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Profiles of Emotional Disturbance across the Five Characteristics of the Federal Definition

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Title: Profiles of Emotional Disturbance across the Five Characteristics of the Federal Definition
Language: English
Authors: Lambert, Matthew C., Katsiyannis, Antonis, Epstein, Michael H., Cullinan, Douglas, Sointu, Erkko
Source: Behavioral Disorders. Aug 2022 47(4):223-235.
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: http://sagepub.com
Peer Reviewed: Y
Page Count: 13
Publication Date: 2022
Document Type: Journal Articles
Reports - Research
Education Level: Elementary Secondary Education
Elementary Education
Secondary Education
Descriptors: Emotional Disturbances, Definitions, Federal Regulation, Student Characteristics, Heterogeneous Grouping, Disability Identification, Profiles, Students with Disabilities, Students, Elementary School Students, Secondary School Students
DOI: 10.1177/01987429211033567
ISSN: 0198-7429
Abstract: Ensuring the provision of a free, appropriate public education (FAPE) to students qualified for services under the disability category of emotional disturbance (ED) has been both challenging and controversial. Examining this population in light of the five characteristics listed in the federal definition may provide useful insights to address needs and improve outcomes. The purpose of this study was to use latent class analysis to examine profiles across the five characteristics of the federal definition of ED for a sample of 491 students school-identified with ED. Key findings include that (a) students with ED are a heterogeneous group with distinct and qualitatively different subgroups; (b) latent classes representing the severe problems and the externalizing problems typologies tended to consist of younger students; (c) greater proportions of Black, Hispanic, and English-language learner students were found in the severe and externalizing latent classes; and (d) students in the externalizing and severe latent classes spent more time in special education classrooms and had worse ratings on social maladjustment. The findings highlight important implications for practice in regard to assessment, program differentiation, and preservice teacher training. Research limitations and directions for future research are also discussed.
Abstractor: As Provided
Entry Date: 2022
Accession Number: EJ1343406
Database: ERIC
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  Value: <anid>AN0157637379;bhd01aug.22;2022Jun28.05:10;v2.2.500</anid> <title id="AN0157637379-1">Profiles of Emotional Disturbance Across the Five Characteristics of the Federal Definition </title> <p>Ensuring the provision of a free, appropriate public education (FAPE) to students qualified for services under the disability category of emotional disturbance (ED) has been both challenging and controversial. Examining this population in light of the five characteristics listed in the federal definition may provide useful insights to address needs and improve outcomes. The purpose of this study was to use latent class analysis to examine profiles across the five characteristics of the federal definition of ED for a sample of 491 students school-identified with ED. Key findings include that (a) students with ED are a heterogeneous group with distinct and qualitatively different subgroups; (b) latent classes representing the severe problems and the externalizing problems typologies tended to consist of younger students; (c) greater proportions of Black, Hispanic, and English-language learner students were found in the severe and externalizing latent classes; and (d) students in the externalizing and severe latent classes spent more time in special education classrooms and had worse ratings on social maladjustment. The findings highlight important implications for practice in regard to assessment, program differentiation, and preservice teacher training. Research limitations and directions for future research are also discussed.</p> <p>Keywords: emotional disturbance; behavioral; assessment; behavior(s)</p> <p>Ensuring the provision of a free, appropriate public education (FAPE) to students qualified for services under the disability category of emotional disturbance (ED) has been both challenging and controversial. In 2017, a total of 331,680 children and adolescents received special education and related services in ED, representing 0.5% of the school-age population ([<reflink idref="bib62" id="ref1">62</reflink>]). These students met the eligibility criteria set by states reflecting the five key characteristics<emph>—Inability to Learn, Relationship Problems, Inappropriate Behavior, Unhappiness or Depression</emph>, and <emph>Physical Symptoms or Fears</emph>—over a long period of time and to a marked degree that adversely affects educational performance. The eligibility requirements that states must address are stipulated in federal legislation known as the Individuals with Disabilities Education Improvement Act of [<reflink idref="bib34" id="ref2">34</reflink>] (IDEA). Several authorities have criticized the definition of ED as being outdated, the terminology is vague and ambiguous, and the definition is absent of research support (e.g., [<reflink idref="bib22" id="ref3">22</reflink>]; [<reflink idref="bib23" id="ref4">23</reflink>]; [<reflink idref="bib29" id="ref5">29</reflink>], [<reflink idref="bib30" id="ref6">30</reflink>]; [<reflink idref="bib53" id="ref7">53</reflink>]; [<reflink idref="bib65" id="ref8">65</reflink>]). Also, a particularly thorny issue has been the exclusionary clause regarding social maladjustments, typically viewed as arbitrary and limiting eligibility for at-risk students (see [<reflink idref="bib9" id="ref9">9</reflink>]; Epstein, [<reflink idref="bib14" id="ref10">14</reflink>]; [<reflink idref="bib48" id="ref11">48</reflink>]; [<reflink idref="bib53" id="ref12">53</reflink>]). However, the definition as written in IDEA has not significantly changed over several decades and yet is to be followed by state and local school administrators in the screening and identification of students with ED. Nonetheless, although the IDEA designation is ED, professionals in this disability category prefer designating this population as students with emotional and behavioral disorders.</p> <p>Along with the definitional challenges, there exists a question of the underidentification of students with ED. Numerous research and governmental papers report that upward of 20% of children in the United States at one time demonstrate behavioral challenges sufficient to meet criteria for a mental health disorder (e.g., Ghandour, 2019; [<reflink idref="bib35" id="ref13">35</reflink>]; [<reflink idref="bib52" id="ref14">52</reflink>]; [<reflink idref="bib60" id="ref15">60</reflink>]). However, only a relatively few of these children receive appropriate mental health services, and a much smaller number of school-age students receive special education services for ED. As stated, approximately 0.5% of school-age children are school-identified as ED and receive special education or related services under IDEA ([<reflink idref="bib52" id="ref16">52</reflink>]; [<reflink idref="bib54" id="ref17">54</reflink>]; [<reflink idref="bib62" id="ref18">62</reflink>]). Moreover, this rate has remained remarkably consistent ([<reflink idref="bib36" id="ref19">36</reflink>]).</p> <p>Students with ED are school-identified primarily because of their emotional and behavioral excesses and deficits. Moreover, these students have been exhibiting much poorer educational and life outcomes than their nondisabled peers and their counterparts with other educational disabilities ([<reflink idref="bib4" id="ref20">4</reflink>]; [<reflink idref="bib45" id="ref21">45</reflink>]; [<reflink idref="bib46" id="ref22">46</reflink>]; see also, [<reflink idref="bib56" id="ref23">56</reflink>]). Academically, students with ED exhibit serious levels of scholastic underperformance in all subject areas, and this lack of academic skills increases with age ([<reflink idref="bib69" id="ref24">69</reflink>]; see also, [<reflink idref="bib26" id="ref25">26</reflink>]; [<reflink idref="bib43" id="ref26">43</reflink>]; [<reflink idref="bib47" id="ref27">47</reflink>]; [<reflink idref="bib68" id="ref28">68</reflink>]). Language-related impairments are also prevalent among students with ED ([<reflink idref="bib32" id="ref29">32</reflink>]; see also [<reflink idref="bib8" id="ref30">8</reflink>]). Students with ED receive poor grades, experience numerous course failures, demonstrate high levels of behavior referrals, absenteeism, suspensions, and expulsions and quit school at much higher rates than their peers ([<reflink idref="bib4" id="ref31">4</reflink>]; [<reflink idref="bib62" id="ref32">62</reflink>]; [<reflink idref="bib70" id="ref33">70</reflink>]). As adults, students with ED exhibit high levels of unemployment, involvement with the criminal justice system, and substance abuse ([<reflink idref="bib36" id="ref34">36</reflink>]; [<reflink idref="bib70" id="ref35">70</reflink>]).