A Meta-Analysis of Self-Determination Interventions for Students with Emotional and Behavioral Disorders
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| Title: | A Meta-Analysis of Self-Determination Interventions for Students with Emotional and Behavioral Disorders |
|---|---|
| Language: | English |
| Authors: | Benjamin S. Riden (ORCID |
| Source: | Behavioral Disorders. 2026 51(3):160-176. |
| Availability: | SAGE Publications and Hammill Institute on Disabilities. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 17 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Information Analyses |
| Descriptors: | Self Determination, Intervention, Students with Disabilities, Behavior Disorders, Emotional Disturbances, Behavioral Science Research, Student Behavior, Evidence Based Practice |
| DOI: | 10.1177/01987429251400222 |
| ISSN: | 0198-7429 2163-5307 |
| Abstract: | Self-determination is a latent variable that has been conceptualized differently across academic domains. Due to the variability in the conceptualization of self-determination interventions, a thorough exploration of the approaches is needed. The purpose of this meta-analysis was to explore the literature-base on self-determination interventions to establish if the strategy is an evidence-based practice for students with emotional and behavioral disorders. We examined whether self-determination is an evidence-based practice by evaluating the risk of bias and quantitative evidence available for qualifying interventions. Although case-level effect sizes varied, the results indicate that self-determination interventions were associated with significant behavioral changes for students with emotional and behavioral disorders. However, approximately 12.5% of participants across studies had negative or negligible responses, suggesting the need to modify specific iterations based on student characteristics, environmental factors, and specific behavioral targets. The individual variability is consistent with the emphasis on individualization within special education and provides important guidance for teachers considering using the intervention to support students with emotional and behavioral disorders. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1502863 |
| Database: | ERIC |
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| FullText | Links: – Type: pdflink Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwFT1ZK3pWheiM6fq1c1wJvAAAAA4zCB4AYJKoZIhvcNAQcGoIHSMIHPAgEAMIHJBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDOQUW8smHEoL8OY2HwIBEICBmyawOhXBPYnafrr6RiIo1Th8n5GkRywfsZ990quIbNL-vCGw6hgQ2VpmxWjoibeI9ehMlB7KcY8nWN8Tnp_-NmGd5GTUECZSR9W_7zZo3s0fFhbSrEgXpSdox7S62ruKJnvnLdCUHqZw8bpLjG-Qkm3SllcrjITg8F5M3I0bT9KEv8X97uXHYk4nvzh_MagPQgjLBe5ttPzdtvip Text: Availability: 1 Value: <anid>AN0192953820;bhd01may.26;2026Apr15.01:33;v2.2.500</anid> <title id="AN0192953820-1">A Meta-Analysis of Self-Determination Interventions for Students With Emotional and Behavioral Disorders </title> <p>Self-determination is a latent variable that has been conceptualized differently across academic domains. Due to the variability in the conceptualization of self-determination interventions, a thorough exploration of the approaches is needed. The purpose of this meta-analysis was to explore the literature-base on self-determination interventions to establish if the strategy is an evidence-based practice for students with emotional and behavioral disorders. We examined whether self-determination is an evidence-based practice by evaluating the risk of bias and quantitative evidence available for qualifying interventions. Although case-level effect sizes varied, the results indicate that self-determination interventions were associated with significant behavioral changes for students with emotional and behavioral disorders. However, approximately 12.5% of participants across studies had negative or negligible responses, suggesting the need to modify specific iterations based on student characteristics, environmental factors, and specific behavioral targets. The individual variability is consistent with the emphasis on individualization within special education and provides important guidance for teachers considering using the intervention to support students with emotional and behavioral disorders.</p> <p>Keywords: emotional and behavioral disorders; meta-analysis; registered report; self-determination</p> <hd id="AN0192953820-2">Introduction</hd> <p>Since the inception of the Education for All Handicapped Children Act of 1975, now the Individuals with Disabilities Education Improvement Act ([<reflink idref="bib25" id="ref1">25</reflink>]), students with emotional and behavioral disorders (EBD) have been provided with special education services ([<reflink idref="bib89" id="ref2">89</reflink>]). Students with EBD require these services because behavior inherent to their diagnosis (e.g., inability to foster and maintain relationships with peers and teachers, demonstrate inappropriate behaviors or feelings) affects their educational outcomes in two domains: (a) in-school and (b) postsecondary. For example, students with EBD often struggle academically in school as compared with their peers without disabilities and students with other high-incidence disabilities ([<reflink idref="bib32" id="ref3">32</reflink>]; [<reflink idref="bib98" id="ref4">98</reflink>]). Despite the increased attention to the needs of students with EBD (i.e., academically, behaviorally, emotionally, and socially), students with EBD continue to struggle academically ([<reflink idref="bib37" id="ref5">37</reflink>]); in fact, the academic performance of these students worsens with age ([<reflink idref="bib77" id="ref6">77</reflink>]).</p> <p>Many of these poor academic outcomes are exacerbated by several environmental factors often unaddressed by teachers and school staff, including (a) adverse relationships; (b) alienation; (c) harsher discipline; and (d) teasing, bullying, and gang involvement ([<reflink idref="bib55" id="ref7">55</reflink>]). The inherent challenges experienced by students with EBD contribute to one of the lowest graduation rates among students with disabilities (60%; [<reflink idref="bib85" id="ref8">85</reflink>]). This is in comparison to students with other disabilities: (a) autism spectrum disorder (71%); (b) deaf-blindness (68%); (c) hearing impairment (82%); (d) orthopedic impairment (63%); (e) other health impairment (75%); (f) specific learning disability (77%); (g) speech or language impairment (85%); (h) traumatic brain injury (75%); and (i) visual impairment (82%). For some students with EBD who do graduate, their postsecondary outcomes do not improve ([<reflink idref="bib11" id="ref9">11</reflink>]; [<reflink idref="bib49" id="ref10">49</reflink>]).</p> <p>Compared with other students with high-incidence disabilities, research indicates that students with EBD have lower levels of participation in postsecondary education. For example, [<reflink idref="bib49" id="ref11">49</reflink>] reported that only 53% of students with EBD participated in postsecondary education; the lowest among students with high-incidence disabilities (e.g., specific learning disability [67%], speech or language impairment [67%]). Furthermore, for those students with EBD who participated in postsecondary educational programming, only 35% earned a diploma, which took upward to 8 years to earn ([<reflink idref="bib11" id="ref12">11</reflink>]). This is the lowest among the disability categories besides orthopedic impairment (35%) and deaf-blindness (28%; [<reflink idref="bib49" id="ref13">49</reflink>]). In addition, students with EBD reported significantly lower levels of attaining some form of postsecondary employment (91%) up to 8 years post-high school as compared with other high-incidence disability categories (e.g., specific learning disability [95%], speech or language impairment [94%]), with only 50% reporting employment at time of the interview ([<reflink idref="bib49" id="ref14">49</reflink>]).</p> <p>According to youths involved in the juvenile justice system have a substantially higher prevalence of psychiatric disorders compared with those in the general population, with 45% to 66% of males and 45% to 73% of females in the system meeting the criteria for one or more psychiatric disorders. Anxiety, mood, and behavioral disorders are prevalent. Substance use disorders, which are the most common, affect up to 50% of males and 22% to 46% of females. Comorbid psychiatric disorders are also common; 34% of males and 60% of females in juvenile detention facilities have three or more disorders. Furthermore, 35% to 50% of incarcerated youth have special education needs, of those, roughly 47% have an EBD ([<reflink idref="bib24" id="ref15">24</reflink>]; [<reflink idref="bib61" id="ref16">61</reflink>]). [<reflink idref="bib82" id="ref17">82</reflink>] "In the absence of effective interventions, behavior patterns become more firmly established and less amenable to intervention efforts" ([<reflink idref="bib32" id="ref18">32</reflink>], p. 44), highlighting the need for schools to address skills that may help to curb these negative outcomes. One intervention effective for ameliorating the poor in-school and postsecondary outcomes of students with EBD is self-determination instruction.</p> <hd id="AN0192953820-3">Review of Self-Determination</hd> <p>Self-determination is a latent construct with varying conceptualizations both within and across fields based on the paradigmatic view of the research group (e.g., [<reflink idref="bib13" id="ref19">13</reflink>], [<reflink idref="bib14" id="ref20">14</reflink>]; [<reflink idref="bib20" id="ref21">20</reflink>]; [<reflink idref="bib47" id="ref22">47</reflink>]; [<reflink idref="bib51" id="ref23">51</reflink>]; [<reflink idref="bib76" id="ref24">76</reflink>]; [<reflink idref="bib87" id="ref25">87</reflink>]; [<reflink idref="bib90" id="ref26">90</reflink>]; [<reflink idref="bib93" id="ref27">93</reflink>]). Across these groups, reoccurring patterns of self-determined behaviors reported in the literature include goal setting, developing plans and implementing action plans in service to freely chosen goals, problem solving, evaluating progress toward goal attainment, and adjusting one's behaviors based on the evaluation ([<reflink idref="bib41" id="ref28">41</reflink>]; [<reflink idref="bib42" id="ref29">42</reflink>]; [<reflink idref="bib65" id="ref30">65</reflink>]; [<reflink idref="bib75" id="ref31">75</reflink>]). It is beyond the scope of this paper to critique the various operational definitions and measurement approaches for self-determination; however, we aim to emphasize the heterogeneity in its definition as it informed our methodology for this project.