u003e\n \u003cp\u003e8 (8B), 7 - 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.63889%;\"\u003e\n \u003cp\u003e2-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eEBD, LD, OHI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.94444%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1944%;\"\u003e\n \u003cp\u003eOpen Court Reading Curriculum, PALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8056%;\"\u003e\n \u003cp\u003eCompre, Phon A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eTeachers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eMBD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8889%;\"\u003e\n \u003cp\u003eWehby, Lane, et al. (2005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1944%;\"\u003e\n \u003cp\u003e4 (NM), 5\u0026ndash;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.63889%;\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eAt Risk for EBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.94444%;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1944%;\"\u003e\n \u003cp\u003eScott Foresman Kindergarten Reading Instruction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8056%;\"\u003e\n \u003cp\u003ePhon A, ORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eTeachers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eMBD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8889%;\"\u003e\n \u003cp\u003eWexler et al. (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1944%;\"\u003e\n \u003cp\u003e4 (4M), 16-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.63889%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.94444%;\"\u003e\n \u003cp\u003eJC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1944%;\"\u003e\n \u003cp\u003ePALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8056%;\"\u003e\n \u003cp\u003eCompr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eResearchers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eMBD-P\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8889%;\"\u003e\n \u003cp\u003eWills et al. (2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1944%;\"\u003e\n \u003cp\u003e171 (NM), NM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 7.63889%;\"\u003e\n \u003cp\u003eNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eAt Risk for EBD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.94444%;\"\u003e\n \u003cp\u003eNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1944%;\"\u003e\n \u003cp\u003eSmall Group Instruction, DI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.8056%;\"\u003e\n \u003cp\u003eDIBELS \u0026amp; WRMT-R: Grade NWF\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eTeachers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.1111%;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"9\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cem\u003eNotes:\u003c/em\u003e M = Male, F = Female, NM = Not Mentioned, EBD = Emotional Behavioral Disorder, SLI = Speech Language Impairment, OHI = Other Health Impairment, LD = Learning Disability, ADHD = Attention Deficit Hyperactive Disorder, Dynamic Indicators of Basic Early Literacy Skills, CTOPP = Comprehensive Test of Phonological Processing, FAST = Formative Assessment System for Teachers, WRMT-R = Woodcock Reading Mastery Test Revised, CBM = Curriculum Based Measurement, GORT-4 = Gray Oral Reading Test 4th Edition, , WJ-III = Woodcock Johnson Test of Achievement, CR = Corr`3ective Reading, RR = Repeated Reading, DI = Direct Instruction, ELI = Early Literacy Instruction, PALS = Peer Assisted Learning Strategies ORF = Oral Reading Fluency, PA = Phonological Awareness, NWF = Nonsense Word Fluency, ISF = Initial Sound Fluency, LNF = Letter Naming Fluency, PSF = Phonemic Segmentation Fluency, PB = Phonemic Blending, SCD = Single Case Design, MBD-P = Multiple Baseline Design Across Participants, MBD-B = Multiple Baseline Design Across Behaviors, ATD = Alternating Treatment Design, ABAB = Reversal Design. Studies with asterisk qualified for inclusion in meta-analysis.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eSummary of Effects for Group Designs\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3665%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eIntervention\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDV\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eG\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLower CI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eUpper CI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eBenner (2003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 15.3665%;\"\u003e\n \u003cp\u003eCorrective Reading\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3625%;\"\u003e\n \u003cp\u003eWJ-III Basic Read Skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3625%;\"\u003e\n \u003cp\u003eLetter Word Identification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.3625%;\"\u003e\n \u003cp\u003eWord Attack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003eDIBELS ORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 138px;\"\u003e\n \u003cp\u003eLane \u0026amp; Fletcher (2007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3665%;\"\u003e\n \u003cp\u003ePATR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003ePhon Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3665%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003eNWF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eMcDaniel et al. (2011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 15.3665%;\"\u003e\n \u003cp\u003eCorrective Reading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003eLWI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003eORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003eComprehension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003eWord Attack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNelson \u0026amp; Benner et al. (2005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 15.3665%;\"\u003e\n \u003cp\u003eSSTL Phon Awareness\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003ePhon Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003eISF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003ePhon Segmentation Fluency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003eNWF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003eRNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003eLNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eNelson \u0026amp; Stage et al. (2005)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 15.3665%;\"\u003e\n \u003cp\u003eSSTL Phon Awareness\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003ePhon Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003eWord Identification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003eWord Attack\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003eLNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003eRapid Automatic Naming\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eWills (2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 15.3665%;\"\u003e\n \u003cp\u003eEIR \u0026amp; Reading Mastery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e Grade NWF\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003e1\u003csup\u003est\u003c/sup\u003e Grade ORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003e2\u003csup\u003end\u003c/sup\u003e Grade NWF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.3625%;\"\u003e\n \u003cp\u003e3\u003csup\u003erd\u003c/sup\u003e Grade ORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 642px;\"\u003e\n \u003cp\u003eDIBELS = Dynamic Indicators of Basic Early Literacy Skills, CTOPP = Comprehensive