</p> <p>Given the poor educational and life outcomes of students with ED and the apparent discrepancy between the number of school-age children with social, emotional, and behavioral challenges (i.e., 14%–20%) and the number of students school-identified with ED (i.e., 0.5 %), it is unfortunate that few researchers have examined students with ED on the five characteristics listed in the federal definition, including comorbidity across these characteristics. First, we know that comorbidity of mental disorders closely resembling definitional ED characteristics among children and adolescents is relatively common. For example, three of four children and adolescents ages 3 to 17 years with (a) depression also have anxiety (73.8%) and almost one in two have behavior problems (47.2%); (b) with anxiety, more than one in three also have behavior problems (37.9%) and about one in three also have depression (32.3%); and (c) with behavior problems, more than one in three also have anxiety (36.6%) and about one in five also have depression (20.3%; [<reflink idref="bib5" id="ref36">5</reflink>]; [<reflink idref="bib27" id="ref37">27</reflink>]). Second, studies involving students with ED have reported significant levels of comorbidity. For example, [<reflink idref="bib64" id="ref38">64</reflink>] reported on 441 children with EDs rated by teachers on a checklist of problem behaviors. Factor analysis of the intercorrelations of the behaviors indicated that the three factors of conduct problem or unsocialized aggression, inadequacy–immaturity, and personality problem or neuroticism accounted for 76% of the variance. Similarly, [<reflink idref="bib51" id="ref39">51</reflink>] also reported a 50% comorbidity between externalizing and internalizing problems among students with ED. Third, research on key characteristics in the definition points to gender effects on externalizing versus internalizing behaviors (see [<reflink idref="bib24" id="ref40">24</reflink>]; [<reflink idref="bib28" id="ref41">28</reflink>]), race /ethnicity effects ([<reflink idref="bib13" id="ref42">13</reflink>]), and age effects ([<reflink idref="bib31" id="ref43">31</reflink>]; [<reflink idref="bib74" id="ref44">74</reflink>]).</p> <p>Still, the systematic examination of the five characteristics is limited and dated (e.g., [<reflink idref="bib12" id="ref45">12</reflink>]). [<reflink idref="bib12" id="ref46">12</reflink>] identified 71.2% of the 1,352 students with ED as comorbid (90th percentile criterion) on the five dimensions of the federal definition. Comorbidity with an inability to learn and one more characteristic occurred in 42% of the students, 45.6% with relationship problems, 58.2% with inappropriate behavior, 52.4% with unhappiness and depression, and 56.2% with physical symptoms and fears. Related research on comorbidity also points to the complexity of addressing needs across disorders (see [<reflink idref="bib27" id="ref47">27</reflink>]). For example, is the presence of multiple disorders necessitating the deployment of specific interventions associated with each one of these disorders or points to the need to examine interactions among disorders and devise a holistic approach in intervening? Such comorbidity and related programming considerations are to be expected for students with ED receiving services under IDEA. If that is the case, then does the presence of multiple characteristics listed in the federal definition necessitate that Individualized Education Program (IEP) teams consider these characteristics independently in designing interventions or consider interactions among the behaviors and plan accordingly? In either case, the presence of multiple characteristics very likely exacerbates pathology and has implications for the effectiveness of interventions used. To further complicate this issue, gender, age, socioeconomic status (SES), and race/ethnicity effects may be involved. Finally, similar to mental health comorbidity (e.g., depression and anxiety), comorbidity of characteristics presents not only more acute problems but also more persistent, thus the necessity for early and robust intervention to ameliorate their effects (see [<reflink idref="bib42" id="ref48">42</reflink>]; [<reflink idref="bib59" id="ref49">59</reflink>]; [<reflink idref="bib67" id="ref50">67</reflink>]).</p> <p>Therefore, viewing students with ED as a heterogeneous group and considering co-occurrence across the five characteristics found in the federal definition seems warranted ([<reflink idref="bib12" id="ref51">12</reflink>]; [<reflink idref="bib32" id="ref52">32</reflink>]). Nonetheless, these questions may be premature in nature, given the paucity of research in this area. Having a better understanding of the characteristics of students with ED across the five definitional dimensions will provide the basis for designing interventions, a most pressing need given the dismal outcomes of this population.</p> <p>One assessment system specifically to meet the guidelines of the federal definition of ED and to assist school professionals in the identification of students with ED is the Scales for Assessing Emotional Disturbance–3 (SAED-3; [<reflink idref="bib19" id="ref53">19</reflink>]). The SAED-3 consists of several test instruments including the 45-item Rating Scale (SAED-RS) and was specifically designed for use by school personnel to identify students with ED. The SAED-3 RS is a standardized, norm-referenced instrument developed to operationalize the five primary characteristics and other essential aspects of the federal definition of ED. There is psychometric evidence that the scores from the SAED-3 RS have acceptable reliability and validity ([<reflink idref="bib14" id="ref54">14</reflink>]; Epstein, [<reflink idref="bib14" id="ref55">14</reflink>]; [<reflink idref="bib18" id="ref56">18</reflink>]; [<reflink idref="bib21" id="ref57">21</reflink>]). Moreover, the SAED-RS scores are accurate in discriminating students at risk of having ED across subgroups of students based on important demographic variables including age, sex, race/ethnicity, and student ability group ([<reflink idref="bib16" id="ref58">16</reflink>], [<reflink idref="bib17" id="ref59">17</reflink>]). The updated normative data of SAED-3 (see [<reflink idref="bib19" id="ref60">19</reflink>]) provide an excellent opportunity for studying the emotional and behavioral profiles for students with ED for several compelling reasons: (a) the rating scale mirrors the federal definition, (b) it is technically adequate, (c) it was recently normed on 1,430 students, and (d) it draws from a nationally representative sample. Therefore, the purposes of the study were to (a) conduct a latent class analysis (LCA) of a large sample of students who have been school-identified with ED to explore the heterogeneity (or homogeneity) of profiles across the five characteristics of the federal definition of ED and (b) examine how profiles relate to other student demographic characteristics such as age, gender, and race/ethnicity.</p> <hd id="AN0157637379-2">Method</hd> <p></p> <hd id="AN0157637379-3">Participants</hd> <p>Participants were 491 students identified with ED who had an active IEP. Students identified with ED and another disability or disorder (e.g., Learning Disorder and Attention-Deficit/Hyperactivity Disorder) were excluded from the study. The analytic sample had students with ED from each of the four major geographical regions of the United States: 34.2% from the Northeast, 24.2% from the Midwest, 30.8% from the South, and 10.8% from the West. The sample was predominately male (67.6%; <emph>n</emph> = 332). In terms of race and ethnicity, the sample was 55.2% White/non-Hispanic (<emph>n</emph> = 271), 28.3% Black/non-Hispanic (<emph>n</emph> = 139), 5.5% multiracial/non-Hispanic (<emph>n</emph> = 27), 3.3% other race (e.g., Asian, American Indian; <emph>n</emph> = 16), and 7.7% Hispanic (<emph>n</emph> = 38). Students ranged in age from 5 to 18 with a mean age of 13.6 years (<emph>SD</emph> = 3.52). Nearly 75% of the students were enrolled in public schools (<emph>n</emph> = 367)—data on school settings were unavailable for 12.4% of the sample (<emph>n</emph> = 61). Less than 5% of the sample was identified as English-language learners (ELL; <emph>n</emph> = 16).</p> <hd id="AN0157637379-4">Measures</hd> <p>The SAED-3 ([<reflink idref="bib19" id="ref61">19</reflink>]) is a suite of assessment instruments and procedures specifically developed to aid school personnel in the screening and identification of students with ED. The SAED system includes the Screener (10 items), the RS, the Developmental/Educational Questionnaire, which is an interview to be used with parents/caregivers, and the Observation Form, which is a direct observation format to collect behavioral observation in the classroom. Collectively, the SAED-3 approach ([<reflink idref="bib19" id="ref62">19</reflink>]) is a comprehensive system for use in the identification of students with ED and aid in determining eligibility for special education and related services through IDEA. The SAED-3 RS is a 45-item standardized, norm-referenced instrument developed to operationally define the five IDEA definition characteristics of ED (39 items): (A) Inability to Learn (e.g., <emph>Listening and note-taking skills are weak; Lacks interest, motivation</emph>, and <emph>positive attitude toward learning</emph>), (B) Relationship Problems (e.g., <emph>Has few or no friends</emph> and <emph>Avoids interacting with people</emph>), (C) Inappropriate Behavior (e.g., <emph>Verbally abuses, teases, taunts people</emph> and <emph>Physically assaults or fights people in school</emph>), (D) Unhappiness or Depression (e.g., <emph>Has feelings of worthlessness</emph> and <emph>Pessimistic about future</emph>), and (E) Physical Symptoms or Fears (e.g., <emph>Afraid of unlikely dangers</emph> and <emph>Harms own body</emph>). The SAED-RS also operationalizes the socially maladjusted concept found in the definition, as well as other parts of the IDEA definition of ED. Each item is rated on a 4-point Likert-type scale (0 = "<emph>not a problem</emph>," 1 = "<emph>mild problem</emph>," 2 = "<emph>considerable problem</emph>," and 3 = "<emph>severe problem</emph>") by a person familiar with the student's behavior for a minimum of 2 months. Items composing each of the subscales are summed to obtain a raw score that is transformed to a scaled score. Subscale scaled scores range from 1 to 20. Subscale scaled scores of 13 or below are described as <emph>not indicative of ED</emph>, scores of 14 to 16 are described as <emph>indicative of ED</emph>, and scores of 17 or above are described as <emph>highly indicative of ED</emph>. For this study, the five subscale scaled scores—(A) Inability to Learn, (B) Relationship Problems, (C) Inappropriate Behavior, (D) Unhappiness or Depression, and (E) Physical Symptoms or Fears—were used as indicators of the latent classes.