</p> <p>Research suggests when students with disabilities are provided systematic instruction to acquire behaviors linked to self-determination and the opportunity to receive systematic support to generalize those self-determined behaviors, that capacity building and opportunity is linked to in-school academic achievement ([<reflink idref="bib23" id="ref32">23</reflink>]; [<reflink idref="bib28" id="ref33">28</reflink>]; [<reflink idref="bib36" id="ref34">36</reflink>]; [<reflink idref="bib73" id="ref35">73</reflink>]) and positive postsecondary outcomes, including employment and independent living ([<reflink idref="bib39" id="ref36">39</reflink>]; [<reflink idref="bib75" id="ref37">75</reflink>]; [<reflink idref="bib92" id="ref38">92</reflink>]; [<reflink idref="bib95" id="ref39">95</reflink>]), leisure and recreation ([<reflink idref="bib45" id="ref40">45</reflink>]), and more positive life satisfaction and overall quality of life ([<reflink idref="bib29" id="ref41">29</reflink>]; [<reflink idref="bib52" id="ref42">52</reflink>]; [<reflink idref="bib72" id="ref43">72</reflink>]; [<reflink idref="bib96" id="ref44">96</reflink>]).</p> <p>Although bolstering the self-determined behaviors through instruction has been recognized as integral for ensuring positive in-school and postsecondary outcomes for all students with disabilities ([<reflink idref="bib71" id="ref45">71</reflink>]), research suggests students with EBD receive inadequate instruction on behaviors linked to self-determination ([<reflink idref="bib6" id="ref46">6</reflink>]; [<reflink idref="bib57" id="ref47">57</reflink>]). However, the literature also cites the importance of self-determination and the association with positive in-school and postsecondary outcomes for students with EBD (e.g., [<reflink idref="bib26" id="ref48">26</reflink>]). Thus, when students with EBD receive instruction in self-determination, positive in-school and postsecondary outcomes take place, including (a) academic goal attainment and writing achievement ([<reflink idref="bib22" id="ref49">22</reflink>]); (b) access to the general education curriculum ([<reflink idref="bib35" id="ref50">35</reflink>]); (c) attainment of educationally relevant goals and increased levels of self-determination ([<reflink idref="bib91" id="ref51">91</reflink>]); (d) decrease in disruptive behavior ([<reflink idref="bib44" id="ref52">44</reflink>]); (e) increased levels of on-task behaviors, with a decrease in off-task behaviors ([<reflink idref="bib26" id="ref53">26</reflink>]); and (f) daily living and employment skills ([<reflink idref="bib1" id="ref54">1</reflink>]).</p> <hd id="AN0192953820-4">Establishing an Evidence Base</hd> <p></p> <hd id="AN0192953820-5">Rational and Purpose</hd> <p>For the better part of two decades, special education researchers have emphasized the importance of evidence-based practices (EBP; [<reflink idref="bib8" id="ref55">8</reflink>]; [<reflink idref="bib53" id="ref56">53</reflink>]) while other have highlighted the need for the field to recenter its approach to evidence-based practice (see [<reflink idref="bib78" id="ref57">78</reflink>]; [<reflink idref="bib80" id="ref58">80</reflink>]). Evidence-based practice focuses on (a) research to inform classroom practice with evidence of effectiveness (i.e., our current focus); (b) teacher expertise in the selection of practices, qualitative adaptions to fit student needs, and data-based decision making; and (c) stakeholder (e.g., student, teacher, parent) acceptability of the practice. In the current project, we aimed to provide evidence for the first strand of the EBP practice framework by evaluating the risk of bias and the strength of quantitative evidence for self-determination interventions. The current review is warranted because of advances in review methods since the previous examination of the research-base. That is, while [<reflink idref="bib5" id="ref59">5</reflink>] laid the underpinnings of self-determination interventions for students with EBD, the review was published before the Council for Exceptional Children's (CEC) Standards for Evidence-Based Practices in Special Education ([<reflink idref="bib10" id="ref60">10</reflink>]) were created; thus, the research team did not make a determination of whether self-determination interventions met criteria to be determined an EBP. Evidence-based practices should be conceptualized as revolving around scientific evidence, practitioner expertise, and client preference ([<reflink idref="bib80" id="ref61">80</reflink>]); thus, our current project attempted to address the scientific evidence portion of the EBP process. We, therefore, aimed to extend [<reflink idref="bib5" id="ref62">5</reflink>] review by (a) updating the electronic search (i.e., last search ended in 2008); (b) assessing research methodological quality using CEC's Standards for Evidence-Based Practices in Special Education (2014); (c) using meta-analytic techniques to evaluate the heterogeneity of intervention effects; and (d) investigating potential moderators to identify "by whom," "for whom," and "under what conditions" self-determination interventions are effective in supporting students with EBD in school settings.</p> <hd id="AN0192953820-6">Possible Moderating Variables</hd> <p>We would be remiss not to mention the <emph>a priori</emph> moderator variables we plan to analyze, which include age and implementor of the intervention (i.e., researcher vs. teacher). The research literature suggests early childhood is a paramount time for students to develop cognitive, emotional, language, and social skills ([<reflink idref="bib30" id="ref63">30</reflink>]). Given the developmental nature of many behaviors linked to self-determination ([<reflink idref="bib74" id="ref64">74</reflink>]; [<reflink idref="bib94" id="ref65">94</reflink>]), it seems early childhood is also an important period for the acquisition of these behaviors; thus, leading to more self-determined adults ([<reflink idref="bib19" id="ref66">19</reflink>]). However, even with this knowledge, children may have less opportunities to practice using these learned behaviors associated with self-determination outside the experimental parameters of the intervention that take place within the "clinical" setting. Therefore, we hypothesize the effect of a self-determination intervention on behaviors linked to self-determination outcomes of students with EBD is moderated by age. That is, older students with EBD will have better outcomes than younger students with EBD.</p> <p>Finally, we explore interventionist (i.e., researcher vs. teacher) as a potential moderator. The literature suggests, in many cases, that interventions implemented by researchers produce better outcomes than those implemented by teachers (e.g., [<reflink idref="bib12" id="ref67">12</reflink>]; [<reflink idref="bib54" id="ref68">54</reflink>]; [<reflink idref="bib58" id="ref69">58</reflink>]). Therefore, we hypothesize the effect of a self-determination intervention on self-determination outcomes of students with EBD is moderated by who implements the self-determination intervention. Specifically, we anticipate seeing better outcomes when researchers implement versus when teachers implement a self-determination intervention.</p> <hd id="AN0192953820-7">Research Questions</hd> <p>The following research questions guided our inquiry:</p> <p></p> <ulist> <item> What are the characteristics (i.e., student demographics, environmental context, intervention context, implementation context) of the studies that implemented a self-determination intervention?</item> <p></p> <item> What is the quality of the evidence base on self-determination interventions for students with EBD, as measured by CEC's Standards for Evidence-Based Practices in Special Education (2014), and what components of studies have highest or lowest adherence to those standards?</item> <p></p> <item> What is the average treatment effect of self-determination interventions on outcomes for students with EBD, and how much heterogeneity is identified in treatment effects across studies?</item> <p></p> <item> We will examine additional variables, in an exploratory fashion, that may present themselves such as does the participant age explain variance in intervention effects across studies?</item> </ulist> <hd id="AN0192953820-8">Method</hd> <p>To increase the internal and external validity of findings from this systematic review and meta-analysis, we incorporated several elements into our methodology. First, we used [<reflink idref="bib38" id="ref70">38</reflink>] guidelines for conducting and reporting findings from systematic reviews in the field of EBD. Second, we engaged in open science practices: (a) pre-registered methodology; (b) reported all data from the systematic search, screening, coding, data extraction, and inter-rater agreement; (c) provided all materials used as part of the study (i.e., coding guide with operational definitions, quality appraisal rubric); and (d) provided codes from the statistical analyses used as part of the meta-analytic process. To access materials related to this meta-analysis (e.g., coding, tables) visit https://osf.io/gv58a/?view_only=26bdb65a7170465b832853609a28ef9d.