Test of Phonological Processing, PATR = Phonological Awareness Training for Reading, FAST = Formative Assessment System for Teachers, WRMT-R = Woodcock Reading Mastery Test Revised, GMRT = Gates-MacGinitie Reading Tests ORF = Oral Reading Fluency, BD = Behavioral Disorder, ADHD = Attention Deficit Hyperactive Disorder, Phon Awareness = Phonological Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eSummary of Effects for Single Case Designs\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"678\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eIntervention\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDV\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eG\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eStd Error\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eLower CI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eUpper CI\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eAlber-Morgan et al. (2007)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eRepeated Reading, Error Correct\u003c/p\u003e\n \u003cp\u003ePerformance feedback\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eCompre (Inferential)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eCompre (literal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eORF (CWR/M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eORF (Errors/M)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eAllen-De Boer et al. (2006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eCorrect reading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eBabyak (2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eStory mapping instruction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eComprehension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eBarton et al. (2005)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eDirect Instruction\u003c/p\u003e\n \u003cp\u003ePALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eNWF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eORF_CWR/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003ePhon Blending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eWord Reading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003ePhon Segmentation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eBlankenship (2005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eComputer based cognitive mapping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eReading Compr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eCouncil et al (2016\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eAimsWeb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eCold Read\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eAIMaze\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eCRMaze\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eFalk \u0026amp; Wehby (2001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003ePALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eLetter Sounds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eWord Blending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eWord Segmentation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eLane \u0026amp; Little\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eDI \u0026amp; PALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eNWF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eLane \u0026amp; O\u0026rsquo;Shaughness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eLingo et al. (2006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eCR and RR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eORF_Err/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eORF_CWR/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eread\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eOakes et al. (2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003ePalmer et al. (2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eConcept Mapping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eComprehension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eScott et al.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eDirect Instruction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eORF_CWR/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eSpilles et al. (2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eGBV + Peer Tutoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 143px;\"\u003e\n \u003cp\u003eORF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eStaubitz et al. (2005)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eRepeated Reading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eORF_CWR/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eORF_CWR/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003eStone et al. (2008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eConcept Mapping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eComprehension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003eStrong et al. (2004)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eCorrective Reading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eORF_CWR/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eRepeated Reading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eORF_CWR/M_Grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eSutherland et al. (2007)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003ePALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eORF_Err/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-2.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eSelf-Graphing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eORF_CWR/M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eWehby \u0026amp; Falk (2003)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eOCRP + PALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eNWF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eWord Blending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eLetter Sounds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eSight Words\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eWehby, Lane, \u0026amp; Falk (2005)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003ePATR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eLNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eNWF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eOnset Fluency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eWexler (2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003ePALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 143px;\"\u003e\n \u003cp\u003eComprehension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e2.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 678px;\"\u003e\n \u003cp\u003eARI = Analytical Reading Inventory, Dynamic Indicators of Basic Early Literacy Skills, FAST = Formative Assessment System for Teachers, CBM = Curriculum Based Measurement, GORT-4 = Gray Oral Reading Test 4th Edition, CTOPP = Comprehensive Test of Phonological Processing, WJ-III = Woodcock Johnson Test of Achievement Version 3, WRMT-R = Woodcock Reading Mastery Test Revised, Phon Awareness = Phonemic Awareness, ORF = Oral Reading Fluency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003e\u003cem\u003eModeration Analysis\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"567\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eVariable\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e# of ES\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBCES\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eQ Statistic\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ep-Value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eStudy Design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.754 (0.60, 0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e2.