</p> <p>A number of prior research studies have demonstrated evidence that the scores from previous editions of the SAED-RS meet acceptable standards of reliability and validity ([<reflink idref="bib14" id="ref63">14</reflink>]; [<reflink idref="bib18" id="ref64">18</reflink>], [<reflink idref="bib20" id="ref65">20</reflink>]). For the current edition of the SAED-RS, recent studies have documented test score reliability (e.g., internal consistency, interrater, and test–retest), evidence of validity based on internal structure, validity based on the relation to other variables, measurement invariance across racial and ethnic subgroups, and diagnostic utility (see [<reflink idref="bib19" id="ref66">19</reflink>]). In terms of internal structure, when teachers rate students identified with ED, the SAED-RS ratings are consistent with a bifactor structure with one general factor and five distinct group factors representing the five core SAED subscales ([<reflink idref="bib39" id="ref67">39</reflink>]). Furthermore, SAED-RS items have demonstrated adequate measurement invariance between White, Black, and Hispanic students, which suggests that scores are relatively unbiased and comparable across the three groups of students ([<reflink idref="bib40" id="ref68">40</reflink>]; [<reflink idref="bib41" id="ref69">41</reflink>]). Finally, SAED-RS scores adequately differentiate between students identified with ED, students identified with learning disabilities, and students without disabilities, further supporting the constructs measured by the SAED subscales ([<reflink idref="bib38" id="ref70">38</reflink>]).</p> <p>In addition to the five core SAED subscale scores representing the five characteristics of the federal definition of ED, student demographic characteristics and the social maladjustment scaled scores (from the SAED-3) were used in this study. Data on student characteristics included gender, age, race, ethnicity, ELL status, and the percentage of time spent in general education classrooms. These variables were compared across latent classes to examine whether latent classes differed in terms of student characteristics.</p> <hd id="AN0157637379-5">Data Collection</hd> <p>Data were collected as part of a large measurement study of the SAED-3. Prior to data collection, three university Internal Review Boards (University of Nebraska-Lincoln, University of Northern Colorado, and Elon University) approved sample recruitment and data collection procedures. Data were collected from fall 2015 through spring 2018. School personnel (i.e., general education teachers and special education teachers) were recruited by mail, email, or telephone. Individuals who agreed to participate were instructed on how to rate each of the items. Teachers were informed to rate students whom they had known for at least 2 months and also instructed to provide complete student demographic information. Teachers were asked to complete the SAED-RS on all students or to select a random, unbiased sample of their students. Teachers used the following instructions to select a sample of students: (a) decide how many students you wish to rate, (b) start either at the top or bottom of your class roster and select every other student to rate, and (c) stop selecting students when you reach the number you had decided to rate.</p> <hd id="AN0157637379-6">Data Analysis</hd> <p>Mplus v7.11 ([<reflink idref="bib58" id="ref71">58</reflink>]) was used to fit a series of LCA models to the set of SAED-RS scores. LCA ([<reflink idref="bib44" id="ref72">44</reflink>]) is an approach used to identify latent (i.e., unobserved) subgroups of a population (referred to as <emph>classes</emph>) by characterizing groups of individuals who demonstrate similar patterns of standing on a set of indicators (e.g., behavioral and emotional ratings). LCA has proven to be of substantive practical use in behavioral and educational sciences to explore unobserved heterogeneity within a population and as a statistical method for identifying profiles of at-risk individuals ([<reflink idref="bib10" id="ref73">10</reflink>]; [<reflink idref="bib57" id="ref74">57</reflink>]).</p> <p>The central question being addressed in this LCA is related to the number and nature of the latent classes. So in this case, LCA is primarily a descriptive-analytical approach—how much heterogeneity is present in the sample (i.e., number of different latent classes), and what characteristics cluster together to define the profiles (i.e., nature of the latent classes)? This was an exploratory analysis, and we did not have an a priori hypothesis about the number or nature of the latent classes underlying the data, so we extracted latent classes until the model indices indicated nontenable solutions.</p> <p>To help guide the enumeration and extraction of latent classes, we used three pieces of information from the models: (a) whether the best log-likelihood (LL) value was replicated, (b) Bayesian Information Criterion (BIC) (adjusted for sample size), and (c) the significance of the Lo–Mendell–Rubin likelihood ratio test (LRT). Research has shown that BIC and LRT are largely consistent with one another and tend to perform better than other metrics for determining class enumeration in LCA models ([<reflink idref="bib61" id="ref75">61</reflink>]). We also evaluated the overall entropy and univariate entropy (described below) of the models to assess the overall classification accuracy and the relative importance of each indicator, respectively.</p> <p>LCA models require the use of multiple starting values to find the global maxima of the likelihood function rather than the local maxima, so all of the LCA models were specified with 200 random starting values and 50 final-stage optimizations. In other words, each model was "replicated" 50 times using different starting values to evaluate the consistency of the LL function to determine whether the model parameters converged to the global maxima. Parameter estimates from models that do not replicate the maximum LL value are not trustworthy.</p> <p>The BIC is an absolute measure of model fit based on the likelihood function that allows a set of competing models to be compared with one another—in this case, models with different numbers of latent classes. Lower BIC indicates better model fit; however, there is no statistical test of goodness of fit for BIC or a test of whether BIC significantly differs between two competing models. The LRT represents a statistical test that assesses the degree to which a model with <emph>k</emph> classes fits the data more closely than a model with <emph>k</emph>–1 classes; a significant LRT indicates that the model with more classes fits the data more closely than the more parsimonious model. For example, in the case of a two-class model, a significant LRT statistic indicates that the two-class solution is a better fit to the data than a one-class model (i.e., a homogeneous population without clearly delineated subgroups).</p> <p>Entropy is a metric used to describe how well the indicators identify latent classes ([<reflink idref="bib2" id="ref76">2</reflink>]). Entropy ranges from 0 to 1 with values closer to 1 indicating greater delineation (i.e., separation) between classes. Entropy can be conceptualized as a measure of classification accuracy—values closer to 1 indicate more certainty of classification. The overall entropy indicates how well the entire set of indicators delineate the latent classes. The univariate entropy indicates the relative strength of each specific indicator in terms of delineating the latent classes.</p> <hd id="AN0157637379-7">Relation of latent classes to other variables</hd> <p>After identifying the number and nature of the latent classes, we examined the association between latent class membership and other variables (i.e., student demographics and social maladjustment) using the <emph>BCH</emph> method in Mplus to estimate the relation between latent classes and distal outcomes (Bakk & Vermunt, 2014). Specifically, the BCH approach tests for the equality of means across the latent classes. It is important to note that latent classes are, by definition, unobserved and therefore estimated with a degree of error that is not accounted for in a conventional analysis such as analysis of variance (Clark & Muthén, 2009). Therefore, we decided to conduct additional analyses in Mplus using the BCH approach because it accounts for the uncertainty of latent class membership rather than treating latent class membership as observed. For these analyses, we evaluated statistical significance at the.05 level.