</p> <hd id="AN0192953820-9">Study Identification</hd> <p></p> <hd id="AN0192953820-10">Search Procedures</hd> <p>We conducted our search after receiving in-principal acceptance of the Stage-1 manuscript as part of the registered report process. Every step of the process, along with corresponding data, is reported in a flow diagram (see Figure 1). The following databases were included in our search: (a) <emph>Academic Search Complete</emph> (Academic Search Elite, Academic Search Premier); (b) <emph>Psychology and Behavioral Sciences Collection</emph>; (c) <emph>Education Resources Information Center</emph> (ERIC); (d) <emph>PsycARTICLES</emph>; and (e) <emph>PsycINFO</emph>. In addition, we captured gray literature (i.e., unpublished manuscripts) by searching <emph>ProQuest Dissertations &amp; Theses Global</emph> (i.e., dissertations and theses), <emph>EdArXiv</emph> (i.e., preprints in education), and <emph>PsyArXiv</emph> (i.e., preprints in psychology). We searched each database separately using the following Boolean string: AB(emotional disturbance OR behavior disorders OR behavior problems OR antisocial behavior OR antisocial personality disorder OR conduct disorder OR at risk populations OR oppositional defiant disorder OR mental disorder OR aggressive behavioral OR juvenile delinquency, OR juvenile justice) AND AB(choice making or decision making OR problem solving OR goal setting OR goal attainment of self-knowledge OR self-advocacy OR self advocacy OR leadership OR self-management OR self management OR self-monitoring OR self monitoring OR self-regulation OR self regulation OR self-awareness OR self awareness OR self-efficacy OR self efficacy OR self-graphing OR self graphing OR locus of control). No limiters were used (e.g., date range, type of publication, language). After the search was complete, all duplicates were removed.</p> <p>DIAGRAM: Figure 1. Flow Diagram of Search Process.</p> <hd id="AN0192953820-11">Inclusion Criteria</hd> <p>The following inclusion criteria were used to select papers or this meta-analysis: (<reflink idref="bib1" id="ref71">1</reflink>) published in English; (<reflink idref="bib2" id="ref72">2</reflink>) used an experimental group design (i.e., randomized control trial, cluster randomized control trial, quasi-experimental) or used an experimental single-case design (e.g., multiple-baseline design across participants, behaviors, or settings; ABAB design); (<reflink idref="bib3" id="ref73">3</reflink>) implemented a self-determination intervention; (<reflink idref="bib4" id="ref74">4</reflink>) included students that qualified for special education services under the emotional disturbance category of [<reflink idref="bib25" id="ref75">25</reflink>]); (<reflink idref="bib5" id="ref76">5</reflink>) had a primary diagnosis of EBD from a psychiatrist according to the <emph>Diagnostic and Statistical Manual of Mental Disorders</emph> and/or at-risk for EBD (see [<reflink idref="bib7" id="ref77">7</reflink>]); (<reflink idref="bib6" id="ref78">6</reflink>) at least one student with or at-risk for EBD identified in single-case research design studies or unique data were provided for students identified as having or at-risk for EBD in group design studies; (<reflink idref="bib7" id="ref79">7</reflink>) the dependent variable(s) related to student classroom behavior (e.g., on-task/off-task, engagement, disruptive/challenging behavior), academic behavior, or social skills; and (<reflink idref="bib8" id="ref80">8</reflink>) students were in PreK-12th grade in an academic setting (i.e., public school, private school, preschool, alternative school, juvenile detention center, summer school programs, school-affiliated transition program).</p> <hd id="AN0192953820-12">Exclusion Criteria</hd> <p>Studies were excluded if they (a) were written in a language other than English; (b) used a non-experimental design (e.g., one group pre-posttest design, qualitative, systematic review, practitioner article, survey research); (c) used a single-case design that did not fit requirements for our data analytic plan (e.g., alternating treatments design, adaptive alternating treatments design, changing criterion design, repeated acquisition design); (d) implemented interventions that were not considered to be self-determination strategies (e.g., does not include one of the following components: setting goals, developing plans and implementing actions plans in service to freely chosen goals, problem solving, evaluating progress toward goal attainment, and adjusting based on the evaluation ([<reflink idref="bib41" id="ref81">41</reflink>]; [<reflink idref="bib42" id="ref82">42</reflink>]; [<reflink idref="bib65" id="ref83">65</reflink>]; [<reflink idref="bib91" id="ref84">91</reflink>]); (e) included no student with or at-risk for EBD in a single-case research design or did not provide unique data for students at-risk or identified with EBD in group design; (f) the dependent variable was unrelated to challenging/disruptive behavior, academic behavior, or social skills; and/or (g) the intervention was implemented solely in a setting outside a school environment (e.g., home, clinic).</p> <hd id="AN0192953820-13">Article Selection</hd> <p>We screened titles and abstracts to eliminate studies that were unrelated to our purpose. Following this step, we screened full texts using our inclusion criteria. As we moved through each step, we noted in a spreadsheet the reason why a study was eliminated so we could report this information. Once the final sample of studies were identified, we conducted an ancestral search (i.e., reference list of each included study), first author search (i.e., found all articles published by first author of included studies), forward search (i.e., found all studies citing the included studies), and journal hand search (i.e., looked through table of contents for each journal that published one of our included studies) to identify potential articles for inclusion in our analysis.</p> <hd id="AN0192953820-14">Data Extraction</hd> <p>A coding manual was developed based on [<reflink idref="bib63" id="ref85">63</reflink>] and was used to extract data from identified studies. The following characteristics were extracted: (a) study author; (b) study year; (c) intervention name and description; (d) number of intervention elements; (e) number of participants; (f) participant characteristics; (g) setting characteristics; (h) outcome variable; (i) graphical representation of data; (j) interventionist; (k) publication type; (l) publisher; (m) funding source; and (n) descriptive statistics.</p> <p>Data extraction for the meta-analysis portion of the project consisted of the following steps. First, for single-case research designs (SCRD) we used <emph>Web Plot Digitizer 4.3</emph> ([<reflink idref="bib64" id="ref86">64</reflink>]) to extract all data reported in time-series graphs. The data were extracted and stored in an Excel spreadsheet at the case level (with each phase identified). For group design studies, unique data must be reported for students with EBD. The mean, standard deviation, and sample size for students with EBD were collected for pre- and post-tests across both the comparison and treatment groups and across all measures. If this information was not reported, we contacted the corresponding author to request this data. If this data was no longer available, we extracted relevant statistical information reported (e.g., <emph>F</emph> values, <emph>T</emph> values) to estimate an effect size using the Campbell Collaboration effect size calculator (https://campbellcollaboration.org/escalc/html/EffectSizeCalculator-SMD-main.php).</p> <hd id="AN0192953820-15">Inter-Rater Agreement</hd> <p>We evaluated inter-rater agreement (IRA) at each stage of the process: (a) title/abstract screening; (b) full text screening; (c) ancestral search screening; (d) methodology assessment; (e) visual analysis for SCRD; (f) coding; and (g) digitizing data from SCRD graphs. A research team member was identified as the primary coder, and a research team member was identified as the secondary coder. Before conducting the search, we conducted training on the screening and coding procedures.</p> <p>After training, coders engaged in independent practice with an outcome of 82% agreement. Despite the positive IRA, we decided to include a booster training session to clarify certain codes (i.e., interventionist, repository, publisher) in the manual. Results of this booster session increased IRA to 91%. The secondary coder needed to meet a 90% agreement criterion before independent coding. During independent coding the primary coder evaluated 100% of documents and the secondary coder evaluated a minimum of 30% of documents. To calculate IRA, we used the following formula (agreements/ [agreements + disagreements]) × 100.</p> <hd id="AN0192953820-16">Visual Analysis of Single-Case Research Designs</hd> <p>To systematize our visual analysis, we created a coding guide with operational definitions. When engaging in visual analysis, several characteristics of the data patterns were examined to determine the extent to which a meaningful change in the behavior occurred and the extent to which this change could be attributed to the independent variable ([<reflink idref="bib27" id="ref87">27</reflink>]). The first step involved evaluating within phase (i.e., baseline, intervention) data characteristics: level, trend, and variability. The second step involved evaluating between phase comparisons (i.e., baseline to intervention): immediacy of effect, level change, overlapping data, changes in trend, and changes in variability ([<reflink idref="bib9" id="ref88">9</reflink>]). These elements were used to provide an overall evaluation for each SCRD: (a) weak; (b) moderate; or (c) strong (i.e., this aligns with the What Works Clearinghouse Pilot Single-Case Design Standards, Version 4.0).</p> <p>To support our decision making regarding visual analysis, we used [<reflink idref="bib2" id="ref89">2</reflink>] visual analysis tool for SCRD to detect the presence of a functional relation between phases of the identified studies. The visual analysis tool is a spreadsheet that guides users through the visual analysis process to help determine whether a functional relation is present and how confident the user can be in that determination. The tool prompts users to consider overlap between conditions, differentiation between conditions, and the magnitude and trend of differentiation ([<reflink idref="bib40" id="ref90">40</reflink>]).</p> <hd id="AN0192953820-17">Level, Trend, and Variability</hd> <p>We calculated the mean and median for each condition in each included study. This allowed us to determine if baseline level was of concern and to evaluate level changes between baseline and intervention. We used a split middle approach to determine the direction and magnitude of trend within each phase ([<reflink idref="bib31" id="ref91">31</reflink>]). To evaluate the variability within each phase, we set a criterion that 80% of data must be within 20% of the trend line.