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.075\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eSCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.626 (0.42, 0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.886 (0.67, 1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eIntervention Agent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.656 (0.51, 0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e4.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03**\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eTeacher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.556 (0.39, 0.73)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eResearcher\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.913 (0.64, 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eSetting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.739 (0.58, 0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e2.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eGeneral Ed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.847 (0.63, 1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eSpecial Ed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.614 (0.38, 0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.735 (0.59, 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e3.372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e0.217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eK - 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.719 (0.55, 0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003e5 - 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.588 (0.21, 0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003e9 - 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e1.134 (0.63, 1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eInstructional Format\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.718 (0.56, 0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eCurriculum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.652 (0.40, 0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eInstructional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.766 (0.56, 0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eClass Size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.737 (0.58, 0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.764 (0.60, 0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eOne-on-One\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.544 (0.10, 0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eDependent Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.712 (0.57, 0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e9.366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003ePhon Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.538 (0.33, 0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eReading Fluency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e0.662 (0.42, 0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 40.2116%;\"\u003e\n \u003cp\u003eComprehension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.9347%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21.6931%;\"\u003e\n \u003cp\u003e1.079 (0.80, 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.933%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2275%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-behavioral-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jobe","sideBox":"Learn more about [Journal of Behavioral Education](http://link.springer.com/journal/10864)","snPcode":"10864","submissionUrl":"https://submission.springernature.com/new-submission/10864/3","title":"Journal of Behavioral Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Meta-analysis, emotional disorder, behavioral disorder, reading intervention, reading outcome","lastPublishedDoi":"10.21203/rs.3.rs-5362938/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5362938/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStudents identified with Emotional/Behavioral Disorders (EBD) often exhibit behavioral challenges that negatively affect their academic performance. One of their weaker areas of academic achievement lies in reading. Although numerous research on interventions to improve their reading outcomes has been conducted, some students continue to lag behind. The use of instructional practices not supported by strong empirical evidence may deter students\u0026rsquo; reading outcomes from improving. To address the reading performance gap, educators are encouraged to use evidence-based practices (EBP) supported by strong empirical research. Meta analyses play an important role in identifying and disseminating EBP. This meta-analysis evaluated reading intervention studies \u003cem\u003e(n\u003c/em\u003e\u0026thinsp;=\u0026thinsp;27) for students with emotional and behavioral disorders (EBD) using the Between Case Hedges\u0026rsquo; \u003cem\u003eg\u003c/em\u003e effect size. A random effects meta-analysis of 27 studies indicated that, overall, reading interventions increased reading outcomes of students with EBD by a large margin [\u003cem\u003eg\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.747, \u003cem\u003eSE\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.102, CI (0.547, 0.947), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05]. A meta-regression analysis consisting of six moderators resulted in statistically significant effects of study design, intervention agent, and type of dependent variable. Implications of study findings and suggestions for future research are discussed.\u003c/p\u003e","manuscriptTitle":"A Meta-Analysis of Reading Interventions for Students with Emotional/Behavioral Disorders","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-01 16:29:05","doi":"10.21203/rs.3.rs-5362938/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-17T17:06:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-20T17:17:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"275502138835824246161655996476557149984","date":"2024-11-18T20:10:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-17T23:37:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-02T10:05:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-02T10:04:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Behavioral Education","date":"2024-10-30T18:04:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-behavioral-education","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jobe","sideBox":"Learn more about [Journal of Behavioral Education](http://link.springer.com/journal/10864)","snPcode":"10864","submissionUrl":"https://submission.springernature.com/new-submission/10864/3","title":"Journal of Behavioral Education","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c6e1b8e6-cd01-4a75-9fc5-c97ebe5aa4a3","owner":[],"postedDate":"December 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-22T06:51:42+00:00","versionOfRecord":{"articleIdentity":"rs-5362938","link":"https://doi.org/10.1007/s10864-025-09597-5","journal":{"identity":"journal-of-behavioral-education","isVorOnly":false,"title":"Journal of Behavioral Education"},"publishedOn":"2025-09-13 15:56:54","publishedOnDateReadable":"September 13th, 2025"},"versionCreatedAt":"2024-12-01 16:29:05","video":"","vorDoi":"10.1007/s10864-025-09597-5","vorDoiUrl":"https://doi.org/10.1007/s10864-025-09597-5","workflowStages":[]},"version":"v1","identity":"rs-5362938","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5362938","identity":"rs-5362938","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}