</p> <hd id="AN0157637379-8">Missing data</hd> <p>LCA as implemented in Mplus includes missing data by using maximum likelihood to estimate the model parameters. However, there were no missing data at the subscale level for the five core SAED scores. The BCH method as implemented in Mplus software uses a pairwise present deletion approach to handle missing data for the outcome. Therefore, if a student was missing race data, then that student was excluded from the follow-up analysis for race but included in the follow-up analyses for all other outcomes (e.g., gender and age) for which the student had valid data. As noted previously, data on social maladjustment were only collected for older students (≥ 12 years old), so 34.2% of participants (<emph>n</emph> = 168) were missing those data.</p> <hd id="AN0157637379-9">Results</hd> <p>Table 1 lists the descriptive statistics for SAED subscale scores. Scores for the SAED subscales ranged from a minimum of 7 to a maximum of 20 across the five subscales. Mean SAED subscale scores for the sample were 13.00 (<emph>SD</emph> = 3.77), 13.86 (<emph>SD</emph> = 3.87), 14.27 (<emph>SD</emph> = 4.58), 13.09 (<emph>SD</emph> = 4.02), and 13.48 (<emph>SD</emph> = 3.96) for the Inability to Learn, Inappropriate Behavior, Relationship Problems, Unhappiness or Depression, and Physical Symptoms subscales, respectively. Just over 43% of the sample scored in the <emph>indicative of ED</emph> or <emph>highly indicative of ED</emph> range on the Inability to Learn subscale; 52.7% on the Inappropriate Behavior subscale; 51.3% on the Relationship Problems subscale; 40.9% on the Unhappiness or Depression subscale; and 44.8% on the Physical Symptoms subscale.</p> <p>Graph</p> <p>Table 1. Descriptive Statistics for SAED Subscale Scores.</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Subscale</th><th align="center">Range</th><th align="center"><italic>M</italic> (<italic>SD</italic>)</th><th align="center">% indicative of ED</th><th align="center">% highly indicative of ED</th></tr></thead><tbody><tr><td>(A) Inability to Learn</td><td>7–20</td><td>13.00 (3.77)</td><td>21.4</td><td>22.0</td></tr><tr><td>(B) Relationship Problems</td><td>9–20</td><td>13.86 (3.87)</td><td>24.4</td><td>27.9</td></tr><tr><td>(C) Inappropriate Behavior</td><td>8–20</td><td>14.27 (4.58)</td><td>13.6</td><td>39.1</td></tr><tr><td>(D) Unhappiness or Depression</td><td>8–20</td><td>13.09 (4.02)</td><td>16.1</td><td>24.8</td></tr><tr><td>(E) Physical Symptoms</td><td>7–20</td><td>13.48 (3.96)</td><td>18.7</td><td>26.1</td></tr></tbody></table> </ephtml> </p> <p>1 <emph>Note.</emph> Scores between 14 and 16 are <emph>indicative of ED</emph> and scores ≥ 17 are <emph>highly indicative of ED</emph>. SAED = Scales for Assessing Emotional Disturbance; ED = Emotional Disturbance.</p> <p>The statistical results of the LCA models are reported in Table 2. The table includes whether the log likelihood of the model was replicated across random starting values (which indicate the model converged on the global maximum likelihood rather than a local maximum likelihood), BIC, LRT, and entropy values for the two-, three-, four-, five-, six-, and seven-class models. The log likelihood was replicated for all of the models, the BIC was lower for each of the successively complex models, and the LRTs were statistically significant at the.05 level for the two-, three-, four-, and five-class models, but not for the six- or seven-class models. Taken together, the results suggest that the five-class solution is likely the most tenable model with the maximum extraction of classes. In addition, the overall entropy value for the five-class model was.88, which can be considered relatively high (>.80; Clark & Muthén, 2009) although there is no generally accepted value that should be considered adequate. The univariate entropy was.16,.26,.58,.37, and.27 for the (A) <emph>Inability to Learn</emph>, (B) <emph>Relationship Problems</emph>, (C) <emph>Inappropriate Behavior</emph>, (D) <emph>Unhappiness or Depression</emph>, and (E) <emph>Physical Symptoms or Fears</emph> subscales, respectively.</p> <p>Graph</p> <p>Table 2. Results of the Latent Class Analyses.</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="center">Replicated LL</th><th align="center">BIC</th><th align="center">LRT</th><th align="center">Entropy</th></tr></thead><tbody><tr><td>Two-class model</td><td>Yes</td><td>13,057</td><td>826.54<xref ref-type="table-fn" rid="tfn3">***</xref></td><td>.87</td></tr><tr><td>Three-class Model</td><td>Yes</td><td>12,897</td><td>190.99<xref ref-type="table-fn" rid="tfn3">***</xref></td><td>.82</td></tr><tr><td>Four-class model</td><td>Yes</td><td>12,800</td><td>130.32<xref ref-type="table-fn" rid="tfn3">*</xref></td><td>.88</td></tr><tr><td>Five-class model</td><td>Yes</td><td>12,740</td><td>94.60<xref ref-type="table-fn" rid="tfn3">**</xref></td><td>.88</td></tr><tr><td>Six-class model</td><td>Yes</td><td>12,694</td><td>81.47</td><td>.90</td></tr><tr><td>Seven-class model</td><td>Yes</td><td>12,664</td><td>64.96</td><td>.88</td></tr></tbody></table> </ephtml> </p> <ulist> <item>2 <emph>Note.</emph> BIC = Bayesian Information Criterion; LL = log likelihood; LRT = likelihood ratio test.</item> <item>3 <emph>p</emph> <.05. **<emph>p</emph> <.01. ***<emph>p</emph> <.001.</item> </ulist> <p>Based on the statistical results of the LCA, the analysis appears to have identified five distinct and relevant profiles, which we qualitatively examined for substantive patterns of behavioral and emotional characteristics. Figure 1 shows the mean SAED subscales scores plotted for each latent class profile (the numerical values are also reported in Table 3). The first latent class consisted of 12.4% of the sample and, on average, were borderline indicative of the (A) Inability to Learn characteristic, not indicative of the (C) Inappropriate Behaviors characteristic, and indicative of the (B) Relationship Problems, (D) Unhappiness or Depression, and (E) Physical Symptoms characteristics. We labeled this first latent class as <emph>internalizing problems</emph>. The second latent class consisted of 25.3% of the sample and, on average, were neither borderline indicative nor indicative of any of the five characteristics. We labeled the second latent class as <emph>limited problems</emph>. The third latent class consisted of 18.3% of the sample and, on average, were borderline indicative of the (A) Inability to Learn and (C) Inappropriate Behavior characteristics and not indicative of the other three characteristics. We labeled the third latent class as <emph>borderline problems</emph>. The fourth latent class consisted of 20.8% of the sample, and, on average, were borderline indicative of the (A) Inability to Learn characteristic and the (B) Relationship Problems characteristic, highly indicative of the (C) Inappropriate Behavior characteristic, and not indicative of the other two characteristics. We labeled the fourth class as <emph>externalizing problems</emph>. The fifth latent class consisted of 23.2% of the sample and, on average, were indicative or highly indicative on all five of the characteristics. We labeled the fifth latent class as <emph>severe problems</emph>.</p> <p>Graph: Figure 1. Mean Scales for Assessing Emotional Disturbance subscale scores by latent class.</p> <p>Graph</p> <p>Table 3. Description of Latent Classes and Relation to Other Variables.</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left" rowspan="2">Descriptive label</th><th align="center">Class 1 (<italic>n</italic> = 61)</th><th align="center">Class 2 (<italic>n</italic> = 124)</th><th align="center">Class 3 (<italic>n</italic> = 90)</th><th align="center">Class 4 (<italic>n</italic> = 102)</th><th align="center">Class 5 (<italic>n</italic> = 114)</th></tr><tr><th align="center">Internalizing problems</th><th align="center">Limited problems</th><th align="center">Borderline problems</th><th align="center">Externalizing problems</th><th align="center">Severe problems</th></tr></thead><tbody><tr><td colspan="6">Characteristics of ED</td></tr><tr><td> (A) Inability to Learn</td><td>13.47</td><td>9.67</td><td>13.08</td><td>13.21</td><td>16.14</td></tr><tr><td> (B) Relationship Problems</td><td>16.80</td><td>10.47</td><td>11.84</td><td>14.04</td><td>17.48</td></tr><tr><td> (C) Inappropriate Behavior</td><td>10.42</td><td>8.86</td><td>13.52</td><td>18.65</td><td>19.11</td></tr><tr><td> (D) Unhappiness or Depression</td><td>16.99</td><td>9.69</td><td>11.04</td><td>11.31</td><td>17.95</td></tr><tr><td> (E) Physical Symptoms</td><td>16.46</td><td>10.30</td><td>11.76</td><td>12.55</td><td>17.60</td></tr><tr><td colspan="6">Student characteristics</td></tr><tr><td> Mean age</td><td>14.02</td><td>14.60</td><td>12.49</td><td>13.08</td><td>12.21</td></tr><tr><td> Male</td><td>83.0%</td><td>72.9%</td><td>64.6%</td><td>59.3%</td><td>64.0%</td></tr><tr><td> White/non-Hispanic</td><td>69.7%</td><td>64.0%</td><td>55.1%</td><td>45.4%</td><td>46.3%</td></tr><tr><td> Black/non-Hispanic</td><td>15.6%</td><td>20.6%</td><td>31.9%</td><td>36.7%</td><td>33.4%</td></tr><tr><td> Hispanic</td><td>8.3%</td><td>8.2%</td><td>4.1%</td><td>5.8%</td><td>11.7%</td></tr><tr><td> ELL</td><td>2.5%</td><td>1.3%</td><td>1.7%</td><td>2.5%</td><td>14.2%</td></tr><tr><td> More than 60% in general education</td><td>85.0%</td><td>84.2%</td><td>75.8%</td><td>75.4%</td><td>68.9%</td></tr><tr><td> Mean Social Maladjustment Score</td><td>7.47</td><td>7.19</td><td>7.35</td><td>10.99</td><td>12.39</td></tr></tbody></table> </ephtml> </p> <p>4 <emph>Note.