</p> <hd id="AN0192953820-18">Immediacy of Effect</hd> <p>We compared the last three data points from baseline to the first three data points from intervention, with a specific focus on level change and changes in trend.</p> <hd id="AN0192953820-19">Overlapping Data Points</hd> <p>We used the percentage of non-overlapping data to determine the percentage of intervention data points that overlap with the most extreme baseline datum ([<reflink idref="bib68" id="ref92">68</reflink>]).</p> <hd id="AN0192953820-20">Visual Analysis Agreement</hd> <p>The first and last author conducted independent visual analysis on the graphed data collected for each participant to determine if there was a functional relation. This was done by examining level changes, trend, variability, overlap, and immediacy of change. The first author holds a PhD in special education and is a board-certified Behavior Analyst at the doctoral level. The last author holds a master's degree in applied behavior analysis.</p> <hd id="AN0192953820-21">Data Analytic Plan</hd> <p></p> <hd id="AN0192953820-22">Single-Case Research Designs</hd> <p>We used the log response ratio (LRR) to determine magnitude of intervention effects ([<reflink idref="bib60" id="ref93">60</reflink>]). The LRR effect size has several desirable qualities that make it appropriate for SCRD: (a) is interpretable by various stakeholders when converted to percent change, (b) is insensitive to different behavior measurement procedures for the dependent variable, and (c) can be applied to various types of designs. The LRR was computed for each AB phase contrast for each participant. We anticipated a majority of experiments employed a multiple-baseline or multiple-probe design across participants; thus, each participant would produce one AB phase contrast. We computed the average LRR to include in the model using the approach designed by [<reflink idref="bib60" id="ref94">60</reflink>] if we identified included studies using a multiple-baseline or multiple-probe design across settings or behaviors or an ABAB design:</p> <p>Let R2 1 and R2 2 denote the estimates for the first and second pair of phases, with corresponding sampling variances VR1 and VR2. The composite effect size estimate is calculated as R2 = (R2 1 + R2 2)/2, with sampling variance estimate VR = (VR1 + VR2)/4. (p. 105)</p> <p>We used a multilevel model with effect size estimates at Level 1, participants at Level 2, and the study at Level 3. The base model provided an overall effect size estimate of self-determination interventions across our three outcome categories (i.e., academic, behavioral, social). However, we were also interested in investigating potential moderator variables. We included the following Level 2 predictors: (a) age of participant and (b) ethnicity of participant. We also included the following Level 3 predictors: (a) CEC Quality appraisal rating; (b) interventionist; (c) setting of intervention; and (d) number of self-determination components embedded in the intervention (i.e., one component self-determination interventions vs. multi-component self-determination intervention, respectively). The estimated LRR effect sizes were modeled using a random effects multilevel approach to account for heterogeneity ([<reflink idref="bib48" id="ref95">48</reflink>]). To model the effect size estimates, the metafor package in the R statistical environment was used ([<reflink idref="bib86" id="ref96">86</reflink>]). To account for the autocorrelation among outcome data, we used a cluster robust variance estimation technique that accounts for smaller samples (CRVE; [<reflink idref="bib84" id="ref97">84</reflink>]) using a robust variance estimation ([<reflink idref="bib59" id="ref98">59</reflink>]).</p> <hd id="AN0192953820-23">Group Design</hd> <p>A multivariate random effects model with robust variance estimation was used because (a) we anticipated dependent effect sizes based on multiple measures to evaluate intervention effects within each construct (i.e., academic, behavior, social) and (b) this approach allows us to account for this dependency and provided a more conservative estimate of the effect. Robust variance estimation was used to enhance the model against misspecification. A multivariate random effects model requires several decisions to be made. First,τ estimates based on the standard deviation of the distribution of true study-average effect size were estimated and we use REML to estimate τ. Second, we provided an estimate ρ of that is the correlation between the residuals in study-level effects and assumed this correlation to be 0.60. Last, we used CR2 as the estimator approach for cluster robust variance estimation.</p> <p>To evaluate heterogeneity of study-level effects, we interpreted three statistics: (a) <emph>Q</emph>-statistic; (b) Tau<sups>2</sups>; and (c) the prediction interval. The <emph>Q</emph>-statistic can have insufficient power in cases of small number of studies and can be overpowered when a large sample of studies are included in the meta-analysis. Tau<sups>2</sups> provides an estimation for the variance of study-level effects across the "population" of included studies in the review. Last, we interpreted the 95% prediction interval. The prediction interval reflects the plausible interval a potential new study's effect size would fall given the distribution of currently included study-level effects; thus, a narrow interval reflects less heterogeneity, and a wider interval reflects more heterogeneity.</p> <p>We anticipated heterogeneity to be present and aimed to explain this variability by using meta-regression. We included the following predictors in our meta-regression model: (a) age of participants; (b) CEC Quality appraisal rating; (c) interventionist; (d) setting of intervention; and (e) number of self-determination components embedded in the intervention.</p> <hd id="AN0192953820-24">Methodological Appraisal</hd> <p>To evaluate the methodological quality of articles included in this meta-analysis, we adhered to standards presented in the group comparison and SCRD quality indicator matrix using CEC's 2014 standards—the <emph>Standards Overview and Walk-Through Guide</emph> ([<reflink idref="bib66" id="ref99">66</reflink>]). For brevity, we do not define each indicator and the subcomponents but instead refer readers to open-source materials provided by [<reflink idref="bib66" id="ref100">66</reflink>]. We assessed each article for eight quality indicators. Included within the eight quality indicators are 28 components, with 24 pertaining to group design studies and 22 pertaining to SCRD studies. When assessing, we inserted a 0 when a quality indicator was not met, a 1 when a quality indicator was met, and a NA when a quality indicator was not applicable.</p> <hd id="AN0192953820-25">Publication Bias</hd> <p>To proactively address publication bias, we actively sought gray literature by searching through databases that published dissertations and theses and through preprint databases (e.g., <emph>EdArXiv</emph>, <emph>PsyArXiv</emph>). Once the final sample of studies were identified, we constructed a funnel plot for group designs and SCRDs with estimated effect per study along the horizontal axis and standard error of the estimate along the vertical axis. We engaged in visual analysis of the funnel plot to inspect for asymmetry. In addition, we conducted a statistical test using Egger's regression test ([<reflink idref="bib18" id="ref101">18</reflink>]) to identify if publication bias was affecting the sample of studies included in the meta-analysis. Both the funnel plot and Egger's regression were generated using the tutorial provided by [<reflink idref="bib16" id="ref102">16</reflink>].</p> <hd id="AN0192953820-26">Results</hd> <p>The authors found 88 articles meeting inclusion criteria, 78 single-case and 10 group experimental articles. The following review will report results by design type for participants, settings, dependent variables, IRA, data analysis, and methodological appraisal.</p> <hd id="AN0192953820-27">Participants</hd> <p></p> <hd id="AN0192953820-28">Single Case Research Design</hd> <p>Across all qualifying SCRD articles (<emph>n</emph> = 78), 349 participants were reported to have participated in studies that implemented a variation of self-determination (e.g., self-management, goal setting, self-reinforcement). Of the SCRD studies, 91% (<emph>n</emph> = 68) reported participants' gender. 96% of all SCRD-reported participant ages (<emph>n</emph> = 72). A far fewer number of studies (50%; <emph>n</emph> = 40) reported participant race/ethnicity. This meta-analysis focused on self-determination interventions with students with EBD. Therefore, all studies included at least one participant identified as receiving services under the emotional disturbance category in [<reflink idref="bib25" id="ref103">25</reflink>].</p> <hd id="AN0192953820-29">Group Design</hd> <p>Across all qualifying group design articles (<emph>n</emph> = 10), 485 participants were reported to have participated in studies that implemented a variation of self-determination (e.g., self-management, goal setting, and self-reinforcement). All group design studies reported participants' gender and race. All but one study ([<reflink idref="bib79" id="ref104">79</reflink>]) failed to report participant ages; instead, the authors reported grade level. This meta-analysis focused on self-determination interventions with students with EBD.</p> <hd id="AN0192953820-30">Settings</hd> <p>Strategies associated with self-determination were implemented in a variety of settings. Of the 88 included studies, interventions were implemented in a vocational school (<emph>n =</emph> 1; 1.1%), special education classroom with no designation (<emph>n</emph> = 2; 2.2%), summer school (<emph>n =</emph> 2; 2.2%), separate spaces in school setting such as an office or conference (<emph>n</emph> = 4; 4.5%), resource classroom (<emph>n</emph> = 5; 5.6%), multiple settings (<emph>n</emph> = 10; 11%), alternative school settings (<emph>n</emph> = 11; 12.5%) general education (<emph>n</emph> = 12; 13.6%), alternative settings (e.g., hospital, residential; <emph>n</emph> = 12; 13.6%), and lastly, self-contained classrooms (<emph>n =</emph> 29; 32.9%).