</emph> ED = emotional disturbance; ELL = English Language Learner.</p> <p>After estimating and interpreting the five latent classes, we then evaluated how the latent classes were related to student demographics and social maladjustment scores. Student age significantly differed across the five latent classes at the.001 significance level. Students in the limited problems latent class were the oldest while students in the severe problems latent class were the youngest, on average. Student gender also significantly differed across the five latent classes at the.01 significance level. The percentage of male students was highest for the internalizing problems latent class and lowest for the externalizing problems latent class. Student race significantly differed across the five latent classes at the.01 significance level. The internalizing problems latent class had the highest percentage of White students and the lowest percentage of Black students, while the externalizing problems latent class had the lowest percentage of White students and the highest percentage of Black students. Ethnicity and ELL status were not statistically compared due to small sample sizes, but the severe problems latent class had the highest percentage of Hispanic students and ELL. The amount of time that students spent in general education learning environments also differed between some of the latent classes at the.05 significance level. For example, students in the externalizing problems latent class spent significantly less time in general education environments compared with students in the limited problems and internalizing problems latent classes. In terms of social maladjustment, the externalizing problems and severe problems latent classes had the highest scores, which were significantly different from the other three latent classes at the.001 significance level, but not significantly different from one another.</p> <hd id="AN0157637379-10">Discussion</hd> <p>The purposes of this study were to examine profiles across the five characteristics of the federal definition of ED of a large sample of school-identified students with ED, both in general and regarding how student demographics are related to these profiles. Key findings include that (a) students with ED comprise a heterogeneous group with distinct and qualitatively different subgroups, (b) latent classes representing the severe problems and the externalizing problems typologies tended to consist of younger students, (c) the percentage of male students was highest for the internalizing problems latent class and lowest for the externalizing problems latent class, (d) greater proportions of Black, Hispanic, and ELL students were found in the severe and externalizing latent classes, and (e) students in the externalizing and severe latent classes spent more time in special education classrooms and were rated as engaging in more social maladjustment behaviors.</p> <p>These findings are consistent with findings by [<reflink idref="bib12" id="ref77">12</reflink>] regarding comorbidity among the five characteristics of federal ED definition, particularly with regard to the most severe latent class. [<reflink idref="bib12" id="ref78">12</reflink>] found that approximately 22% of students demonstrated comorbidity across all five characteristics of ED, while the current study found that approximately 23% of students were identified in the severe latent class, which demonstrated significant problems across all five characteristics. The comorbidity findings are significant as the two studies and samples—[<reflink idref="bib12" id="ref79">12</reflink>] and the current study—are separated by over two decades which speaks to the consistency of the findings. Clearly, the comorbidity of behavioral challenges is an important marker variable for a large percentage of students with ED and warrants further study in the student population. Furthermore, these findings closely reflect work by [<reflink idref="bib64" id="ref80">64</reflink>] who analyzed teacher ratings of problems of several hundred students with "behavior disorders." They found three dimensions of problems that they called Conduct Disorder (including disobedience, hyperactivity, and disrespectfulness), Inadequacy-Immaturity (including dislike of school, daydreaming, and easily misled), and Personality Problem (including withdrawal, depression, and fearfulness). Similarly, using data from teacher ratings, parent ratings, and student self-reports, [<reflink idref="bib1" id="ref81">1</reflink>] classified the mental health problems of young people into two broadband types of behavior problems: externalizing (aggression and rule-breaking problems) and internalizing (anxiety, depression, withdrawal, and physical malfunctioning). The researchers also identified three smaller groupings of problems related to interpersonal malfunctioning, severely maladaptive thought, and poor attention.</p> <p>Another interesting finding is that one-quarter of the current study sample, which was labeled <emph>limited problems</emph>, was not rated as indicative of ED on any of the five characteristics. According to the federal definition of ED, students need to exceed their non-ED peers on one or more of the five characteristics to be identified as ED. The present results indicate that about 25% of school-identified students with ED did not meet this threshold, which suggests that these students may have been misidentified by school professionals or that while no single characteristic led to the identification, it may have been a cumulative effect across multiple characteristics. A third possible explanation is that these students did present with one or more scores indicative of ED when they were identified; however, after receiving special education services, they no longer rated as indicative of ED. Clearly, additional research is warranted to explore this group of students with ED and what variable(s) led to their identification.</p> <p>The role of age was also consistent with prior research in that younger students tended to be classified in the severe problems and externalizing problems latent classes. For example, [<reflink idref="bib27" id="ref82">27</reflink>] found that the prevalence of behavioral/conduct problems peaking in middle childhood (ages 6–11 years), whereas depression and anxiety problems were most common among adolescents (ages 12–17 years). This finding may be interpreted by the tendency of ED verification practices favoring comorbid students and students with more acute problems/externalizing behaviors. Indeed, students exhibiting externalizing behaviors are more likely to capture the attention of teachers, be referred for eligibility under IDEA, and eventually found eligible for special education and related services. In addition, what may be the reasons for older students to display less severe and externalizing behaviors? Perhaps, one explanation is related to the effectiveness in programming (e.g., socializing the aggressive–disruptive kids to school or students becoming more homogeneous as the result of disproportionate rates of dropping out and delinquency, for example, the most defiant, disruptive, aggressive students with ED).</p> <p>Similarly, race/ethnicity differences were observed across the latent classes with a greater proportion of Hispanic, Black, and ELL students found in the severe and externalizing latent classes (see [<reflink idref="bib12" id="ref83">12</reflink>]; Cullinan et al., 2004). This finding is particularly disturbing, as these groups have been reported to have already a host of negative academic and behavioral outcomes ([<reflink idref="bib11" id="ref84">11</reflink>]; [<reflink idref="bib25" id="ref85">25</reflink>]; [<reflink idref="bib62" id="ref86">62</reflink>]; [<reflink idref="bib66" id="ref87">66</reflink>]; [<reflink idref="bib72" id="ref88">72</reflink>]). However, these findings must be viewed with caution because of the small number of Hispanic and ELL students.</p> <p>Regarding gender, males comprised the majority of the students in the sample (67.6%) and were "overrepresented" in the internalizing problems and the limited problems latent classes. This finding is counter to research that found males to be exhibiting more externalizing behaviors (see [<reflink idref="bib24" id="ref89">24</reflink>]). This finding should also be viewed with caution as the internalizing latent class consists of the smallest number of students.