</p> <hd id="AN0192953820-31">Dependent Variables</hd> <p>A variety of dependent variables were identified across the articles included for review. Dependent variables were coded into four categories: academic behavior, disruptive behavior, self-determined outcomes, and social skills. Of the 88 articles included in this review, disruptive behavior was identified as the most frequent dependent variable (<emph>n</emph> = 61; 69.3%), followed by academic behaviors (<emph>n</emph> = 55; 62.5%), and self-determined outcomes (<emph>n</emph> = 54; 61.3%). The lowest frequency of dependent variables identified across qualifying articles was social skills (<emph>n</emph> = 15; 17.04%).</p> <hd id="AN0192953820-32">Inter-Rater Agreement</hd> <p>Inter-rater agreement was calculated for eight separate steps during the article procurement and coding stages. Specifically, we calculated IRA for full text screening (IRA = 95%), ancestral search screening (IRA = 100%), methodology assessment (IRA = 100%), visual analysis for SCRD (IRA = 92%), coding (IRA = 98%), and digitizing data from SCRD graphs (IRA = 100%).</p> <hd id="AN0192953820-33">Data Analysis</hd> <p></p> <hd id="AN0192953820-34">Single-Case Research Design</hd> <p></p> <hd id="AN0192953820-35">Visual Analysis</hd> <p>Of the studies included in this meta-analysis (<emph>n</emph> = 88), 78 of them were conducted using an SCRD. The most recent study investigating a component of self-determination was published by [<reflink idref="bib15" id="ref105">15</reflink>], who examined self-monitoring. The earliest study identified was published by Warrenfeltz and colleagues in [<reflink idref="bib88" id="ref106">88</reflink>], who were also investigating self-monitoring interventions. Aligned with traditional data analysis approaches surrounding SCRD, we conducted a systematic visual analysis of each SCRD study. After visually analyzing each SCRD included in the analysis, there was a functional relation for 158 participants. There were 174 participants; we were unable to determine if a functional relation existed between the dependent variables and the independent variables. We were unable to conduct a visual analysis on one study ([<reflink idref="bib62" id="ref107">62</reflink>]) because no graph was provided.</p> <hd id="AN0192953820-36">Case-Level Effect Size Analysis</hd> <p>A total of 152 case-level effect sizes were calculated using Log Response Ratios for Improvement (LRRi), examining the effects of self-determination interventions on target behaviors for students with EBD. Effect sizes ranged from −0.64 to 4.58, with a mean effect size of 0.78 (<emph>SD</emph> = 0.73), indicating generally positive intervention effects. The majority of participants (<emph>n</emph> = 133, 87.5%) demonstrated positive effect sizes, suggesting that self-determination interventions were associated with improvements in target behaviors for most students. Notably large effect sizes (LRRi &gt; 2.0) were observed in several studies, including [<reflink idref="bib46" id="ref108">46</reflink>], [<reflink idref="bib26" id="ref109">26</reflink>], [<reflink idref="bib43" id="ref110">43</reflink>], and [<reflink idref="bib56" id="ref111">56</reflink>], with the largest individual effect size of 4.58 observed for Phillip in the [<reflink idref="bib56" id="ref112">56</reflink>] study.</p> <p>For behaviors targeted for decrease, 47 case-level effect sizes were calculated using Log Response Ratios for Decrease (LRRd), with effect sizes ranging from 1.13 to −3.06. The mean LRRd effect size was −0.81 (<emph>SD</emph> = 0.73), indicating that self-determination interventions were generally effective in reducing problematic behaviors. Approximately 70% of participants showed meaningful decreases in target behaviors (negative LRRd values), with particularly strong effects observed in studies by [<reflink idref="bib50" id="ref113">50</reflink>], [<reflink idref="bib17" id="ref114">17</reflink>], and [<reflink idref="bib3" id="ref115">3</reflink>]. The pattern of results across both analyses suggests that self-determination interventions were effective in promoting positive behavioral outcomes while simultaneously reducing problematic behaviors among students with EBD, though considerable variability exists in individual responses to these interventions.</p> <hd id="AN0192953820-37">Multilevel Meta-Analysis</hd> <p>A multilevel meta-analysis was conducted to examine the overall effectiveness of interventions and identify potential moderators of treatment effects. The analysis employed cluster robust standard errors to account for dependencies among effect sizes within studies. The overall intercept model revealed a small negative effect size (β = −0.139, <emph>SE</emph> = 1.426, <emph>p</emph> =.925), indicating no significant overall intervention effect across all included studies. This suggests considerable heterogeneity in intervention outcomes that warranted further investigation through moderator analyses.</p> <hd id="AN0192953820-38">Publication Bias</hd> <p>The evaluation of publication bias in this meta-analysis was assessed through visual inspection of funnel plots for both LRRi and LRRd effect sizes. The funnel plot for improvement outcomes (LRRi) shows substantial asymmetry, with a clear gap in the lower-left quadrant where studies with small sample sizes (high standard errors) and small or negative effect sizes would be expected. The distribution is heavily skewed toward positive effect sizes, with several studies showing exceptionally large effects (LRRi &gt; 4.0) that appear as extreme outliers.</p> <p>The funnel plot for decreased outcomes (LRRd) displays a more complex pattern of asymmetry, with the majority of studies clustered in the lower portion of the plot (indicating effective behavior reduction) but with notable gaps in areas where studies with minimal effects would typically appear. The concentration of studies showing strong positive intervention effects in both plots, combined with the absence of studies in regions indicating null or negative findings, strongly suggests publication bias favoring studies with significant, positive results. This pattern is consistent with tendencies in educational intervention research, where studies demonstrating clear positive effects are more likely to be published than those showing equivocal results. Furthermore, publication bias presents particular challenges for single-case experimental design research, as the relative scarcity of gray literature in behavior science research can exacerbate these biases ([<reflink idref="bib16" id="ref116">16</reflink>]). These visual indicators of publication bias should be considered when interpreting the overall effectiveness estimates, as the true population effect may be smaller than observed in this analysis.</p> <hd id="AN0192953820-39">Moderator Analyses</hd> <p>Several potential moderators were examined to understand sources of heterogeneity in intervention effects. Outcome category emerged as a notable moderator, with behavioral outcomes showing a small positive effect (β = 0.399, <emph>SE</emph> = 1.339, <emph>p</emph> =.796) and other outcome types demonstrating a moderate positive effect (β = 0.717, <emph>SE</emph> = 1.379, <emph>p</emph> =.644), though neither reached statistical significance. Age showed a small positive association with intervention effectiveness (β = 0.044, <emph>SE</emph> = 0.034, <emph>p</emph> =.211), suggesting that interventions may be slightly more effective for older participants, though this effect was not statistically significant.</p> <p>Ethnicity emerged as a significant moderator of intervention effects. Notably, Bosnian participants showed significantly reduced intervention effectiveness compared with the reference group (β = −0.429, <emph>SE</emph> = 0.126, <emph>p</emph> =.075), approaching statistical significance. Black participants also demonstrated reduced effectiveness (β = −0.353, <emph>SE</emph> = 0.174, <emph>p</emph> =.177), while other ethnic groups showed varying patterns of response. European American participants showed notably reduced effectiveness (β = −0.465, <emph>SE</emph> = 0.316, <emph>p</emph> =.169), while Hispanic participants showed minimal differences from the reference group (β = −0.059, <emph>SE</emph> = 0.064, <emph>p</emph> =.438). Caucasian participants also showed reduced effectiveness (β = −0.151, <emph>SE</emph> = 0.075, <emph>p</emph> =.131), while White participants showed a small positive effect (β = 0.066, <emph>SE</emph> = 0.526, <emph>p</emph> =.910).</p> <p>Implementation characteristics, including whether the interventionist was a researcher (β = −0.020, <emph>SE</emph> = 0.287, <emph>p</emph> =.945) and the number of intervention components (β = −0.030, <emph>SE</emph> = 0.046, <emph>p</emph> =.555), showed no significant associations with intervention effectiveness. The CEC rating, measuring implementation quality, also showed no significant relationship with outcomes (β = 0.221, <emph>SE</emph> = 0.269, <emph>p</emph> =.429). The multilevel meta-analysis revealed substantial heterogeneity in intervention effects, with no significant overall effect detected. Moderator analyses suggested that participant ethnicity may be an important factor influencing intervention effectiveness, with some ethnic groups showing differential response patterns. However, most moderator effects did not reach conventional levels of statistical significance, indicating that the sources of heterogeneity in intervention effects remain largely unexplained by the variables examined in this analysis.</p> <hd id="AN0192953820-40">Group Design</hd> <p></p> <hd id="AN0192953820-41">Overall Effect Size and Heterogeneity</hd> <p>We included eight group design studies comprising 112 effect sizes in the meta-analytic model. The multivariate meta-analysis revealed a statistically significant large mean effect size (<emph>g</emph> = 1.37, <emph>SE</emph> = 0.43, <emph>t</emph> = 3.19, <emph>df</emph> = 6.95, <emph>p</emph> =.015, 95% CI [0.52, 2.21]). We observed substantial heterogeneity across studies, with the Q-statistic reaching statistical significance (<emph>Q</emph> = 548.91, <emph>df</emph> = 111, <emph>p</emph> &lt;.001). Between-study variance reached 1.29 (<emph>τ</emph> = 1.14) and within-study variance reached 0.48 (<emph>τ</emph> = 0.69). The 95% prediction interval ranged from −1.24 to 3.98, confirming considerable variability in intervention outcomes and justifying investigation of potential moderating variables.</p> <hd id="AN0192953820-42">Publication Bias Assessment</hd> <p>We assessed potential publication bias in the group design studies using both trim-and-fill analysis and Egger's regression test. The trim-and-fill method estimated zero missing studies on the left side of the distribution (<emph>SE</emph> = 1.84), indicating no evidence of missing studies with smaller effect sizes. Consequently, the bias-adjusted effect size estimate remained at 1.91 (95% CI [0.59, 3.23]), identical to the original estimate. However, Egger's regression test revealed significant funnel plot asymmetry (<emph>z</emph> = 11.99, <emph>p</emph> &lt;.001), with the limit estimate as standard error approaches zero being -1.16 (95% CI [−1.53, −0.79]).</p> <p>The conflicting results between these two methods require careful interpretation. Trim-and-fill assumes publication bias follows a pattern where small studies with non-significant results are systematically missing but estimating zero missing studies suggests the observed asymmetry does not conform to this classic pattern. Egger's test detects any funnel plot asymmetry regardless of source, and the significant result indicates substantial asymmetry exists. This asymmetry may result from factors other than traditional publication bias, such as differences in study quality, heterogeneity in intervention effects across populations, or selective reporting within studies. The negative limit estimate (−1.16) suggests that smaller, less precise studies may report inflated effects compared with larger studies. Given these contradictory signals and the limited number of studies (<emph>n</emph> = 8), we interpret the overall effect size with appropriate caution while acknowledging that the nature and impact of potential bias in this literature remains unclear.</p> <hd id="AN0192953820-43">Moderator Analyses</hd> <p>We conducted comprehensive moderator analyses to examine sources of heterogeneity in intervention effects. Setting emerged as a significant moderator (<emph>F</emph> = 3.32, <emph>df₁</emph> = 3, <emph>df₂</emph> = 109, <emph>p</emph> =.023), with self-contained classrooms showing the largest effects (<emph>g</emph> = 1.45, <emph>SE</emph> = 0.58, <emph>p</emph> =.015), separate schools showing marginal significance (<emph>g</emph> = 1.85, <emph>SE</emph> = 0.95, <emph>p</emph> =.055), and general education settings showing minimal effects (<emph>g</emph> = 0.24, <emph>SE</emph> = 1.26, <emph>p</emph> =.850). We found age to be a significant moderator (<emph>F</emph> = 2.82, <emph>df₁</emph> = 3, <emph>df₂</emph> = 109, <emph>p</emph> =.042), with high school students showing the largest effects (<emph>g</emph> = 1.85, <emph>SE</emph> = 1.00, <emph>p</emph> =.067), followed by elementary students (<emph>g</emph> = 1.53, <emph>SE</emph> = 0.98, <emph>p</emph> =.121) and middle school students (<emph>g</emph> = 1.10, <emph>SE</emph> = 0.68, <emph>p</emph> =.110).</p> <p>Interventionist role demonstrated the strongest moderating effect (<emph>F</emph> = 4.45, <emph>df₁</emph> = 2, <emph>df₂</emph> = 107, <emph>p</emph> =.014), with researchers as interventionists producing significantly larger effect sizes (<emph>g</emph> = 1.63, <emph>SE</emph> = 0.63, <emph>p</emph> =.011) compared with teachers (<emph>g</emph> = 0.80, <emph>SE</emph> = 0.53, <emph>p</emph> =.134). The number of self-determination elements significantly moderated outcomes (<emph>F</emph> = 2.55, <emph>df₁</emph> = 4, <emph>df₂</emph> = 105, <emph>p</emph> =.044), with interventions containing seven elements showing the largest effects (<emph>g</emph> = 2.87, <emph>SE</emph> = 1.20, <emph>p</emph> =.019). We found that dependent variable type strongly influenced intervention effects (<emph>F</emph> = 11.82, <emph>df₁</emph> = 5, <emph>df₂</emph> = 107, <emph>p</emph> &lt;.001), with social skills outcomes showing the largest effects (<emph>g</emph> = 2.09, <emph>SE</emph> = 0.49, <emph>p</emph> &lt;.001), followed by behavioral outcomes (<emph>g</emph> = 1.86, <emph>SE</emph> = 0.45, <emph>p</emph> &lt;.001) and academic engagement (<emph>g</emph> = 0.92, <emph>SE</emph> = 0.46, <emph>p</emph> =.049). Gender composition showed a small but significant positive association with intervention effectiveness (β = 0.047, <emph>SE</emph> = 0.026, <emph>p</emph> =.069).</p> <hd id="AN0192953820-44">Methodological Quality Analysis</hd> <p>We analyzed methodological quality using CEC's Standards for Evidence-Based Practices in Special Education (2014) and observed significant overall variation (<emph>F</emph> = 2.89, <emph>df₁</emph> = 3, <emph>df₂</emph> = 109, <emph>p</emph> =.039). Studies that did not meet standards showed the largest effects (<emph>g</emph> = 1.69, <emph>SE</emph> = 0.70, <emph>p</emph> =.017), while studies that met standards with reservations showed moderate effects (<emph>g</emph> = 1.42, <emph>SE</emph> = 0.97, <emph>p</emph> =.146), and studies that fully met standards showed smaller effects (<emph>g</emph> = 0.79, <emph>SE</emph> = 0.95, <emph>p</emph> =.409). Post-hoc pairwise comparisons revealed no statistically significant differences between quality categories despite substantial effect size differences, with wide confidence intervals including zero for all comparisons: Does Not Meet versus Meets (−2.32, 6.40), Does Not Meet versus Reservations (−3.44, 3.55), and Reservations versus Meets (−2.34, 6.31). These findings remained consistent across multiple comparison adjustment methods and likely reflect limited sample sizes within each quality category and substantial within-group heterogeneity.</p> <p>The findings regarding methodological quality revealed an unexpected pattern contrary to typical expectations in intervention research. Studies with lower methodological quality demonstrated larger effect sizes, with studies that did not meet CEC standards showing the largest effects (<emph>g</emph> = 1.69), while studies that fully met standards showed the smallest effects (<emph>g</emph> = 0.79). This counterintuitive finding may reflect several factors, including the challenges of conducting rigorous experimental research in authentic educational settings with students with EBD, potential differences in study populations or interventions across quality categories, or the possibility that more rigorous studies provide more conservative effect estimates. The wide confidence intervals and lack of statistical significance in pairwise comparisons highlight the need for caution in interpreting these differences, particularly given the small sample sizes within each quality category.</p> <hd id="AN0192953820-45">Methodological Appraisal</hd> <p>Due to the number of studies included in the methodological appraisal (<emph>n</emph> = 88), we used two Excel spreadsheets of the <emph>Group Comparison and Single-Case Research Design Quality Indicator Matrix Using Council for Exceptional Children 2014 Standards</emph> ([<reflink idref="bib33" id="ref117">33</reflink>]; https://<ulink href="http://www.ci3t.org/practice">www.ci3t.org/practice</ulink>). The tool allows up to 70 studies to be appraised on one spreadsheet. Therefore, one sheet was used to appraise 66 studies, while the second was used to appraise the remaining 22 studies. The tool calculated the percentage of studies meeting each quality indicator component. Across appraisal components, 90% to 100% of the 66 studies had high adherence to CEC components. For a detailed explanation of which studies adhered to which CEC components, please visit the OSF repository for this study.</p> <p>In addition to the above, we aimed to analyze the relation between methodological quality and intervention effects. The methodological assessment for the group design studies ranged from 5.33 to 8.00. Of the 10 group design studies, two met all components of CEC's 2014 standards (i.e., 80% of quality indicators based on the 80% weighted coding method we employed; [<reflink idref="bib66" id="ref118">66</reflink>]), four met with reservations, and four did not meet. Variation in intervention effects was observed based on the quality appraisal of the group design studies (<emph>F</emph> = 2.89, <emph>df₁</emph> = 3, <emph>df₂</emph> = 109, <emph>p</emph> =.039). Despite differences in weighted effect sizes across the three levels of our quality appraisal, the post hoc moderator test did not indicate statistically significant differences between the three groups, which is likely due to the small sample size impacting statistical power. For the SCRD studies, the methodological assessment ranged from 4.20 to 8.00. Of the 78 SCRD studies, seven met all components of CEC's 2014 standards (i.e., 80% of quality indicators based on the 80% weighted coding method we employed; [<reflink idref="bib66" id="ref119">66</reflink>]), 36 met with reservations, and 35 did not meet. Variation in intervention effects was not observed based on the quality appraisal of the SCRD studies; therefore, methodological quality did not serve as a moderator.</p> <hd id="AN0192953820-46">Discussion</hd> <p></p> <hd id="AN0192953820-47">Single-Case Research Design</hd> <p></p> <hd id="AN0192953820-48">Case-Level Effect</hd> <p>The case-level effect size analyses provide compelling evidence for the effectiveness of self-determination interventions at the individual participant level, suggesting that these interventions can produce meaningful behavioral changes for students with EBD. The finding that 87.5% of participants showed positive improvements in target behaviors (LRRi &gt; 0) aligns with theoretical expectations from self-determination theory ([<reflink idref="bib67" id="ref120">67</reflink>]), which posits that when individuals develop greater autonomy, competence, and relatedness, they are more likely to engage in adaptive behaviors and academic tasks. The substantial effect sizes observed in several studies, particularly those exceeding 2.0, suggest that self-determination interventions can produce clinically significant changes for some students.