</p> <p>Finally, a few additional observations regarding the findings involve student scores on the Socially Maladjusted scale included in SAED-3. Over the years, the exclusionary clause in the definition regarding social maladjustment has been controversial ([<reflink idref="bib23" id="ref90">23</reflink>]; [<reflink idref="bib48" id="ref91">48</reflink>]). Findings from this study, however, point to overlap between externalizing behaviors and social maladjustment, allowing for the speculation that students scoring high in the Social Maladjustment scale will likely be captured under the definitional characteristic of inappropriate behavior. Furthermore, the finding that students in the externalizing and severe problems classes tend to be placed in more restrictive settings is expected, given the acuteness of problems. This finding along with the fact that almost two-thirds of the sample exhibited behaviors more indicative of ED may explain why students with ED have been traditionally placed in more restrictive settings compared with other students with disabilities (see [<reflink idref="bib62" id="ref92">62</reflink>]).</p> <hd id="AN0157637379-11">Limitations and Future Research</hd> <p>As with any research, there are a number of noteworthy limitations. First, the sample of students with ED was not selected on a random basis. In most cases, school professionals—teachers, special education teachers, school psychologists, and social workers—were contacted by the authors and asked to contribute student data to the norming effort of the SAED-3. Thus, the sample included school personnel who volunteered to assist and completed rating forms on their students. Therefore, this sample does not include information about students with ED whose teachers chose not to volunteer and whose responses might have been systematically different from those individuals who chose to volunteer, which thus may have introduced bias to the findings. Second, while the sample was nationally representative in many ways, the sample included fewer Hispanic students than are found in the population of students with school-identified ED; therefore, the external validity of the findings may be limited with respect to Hispanic students. Relatedly, the small samples of Hispanic and ELL students limited the statistical power of inferential tests involving those groups of students, and the conclusions regarding those statistical tests should be interpreted accordingly.</p> <p>Third, data on age at identification were unavailable as were data on the types and lengths of supports provided to each student, and therefore, the extent to which these data were related to the latent classes is unknown (e.g., limited problems latent class). Fourth, data were unavailable on the hierarchical nesting of students within teachers, so there is a possibility that teacher effects may have affected the analysis. Fifth, LCA as a methodology is highly sample dependent and requires large samples to estimate accurate model parameters (see [<reflink idref="bib33" id="ref93">33</reflink>]), so the results of this particular analysis should be viewed with caution until the study is replicated. Future research using LCA should consider oversampling students from diverse backgrounds, such as Hispanic and Native American students, to be able to better estimate the role of race and ethnicity on latent class membership. Finally, as noted previously, data on the social maladjustment construct was collected only on students age 12 years or older, which resulted in missing data for over one-third of the sample. Thus, the findings related to social maladjustment may have been influenced by the age of the students in each latent class. Future researchers with larger samples of students with ED may want to replicate the study and look at elementary and secondary students separately to determine how the social maladjustment variable is related to student profiles.</p> <p>Along with the limitations, which provide a number of considerations for future research, there are additional areas for future study. Clearly, the present study represents an initial investigation into the emotional and behavioral profiles presented by students with ED. For this reason, the current study calls for replication with a nationally representative sample of students with ED and a comprehensive set of demographic, academic, and familial variables. Overall, further research needs to include representative samples of ethnic/racial groups (i.e., Hispanic and Native Americans) and females. This endeavor will necessitate samples drawn from regional, state, and district levels involving primarily southwestern states with a higher concentration of these populations. In addition, it will be beneficial to expand background variables to include academic performance (e.g., grades), SES, attendance, discipline data, family functioning, to name a few. Such samples will allow for a better understanding of how these profiles may be associated with these additional variables and ultimately could result in improved programming and outcomes.</p> <hd id="AN0157637379-12">Implications for Assessment and Intervention</hd> <p>The SAED-3 (RS) normative data used in this study to examine profiles of students with ED on the five characteristics of the federal definition provided important insights regarding students with ED. First, it is clear that students with ED represent a diverse group, which will continue to challenge educators in their efforts to ensure a free appropriate public education. The utility of the scale along with other pertinent information will provide the springboard for individualized, evidence-based academic and behavioral interventions. Indeed, SAED-3 is a technically adequate rating scale that has ample validation for its previous versions and mounting support for the most recent edition (see [<reflink idref="bib19" id="ref94">19</reflink>]).</p> <p>Regarding interventions, teacher ratings of students using the SAED-3 RS is likely to provide critical information in developing comprehensive programming. Interventions selected to address individual needs must be based on peer-reviewed research to the extent appropriate in light of IDEA requirements. According to [<reflink idref="bib73" id="ref95">73</reflink>], empirically validated interventions include (a) creating structure and predictability (e.g., routines and active supervision), (b) promoting positive classroom climate (e.g., behavior-specific praise), (c) using effective instructional strategies (e.g., explicit instruction and performance feedback), and (d) incorporating evidence-based assessment practices in data-based decision-making (e.g., screening and progress monitoring and function-based assessment; see [<reflink idref="bib54" id="ref96">54</reflink>]). For example, scores on the RS subscales will provide critical information to develop effective BIPs (see [<reflink idref="bib55" id="ref97">55</reflink>]). BIPs, although only required under certain disciplinary actions under IDEA (i.e., behavior is determined to be a manifestation of the child's disability and placements for up to 45 school days in an interim alternative education setting), may be a necessary proactive tool for students with ED ([<reflink idref="bib49" id="ref98">49</reflink>]). The best fit of such an approach is IDEA's IEP provisions under "Consideration of Special Factors," which state that the "IEP Team shall in the case of a child whose behavior impedes the child's learning or that of others, consider the use of positive behavioral interventions & supports (PBIS), and other strategies, to address that behavior" (§1414 (d)(<reflink idref="bib3" id="ref99">3</reflink>)(B) (i)).</p> <p>High scores on scales such as (D) Unhappiness or Depression and (E) Physical Symptoms or Fears further accentuate the need for school-based mental health services. Unfortunately, the extent and quality of school-based mental health services are questionable (see [<reflink idref="bib37" id="ref100">37</reflink>]; [<reflink idref="bib50" id="ref101">50</reflink>]). In 2017 to 2018, about 51% of public schools reported providing diagnostic mental health assessments; only 38% of public schools reported providing treatment. Major hurdles involving mental health services reported by public schools included inadequate funding (52%) and inadequate access to licensed mental health professionals (41%; [<reflink idref="bib71" id="ref102">71</reflink>]). Furthermore, there is no evidence that the multifaced cognitive and behavioral interventions proposed by [<reflink idref="bib37" id="ref103">37</reflink>] have been a widespread treatment approach. Yet, if school personnel are obligated to fulfill their statutory obligation under IDEA regarding FAPE, such services must be provided and must allow the student to make meaningful progress. This need for progress in light of the child's circumstances was clearly articulated by the Supreme Court in <emph>Endrew F. v. Douglas County School District</emph> ([<reflink idref="bib15" id="ref104">15</reflink>]) (see [<reflink idref="bib63" id="ref105">63</reflink>]).</p> <p>Furthermore, in light of age effects regarding verification (i.e., capturing the most acute of cases) and persistent concerns of the gross underrepresentation of the ED population, it is necessary to expand the use of Multi-Tiered System of Supports (MTSS, 2020) as a means to provide interventions in a systematic tiered approach, in addition to being preventive in nature. Popular frameworks such as PBIS (2020) are promising means to ameliorate academic and behavioral problems in a systematic, data-driven, and science-based manner. Indeed, given disciplinary exclusions and academic underperformance, particularly among certain race and ethnic groups (Black, Hispanic, Native Americans), both disturbing and persistent concerns underscore the importance of MTSS to ensure host environments conducive to successful outcomes among all students.