</p> <p>This is particularly important for students with EBD, who often struggle with self-regulation, goal setting, and decision-making skills that are central to self-determined behavior ([<reflink idref="bib5" id="ref121">5</reflink>]). The effectiveness of these interventions in promoting positive behaviors while simultaneously reducing problematic behaviors supports the comprehensive nature of self-determination as a framework for addressing the complex needs of this population, consistent with meta-analytic findings that choice-making interventions result in clinically significant reductions in problem behavior ([<reflink idref="bib70" id="ref122">70</reflink>]).</p> <p>However, the considerable variability in individual responses (effect sizes ranging from negative to highly positive) highlights the importance of individualized approaches to self-determination instruction, a cornerstone principle of special education embodied in the individualized education program process. This variability may reflect differences in students' developmental readiness for self-determination skills, the severity of their emotional and behavioral challenges, or the degree to which interventions were tailored to individual needs and cultural contexts ([<reflink idref="bib52" id="ref123">52</reflink>]). The finding that approximately 12.5% of participants showed negative or minimal responses to intervention highlights the need for a more nuanced understanding of which students benefit most from self-determination interventions and under what conditions. This individual variability supports the emphasis in special education on individualized education programs and suggests that self-determination interventions may need to be adapted based on student characteristics, environmental factors, and specific behavioral targets to maximize effectiveness for all students with EBD ([<reflink idref="bib83" id="ref124">83</reflink>]). Furthermore, the pattern of results aligns with research demonstrating that self-determination interventions require careful consideration of individual student needs and systematic implementation to achieve optimal outcomes ([<reflink idref="bib97" id="ref125">97</reflink>]).</p> <hd id="AN0192953820-49">Multilevel Effect</hd> <p>The current findings of the multilevel meta-analysis reveal substantial heterogeneity in self-determination intervention effects without a significant overall effect, which aligns with broader patterns observed in meta-analytic research examining interventions for students with EBD. This heterogeneity likely reflects the complex nature of self-determination intervention research, where variability in participants, intervention components, and implementation contexts can produce meaningfully different outcomes across studies ([<reflink idref="bib5" id="ref126">5</reflink>]). Such heterogeneity should be expected rather than viewed as problematic, as it provides valuable information about the conditions under which self-determination interventions may be more or less effective for students with EBD. The absence of a significant overall effect (β = −0.139, <emph>p</emph> =.925) does not necessarily indicate that self-determination interventions are ineffective but rather suggests that intervention effects vary considerably across different contexts and populations, warranting careful examination of moderating factors.</p> <p>The most notable finding concerns the differential self-determination intervention effectiveness across ethnic groups, which is particularly concerning given the established importance of self-determination for post-school outcomes among students with EBD. [<reflink idref="bib5" id="ref127">5</reflink>] noted in their comprehensive review that relatively few self-determination intervention studies have addressed students from culturally diverse backgrounds, highlighting a critical gap in the literature. The present findings, showing reduced effectiveness for Bosnian (β = −0.429, <emph>p</emph> =.075), Black (β = −0.353, <emph>p</emph> =.177), and European American participants (β = −0.465, <emph>p</emph> =.169), may reflect the need for culturally responsive self-determination intervention approaches. Self-determination theory posits that all humans have universal basic psychological needs for autonomy, competence, and relatedness, yet research suggests that cultures differ in the extent to which they support the satisfaction of these needs ([<reflink idref="bib69" id="ref128">69</reflink>]). For students with EBD from diverse ethnic backgrounds, experiences of discrimination and cultural factors may significantly influence their responsiveness to traditional self-determination interventions. The pattern of results suggests that self-determination interventions for students with EBD may need to be tailored to address the unique cultural contexts and experiences of different ethnic groups to maximize effectiveness, particularly given the critical role that self-determination plays in transition outcomes for this population of students ([<reflink idref="bib97" id="ref129">97</reflink>]).</p> <hd id="AN0192953820-50">Group Design</hd> <p>A surprising finding from the group design literature was that we only identified eight studies that met the inclusion criteria—perhaps highlighting the difficulties in conducting group experimental design studies targeting this population of learners. The overall effect size evaluation raises many questions. The heterogeneity in effects was large, when interpreting the 95% CI (.52, 2.21) and the 95% prediction interval (−1.24, 3.98). These variations were likely not influenced by random error alone but rather by systematic differences across studies, which moderator analysis is well situated to investigate. Our preregistration outlined specific moderator variables we aimed to investigate from a theoretical and empirical basis. However, the limited sample size (i.e., eight group design studies) affected the statistical power and thus affects the confidence readers can place in interpreting the moderator analyses.</p> <p>While considering the caveats on the moderator analyses above, the results inform future research. Our evaluation of setting as a moderator indicated interventions occurring in the general education setting had much smaller intervention effects than interventions that occurred in self-contained special education settings or separate schools designed for students with disabilities. This is critical to consider as we see an emphasis on promoting access to the general education environment for students with EBD. However, there are several logistical challenges to promoting access—chief among them is that general educators often lack the knowledge and skills needed to implement research-based practices for students with disabilities, particularly those with EBD, which hinders the creation of effective learning environments. Special educators can promote self-determined behaviors through effective interventions occurring in separate settings; however, planning for generalization of these behaviors to new environments hinges on the new environment reinforcing these behaviors also known as naturally maintaining contingencies ([<reflink idref="bib81" id="ref130">81</reflink>]). That is, as a field we want to examine general education environments to ensure that they reinforce self-determined behaviors to ensure students with EBD receive as much of their instruction in the least restrictive environment as possible.</p> <p>The emphasis on educator knowledge and skills in implementing research-aligned practices is also relevant to our moderator test of the intervention agent. Studies that used researchers as the intervention agent had effects that were two-times larger than studies that used teachers as the intervention agent. This highlights that identifying effective interventions using researchers as intervention agents is an essential first step in the process to test the theoretical or empirical components of the intervention packages to ensure fidelity is controlled. However, this cannot be the final step because if the intervention cannot be implemented by educators, then it cannot be scaled. Research on how to effectively train educators to implement research-aligned practices is a necessary step to promote the scalability of EBP implementation.</p> <hd id="AN0192953820-51">Methodological Appraisal</hd> <p>Following established quality indicators when designing and implementing research has been shown to improve outcomes for students with disabilities (e.g., [<reflink idref="bib58" id="ref131">58</reflink>]). However, studies also highlight the challenges of applying these rigorous standards in school-based research settings (e.g., [<reflink idref="bib4" id="ref132">4</reflink>]; [<reflink idref="bib34" id="ref133">34</reflink>])—a key component of our inclusion criteria for this meta-analysis. For group design studies, we did observe variation in effect sizes across the three levels of our quality evaluation (i.e., Meets, Meets with Reservations, Does not Meet); however, the post hoc moderator test indicated the differences were not statistically significantly different, which was likely affected by the small sample size. Similarly, for SCRD studies, the methodological quality did not function as a moderator in intervention effectiveness. Most of the studies included in our analysis either met quality indicators with reservations or did not meet them at all, based on the 80% weighted coding method we employed. This finding aligns with trends in the existing literature, especially among studies investigating self-determination conducted in authentic educational settings (e.g., [<reflink idref="bib4" id="ref134">4</reflink>]; [<reflink idref="bib58" id="ref135">58</reflink>]). When conducting school-based research, it is important to strike a balance between methodological rigor and the realities of working within public education systems to ensure that findings remain applicable and contextually valid ([<reflink idref="bib21" id="ref136">21</reflink>]).</p> <hd id="AN0192953820-52">Limitations and Implications</hd> <p>Results of this meta-analysis should be interpreted with caution due to the following limitations. First, the magnitude of this meta-analysis should be considered a limitation. Despite the comprehensive search process, we may have missed articles during the initial electronic, hand, and ancestral search due to the broad construct that is self-determination. Next, some of the included reviews, especially those that contained group design studies, did not specify the precise number of students with EBD, the age of participants, setting information, and other relevant demographic information (e.g., gender and race). This made it impossible to report the precise number of participants in different contexts. For every instance and in every environment, we urge researchers to provide the precise number of participants and their disabilities, along with associated participants and setting characteristics. It is challenging to fully trust any material presented in evaluations that do not disclose their methods with reproducible accuracy in the absence of this transparency. Furthermore, we urge the field's scholars to keep conducting applied research in conventional educational environments and to explicitly describe the setting's features. Another limitation uncovered during this work is the design and employment of SCRD. For example, some studies did not include enough data points within phases to confidently make an assessment using visual analysis. We encourage authors conducting single-case research to adhere to the critical aspects of single-case design studies.</p> <hd id="AN0192953820-53">Conclusion</hd> <p>This meta-analysis highlights both the promise and the complexity of implementing self-determination interventions for students with EBD. Regarding the first leg of EBP evaluation, results suggest self-determination as a potential EBP for student with EBD, with the understanding that to qualify as an EBP additional evidence is needed. While case-level findings underscore the potential for meaningful individual change, variability in outcomes—particularly across methodological quality and cultural contexts—emphasizes the need for individualized, context-responsive approaches. The limited number of group design studies and the lack of statistically significant moderators suggest that more rigorous and transparent research is needed, particularly in authentic educational settings where real-world applicability is paramount. Moreover, differences in intervention effectiveness based on setting and interventionist underscore the critical role of educator training and systematic support in scaling EBP in the applied setting. Taken together, these findings emphasize the importance of designing self-determined interventions that are not only methodologically sound but also responsive and feasible for school-based implementation to support positive long-term outcomes for students with EBD.</p> <ref id="AN0192953820-54"> <title> References </title> <blist> <bibl id="bib1" idref="ref54" type="bt">1</bibl> <bibtext> Agran M., Wehmeyer M., Cavin M., Palmer S. (2010). 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Remedial and Special Education, 34(3), 154–165. https://doi.org/10.1177%2F0741932512448253</bibtext> </blist> </ref> <ref id="AN0192953820-55"> <title> Footnotes </title> <blist> <bibtext> Benjamin S. Riden</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0002-6733-1942 Corey Peltier</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0003-3138-4126 Art Dowdy</bibtext> </blist> <blist> <bibtext>Graph https://orcid.org/0000-0001-6466-6774</bibtext> </blist> <blist> <bibtext> The authors received no financial support for the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> </ref> <aug> <p>By Benjamin S. Riden; Joshua M. Pulos; Corey Peltier; Art Dowdy; Noah A. Wisnieski; Megan E. Bell; Alexandra P. Brandenberger; Jane E. Britton and Elisabeth R. Morris</p> <p>Reported by Author; Author; Author; Author; Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib25" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib89" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib32" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib98" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib37" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib77" firstref="ref6"></nolink> <nolink nlid="nl7" bibid="bib55" firstref="ref7"></nolink> <nolink nlid="nl8" bibid="bib85" firstref="ref8"></nolink> <nolink nlid="nl9" bibid="bib11" firstref="ref9"></nolink> <nolink nlid="nl10" bibid="bib49" firstref="ref10"></nolink> <nolink nlid="nl11" bibid="bib24" firstref="ref15"></nolink> <nolink nlid="nl12" bibid="bib61" firstref="ref16"></nolink> <nolink nlid="nl13" bibid="bib82" firstref="ref17"></nolink> <nolink nlid="nl14" bibid="bib13" firstref="ref19"></nolink> <nolink nlid="nl15" bibid="bib14" 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| Items | – Name: Title Label: Title Group: Ti Data: A Meta-Analysis of Self-Determination Interventions for Students with Emotional and Behavioral Disorders – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Benjamin+S%2E+Riden%22">Benjamin S. Riden</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6733-1942">0000-0002-6733-1942</externalLink>)<br /><searchLink fieldCode="AR" term="%22Joshua+M%2E+Pulos%22">Joshua M. Pulos</searchLink><br /><searchLink fieldCode="AR" term="%22Corey+Peltier%22">Corey Peltier</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-3138-4126">0000-0003-3138-4126</externalLink>)<br /><searchLink fieldCode="AR" term="%22Art+Dowdy%22">Art Dowdy</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6466-6774">0000-0001-6466-6774</externalLink>)<br /><searchLink fieldCode="AR" term="%22Noah+A%2E+Wisnieski%22">Noah A. Wisnieski</searchLink><br /><searchLink fieldCode="AR" term="%22Megan+E%2E+Bell%22">Megan E. Bell</searchLink><br /><searchLink fieldCode="AR" term="%22Alexandra+P%2E+Brandenberger%22">Alexandra P. Brandenberger</searchLink><br /><searchLink fieldCode="AR" term="%22Jane+E%2E+Britton%22">Jane E. Britton</searchLink><br /><searchLink fieldCode="AR" term="%22Elisabeth+R%2E+Morris%22">Elisabeth R. Morris</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Behavioral+Disorders%22"><i>Behavioral Disorders</i></searchLink>. 2026 51(3):160-176. – Name: Avail Label: Availability Group: Avail Data: SAGE Publications and Hammill Institute on Disabilities. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 17 – Name: DatePubCY Label: Publication Date Group: Date Data: 2026 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Information Analyses – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Self+Determination%22">Self Determination</searchLink><br /><searchLink fieldCode="DE" term="%22Intervention%22">Intervention</searchLink><br /><searchLink fieldCode="DE" term="%22Students+with+Disabilities%22">Students with Disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Behavior+Disorders%22">Behavior Disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Emotional+Disturbances%22">Emotional Disturbances</searchLink><br /><searchLink fieldCode="DE" term="%22Behavioral+Science+Research%22">Behavioral Science Research</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Behavior%22">Student Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Evidence+Based+Practice%22">Evidence Based Practice</searchLink> – Name: DOI Label: DOI Group: ID Data: 10.1177/01987429251400222 – Name: ISSN Label: ISSN Group: ISSN Data: 0198-7429<br />2163-5307 – Name: Abstract Label: Abstract Group: Ab Data: Self-determination is a latent variable that has been conceptualized differently across academic domains. Due to the variability in the conceptualization of self-determination interventions, a thorough exploration of the approaches is needed. The purpose of this meta-analysis was to explore the literature-base on self-determination interventions to establish if the strategy is an evidence-based practice for students with emotional and behavioral disorders. We examined whether self-determination is an evidence-based practice by evaluating the risk of bias and quantitative evidence available for qualifying interventions. Although case-level effect sizes varied, the results indicate that self-determination interventions were associated with significant behavioral changes for students with emotional and behavioral disorders. However, approximately 12.5% of participants across studies had negative or negligible responses, suggesting the need to modify specific iterations based on student characteristics, environmental factors, and specific behavioral targets. The individual variability is consistent with the emphasis on individualization within special education and provides important guidance for teachers considering using the intervention to support students with emotional and behavioral disorders. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2026 – Name: AN Label: Accession Number Group: ID Data: EJ1502863 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/01987429251400222 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 17 StartPage: 160 Subjects: – SubjectFull: Self Determination Type: general – SubjectFull: Intervention Type: general – SubjectFull: Students with Disabilities Type: general – SubjectFull: Behavior Disorders Type: general – SubjectFull: Emotional Disturbances Type: general – SubjectFull: Behavioral Science Research Type: general – SubjectFull: Student Behavior Type: general – SubjectFull: Evidence Based Practice Type: general Titles: – TitleFull: A Meta-Analysis of Self-Determination Interventions for Students with Emotional and Behavioral Disorders Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Benjamin S. Riden – PersonEntity: Name: NameFull: Joshua M. Pulos – PersonEntity: Name: NameFull: Corey Peltier – PersonEntity: Name: NameFull: Art Dowdy – PersonEntity: Name: NameFull: Noah A. Wisnieski – PersonEntity: Name: NameFull: Megan E. Bell – PersonEntity: Name: NameFull: Alexandra P. Brandenberger – PersonEntity: Name: NameFull: Jane E. Britton – PersonEntity: Name: NameFull: Elisabeth R. Morris IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 0198-7429 – Type: issn-electronic Value: 2163-5307 Numbering: – Type: volume Value: 51 – Type: issue Value: 3 Titles: – TitleFull: Behavioral Disorders Type: main |
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