</p> <p>Finally, these insights on the profiles that students with ED present should become part of teacher preparation programs that specifically address the challenges of working with heterogeneous populations of students. This training will underscore the need for teachers to (a) employ valid measures such as SAED-3 for identification and programming, (b) recognize that comorbidity exacerbates pathology, and (c) ensure better individualization of services. Furthermore, such training will enable teachers to become more effective advocates, for example, for children in latent classes representing the severe problems and the externalizing problems typologies consisting of younger students. These students are likely in need of complementary services by outside agencies, including social service and mental health providers (see also [<reflink idref="bib68" id="ref106">68</reflink>]).</p> <ref id="AN0157637379-13"> <title> References </title> <blist> <bibl id="bib1" idref="ref81" type="bt">1</bibl> <bibtext> Achenbach T. M., Rescorla L. A. (2001). Manual for the ASEBA school-age forms and profiles. University of Vermont.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref76" type="bt">2</bibl> <bibtext> Asparouhov T., Muthén B. (2014). Auxiliary variables in mixture modeling: Using the BCH method in Mplus to estimate a distal outcome model and an arbitrary secondary model. Mplus Web Notes, 21(2), 1–22.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref99" type="bt">3</bibl> <bibtext> Bakk Z., Vermunt J. K. (2016). Robustness of stepwise latent class modeling with continuous distal outcomes. Structural Equation Modeling: A Multidisciplinary Journal, 23(1), 20–31.</bibtext> </blist> <blist> <bibl id="bib4" idref="ref20" type="bt">4</bibl> <bibtext> Bradley R., Doolittle J., Bartolotta R. (2008). Building on the data and adding to the discussion: The experiences and outcomes of students with emotional disturbance. Journal of Behavioral Education, 17, 4–23. https://doi.org/10.1007/s10864-007-9058-6</bibtext> </blist> <blist> <bibl id="bib5" idref="ref36" type="bt">5</bibl> <bibtext> Centers for Disease Control and Prevention. (2020). Data and statistics on children's mental health. https://<ulink href="http://www.cdc.gov/childrensmentalhealth/data.html">www.cdc.gov/childrensmentalhealth/data.html</ulink></bibtext> </blist> <blist> <bibl id="bib6" type="bt">6</bibl> <bibtext> Center on Multi-Tiered System of Supports. (2020). Essential components. https://mtss4success.org/essential-components</bibtext> </blist> <blist> <bibl id="bib7" type="bt">7</bibl> <bibtext> Center on Positive Behavioral Interventions & Supports. (2020). Tiered framework. https://<ulink href="http://www.pbis.org/pbis/tiered-framework">www.pbis.org/pbis/tiered-framework</ulink></bibtext> </blist> <blist> <bibl id="bib8" idref="ref30" type="bt">8</bibl> <bibtext> Chow J. C., Wehby J. H. (2018). Associations between language and problem behavior: A systematic review and correlational meta-analysis. Educational Psychology Review, 30(1), 61–82.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref9" type="bt">9</bibl> <bibtext> Cloth A. H., Evans S. W., Becker S. P., Paternite C. E. (2014). Social maladjustment and special education: State regulations and continued controversy. Journal of Emotional and Behavioral Disorders, 22(4), 214–224.</bibtext> </blist> <blist> <bibtext> Collins L. M., Lanza S. T. (2009). Latent class and latent transition analysis: With applications in the social, behavioral, and health sciences (Vol. 718). John Wiley & Sons.</bibtext> </blist> <blist> <bibtext> Counts J., Katsiyannis A., Whitford D. (2018). Culturally and linguistically diverse learners in special education: English learners. NASSP Bulletin, 102, 5–21.</bibtext> </blist> <blist> <bibtext> Cullinan D., Epstein M. H. (2001). Comorbidity among students with emotional disturbance. Behavioral Disorders, 26(3), 200–213.</bibtext> </blist> <blist> <bibtext> Cullinan D., Evans C., Epstein M. H., Ryser G. (2003). Characteristics of emotional disturbance of elementary school students. Behavioral Disorders, 28, 94–110.</bibtext> </blist> <blist> <bibtext> Cullinan D., Harniss M. K., Epstein M. H., Ryser G. (2002). The scale for assessing emotional disturbance: Concurrent validity. Journal of Child and Family Studies, 10, 449–466.</bibtext> </blist> <blist> <bibtext> Endrew F. v. Douglas County School District, 137 S. Ct. 988 (2017).</bibtext> </blist> <blist> <bibtext> Epstein M. H., Cullinan D. (1998). Scale for Assessing Emotional Disturbance (SAED). PRO-ED.</bibtext> </blist> <blist> <bibtext> Epstein M. H., Cullinan D. (2010). Scales for Assessing Emotional Disturbance (2nd ed.). PRO-ED.</bibtext> </blist> <blist> <bibtext> Epstein M. H., Cullinan D., Harniss M. K., Ryser G. (1999). The scale for assessing emotional disturbance: Test-retest and interrater reliability. Behavioral Disorders, 24(3), 222–230.</bibtext> </blist> <blist> <bibtext> Epstein M. H., Cullinan D., Pierce C., Huscroft-D'Angelo J., Wery J. (2020). Scales for Assessing Emotional Disturbance (3rd ed.). PRO-ED.</bibtext> </blist> <blist> <bibtext> Epstein M. H., Cullinan D., Ryser G., Pearson N. (2002). Development of a scale to assess emotional disturbance. Behavioral Disorders, 28, 5–22.</bibtext> </blist> <blist> <bibtext> Epstein M. H., Nordness P. D., Cullinan D., Hertzog M. (2002). Scale for assessing emotional disturbance: Long term test–retest reliability and convergent validity with kindergarten and first grade students. Remedial and Special Education, 23(3), 141–148.</bibtext> </blist> <blist> <bibtext> Florell D. (2018). Historical foundations. In Grapin S. L., Kranzler J. H. (Eds.), School psychology: Professional issues and practices (pp. 21–41). Springer.</bibtext> </blist> <blist> <bibtext> Forness S. R., Knitzer J. (1992). A new proposed and definition and terminology to replace "serious emotional disturbance" in Individuals with Disabilities Education Act. School Psychology Review, 21, 12–20.</bibtext> </blist> <blist> <bibtext> Gage N. (2013). Characteristics of students with emotional disturbance manifesting internalizing behaviors: A latent class analysis. Education and Treatment of Children, 36(4), 127–145.</bibtext> </blist> <blist> <bibtext> Gage N. A., Whitford D. K., Katsiyannis A., Adams S., Jasper A. (2019). National analysis of the disciplinary exclusion of black students with and without disabilities. Journal of Child and Family Studies, 28, 1754–1764.</bibtext> </blist> <blist> <bibtext> Gage N. A., Wilson J., MacSuga-Gage A. S. (2014). Writing performance of students with emotional and/or behavioral disabilities. Behavioral Disorders, 40(1), 4–14.</bibtext> </blist> <blist> <bibtext> Ghandour R. M., Sherman L. J., Vladutiu C. J., Ali M. M., Lynch S. E., Bitsko R. H., Blumberg S. J. (2019). Prevalence and treatment of depression, anxiety, and conduct problems in U.S. children. The Journal of Pediatrics, 206, 256–267.</bibtext> </blist> <blist> <bibtext> Gresham F. M., Lane K. L., MacMillan D. L., Bocian K. M. (1999). Social and academic profiles of externalizing and internalizing groups: Risk factors for emotional and behavioral disorders. Behavioral Disorders, 24, 231–245.</bibtext> </blist> <blist> <bibtext> Hanchon T. A., Allen R. A. (2013). Identifying students with emotional disturbance: School psychologists' practices and perceptions. Psychology in the Schools, 50(2), 193–208. https://doi.org/10.1002/pits.21668</bibtext> </blist> <blist> <bibtext> Hanchon T. A., Allen R. A. (2018). The identification of students with emotional disturbance: Moving the field toward responsible assessment practices. Psychology in the Schools, 55(2), 176–189. https://doi.org/10.1002/pits.22099</bibtext> </blist> <blist> <bibtext> Hartung C. M., Lefler E. K. (2019). Sex and gender in psychopathology: DSM-5 and beyond. Psychological Bulletin, 145(4), 390–409.</bibtext> </blist> <blist> <bibtext> Hollo A., Wehby J. H., Oliver R. O. (2014). Unidentified language deficits in children with emotional and behavioral disorders: A meta-analysis. Exceptional Children, 80, 169–186.</bibtext> </blist> <blist> <bibtext> Hu J., Leite W. L., Gao M. (2017). An evaluation of the use of covariates to assist in class enumeration in linear growth mixture modeling. Behavior Research Methods, 49, 1179–1190. https://doi.org/10.3758/s13428-016-0778-1</bibtext> </blist> <blist> <bibtext> Individuals with Disabilities Education Improvement Act of 2004 (IDEA). (2004). Pub. L. No. 108-446, 20 U.S.C. §1400 et seq.</bibtext> </blist> <blist> <bibtext> Jaffee S. R., Harrington H., Cohen P., Moffitt T. E. (2005). Cumulative prevalence of psychiatric disorder in youths. Journal of the American Academy of Child and Adolescent Psychiatry, 44(5), 406–407.</bibtext> </blist> <blist> <bibtext> Kauffman J. M., Landrum T. J. (2018). Characteristics of emotional and behavioral disorders of children and youth (11th ed.). Pearson.</bibtext> </blist> <blist> <bibtext> Kazdin A. E., Marciano P. L. (1998). Childhood and adolescent depression. In Mash E. J., Barkley R. A. (Eds.), Treatment of childhood disorders (2nd ed., pp. 211–248). Guilford.</bibtext> </blist> <blist> <bibtext> Lambert M. C., Cullinan D., Epstein M. H., Martin J. (2021a). Differences between students with emotional disturbance, learning disabilities, and without disabilities on the five dimensions of emotional disturbance. Journal of Applied School Psychology. Advance online publication. https://doi.org/10.1080/15377903.2021.1895399</bibtext> </blist> <blist> <bibtext> Lambert M. C., Cullinan D., Epstein M. H., Martin J. (2021b). Factor structure of the Scales for Assessing Emotional Disturbance-3 rating scale for student identified with emotional disturbance. Behavioral Disorders. Advance online publication. https://doi.org/10.1177/01987429211012328</bibtext> </blist> <blist> <bibtext> Lambert M. C., Martin J., Epstein M. H., Cullinan D. (2021). Differential item functioning of the Scales for Assessing Emotional Disturbance-3 for White and Hispanic students. Journal of Emotional and Behavioral Disorders. Advance online publication. https://doi.org/10.1177/1063426620988658</bibtext> </blist> <blist> <bibtext> Lambert M. C., Martin J., Epstein M. H., Cullinan D., Katsiyannis A. (2021). Differential item functioning of the Scales for Assessing Emotional Disturbance-3 for White and African American students. Psychology in the Schools, 58(3), 1–16. https://doi.org/10.1002/pits.22463</bibtext> </blist> <blist> <bibtext> Lambros K., Ward S., Bocian K., MacMillan D., Gresham F. (1998). Behavioral profiles of children at-risk for emotional and behavioral disorders: Implications for assessment and classification. Focus on Exceptional Children, 30, 1–16.</bibtext> </blist> <blist> <bibtext> Lane K. L., Barton-Arwood S. M., Nelson J. R., Wehby J. (2008). Academic performance of students with emotional and behavioral disorders served in a self-contained setting. Journal of Behavioral Education, 17, 43–62.</bibtext> </blist> <blist> <bibtext> Lazarsfeld P. F., Henry N. W. (1968). Latent structure analysis. Houghton Mifflin Co.</bibtext> </blist> <blist> <bibtext> Lewis T. J., Wehby J. H., Scott T. M. (2019). The Peacock Hill Working Group "problems and promises" three decades later: Introduction to the Creek Bend Consortium special issue. Behavioral Disorders, 44(2), 67–69.</bibtext> </blist> <blist> <bibtext> Lipscomb S., Haimson J., Liu A. Y., Burghardt J., Johnson D. R., Thurlow M. L. (2017). Preparing for life after high school: The characteristics and experiences of youth in special education. Findings from the National Longitudinal Transition Study 2012. Vol. 2: Comparisons across disability groups: Full report (NCEE 2017-4018). U.S. Department of Education.</bibtext> </blist> <blist> <bibtext> Losinski M. L., Ennis R. P., Sanders S. A., Nelson J. A. (2019). Examining the evidence-base of mathematic interventions for students with emotional disturbances: A meta-analysis. The Journal of Special Education, 52(4), 228–241.</bibtext> </blist> <blist> <bibtext> Maag J. W., Howell K. W. (1992). Special education and the exclusion of youth with social maladjustments: A cultural-organizational perspective. Remedial and Special Education, 13, 47–54.</bibtext> </blist> <blist> <bibtext> Maag J. W., Katsiyannis A. (2006). Behavioral intervention plans: Legal and practical considerations for students with emotional and behavioral disorders. Behavioral Disorders, 31, 348–362.</bibtext> </blist> <blist> <bibtext> Maag J. W., Katsiyannis A. (2010). School-based mental health services: Funding options and issues. Journal of Disability Policy Studies, 21, 173–180.</bibtext> </blist> <blist> <bibtext> McConaughy S., Skiba R. (1993). Comorbidity of externalizing and internalizing problems. School Psychology Review, 22, 421–436.</bibtext> </blist> <blist> <bibtext> Merikangas K. R., He J., Brody D., Fisher P. W., Bourdon K., Koretz D. S. (2010). Prevalence and treatment of mental disorders among U.S. children in the 2001—2004 NHANES. Pediatrics, 125(1), 75–81.</bibtext> </blist> <blist> <bibtext> Merrell K. W., Walker H. M. (2004). Deconstructing a definition: Social maladjustment versus emotional disturbance and moving the EBD field forward. Psychology in the Schools, 41, 899–910.</bibtext> </blist> <blist> <bibtext> Mitchell B. S., Kern L., Conroy M. A. (2019). Supporting students with emotional or behavioral disorders: State of the field. Behavioral Disorders, 44(2), 70–84.</bibtext> </blist> <blist> <bibtext> Morris R., Blashfield R., Satz P. (1981). Neuropsychology and cluster analysis: Problems and pitfalls. Journal of Clinical Neuropsychology, 3, 79–99.</bibtext> </blist> <blist> <bibtext> Morse W. C., Cutler R. L., Fink A. H. (1964). Public school classes for the emotionally handicapped. Council for Exceptional Children.</bibtext> </blist> <blist> <bibtext> Muthén B. (2004). Latent variable analysis. The Sage Handbook of Quantitative Methodology for the Social Sciences, 345(368), 106–109.</bibtext> </blist> <blist> <bibtext> Muthén L. K., Muthén B. O. (2016). MPlus User's Guide 1998–2016. Muthén & Muthén: Los Angeles, CA.</bibtext> </blist> <blist> <bibtext> National Alliance of Mental Illness. (2018). The comorbidity of anxiety and depression. https://<ulink href="http://www.nami.org/Blogs/NAMI-Blog/January-2018/The-Comorbidity-of-Anxiety-and-Depression">www.nami.org/Blogs/NAMI-Blog/January-2018/The-Comorbidity-of-Anxiety-and-Depression</ulink></bibtext> </blist> <blist> <bibtext> National Research Council and Institute of Medicine. (2009). Preventing mental, emotional, and behavioral disorders among young people: Progress and possibilities. Washington, DC: National Academies Press.</bibtext> </blist> <blist> <bibtext> Nylund K. L., Asparouhov T., Muthen B. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A monte carlo simulation study. Structural Equation Modeling: A Multidisciplinary Journal, 14(4), 535–569.</bibtext> </blist> <blist> <bibtext> Office of Special Education and Rehabilitative Services, Office of Special Education Programs, U.S. Department of Education. (2020). 41st annual report to congress on the implementation of the Individuals with Disabilities Education Act, 2019.</bibtext> </blist> <blist> <bibtext> Prince A., Yell M., Katsiyannis A. (2018). Endrew F. v. Douglas County School District: The U.S. Supreme Court and special education. Intervention in School and Clinic, 53, 321–324.</bibtext> </blist> <blist> <bibtext> Quay H. C., Morse W. C., Cutler R. L. (1966). Personality patterns of pupils in special classes for the emotionally disturbed. Exceptional Children, 32(5), 297–301.</bibtext> </blist> <blist> <bibtext> Skiba R. J., Grizzle K. L. (1992). Qualifications v. logic and data: Excluding conduct disorders from the SED definition. School Psychology Review, 21(1), 23–28. https://doi.org/10.1080/02796015.1992.12085589</bibtext> </blist> <blist> <bibtext> Skiba R. J., Simmons A. B., Ritter S., Gibb A. C., Rausch M. K., Cuadrado J., Chung C. G. (2008). Achieving equity in special education: History, status, and current challenges. Exceptional Children, 74, 264–288.</bibtext> </blist> <blist> <bibtext> Substance Abuse and Mental Health Services Administration. (2019). Key substance use and mental health indicators in the United States: Results from the 2018 National Survey on Drug Use and Health (HHS Publication No. PEP19-5068, NSDUH Series H-54). Center for Behavioral Health Statistics and Quality, Substance Abuse and Mental Health Services Administration. https://<ulink href="http://www.samhsa.gov/dat">www.samhsa.gov/dat</ulink></bibtext> </blist> <blist> <bibtext> Trout A. L., Epstein M. H., Nelson J. R., Reid R., Ohlund B. (2006). Profiles of young children teacher-identified as at risk for emotional disturbance: A pilot study. Behavioral Disorders, 31(2), 162–175.</bibtext> </blist> <blist> <bibtext> Trout A. L., Nordness P. D., Pierce C. D., Epstein M. H. (2003). Research on the academic status of children with emotional and behavioral disorders: A review of the literature from 1961 to 2000. Journal of Emotional & Behavioral Disorders, 11(4), 198–210.</bibtext> </blist> <blist> <bibtext> Wagner M., Kutash K., Duchnowski A. J., Epstein M. H., Sumi W. C. (2005). The Special Education Elementary Longitudinal Study (SEELS) and the National Longitudinal Transition Study (NLTS2): Study designs and implications for children and youth with emotional disturbance. Journal of Emotional and Behavioral Disorders, 13, 25–41.</bibtext> </blist> <blist> <bibtext> Wang K., Chen Y., Zhang J., Oudekerk B. A. (2020). Indicators of school crime and safety: 2019 (NCES 2020-063/NCJ 254485). National Center for Education Statistics, U.S. Department of Education, and Bureau of Justice Statistics, Office of Justice Programs, U.S. Department of Justice.</bibtext> </blist> <blist> <bibtext> Whitford D., Katsiyannis A., Counts J., Carrero K. M., Couvillon M. (2019). Exclusionary discipline for English learners: A national analysis. Journal of Child and Family Studies, 28, 301–314.</bibtext> </blist> <blist> <bibtext> Zaheer I., Maggin D., McDaniel S., McIntosh K., Rodriguez B. J., Fogt J. B. (2019). Implementation of promising practices that support students with emotional and behavioral disorders. Behavioral Disorders, 44(2), 117–128.</bibtext> </blist> <blist> <bibtext> Zahn-Waxler C., Shirtcliff E. A., Marceau K. (2008). Disorders of childhood and adolescence: Gender and psychopathology. Annual Review of Clinical Psychology, 4, 275–303.</bibtext> </blist> </ref> <ref id="AN0157637379-14"> <title> Footnotes </title> <blist> <bibtext> The third and fourth authors are developers of the SAED-3, which was used in this research study. They receive royalties from sales of the assessment.</bibtext> </blist> <blist> <bibtext> The author(s) received no financial support for the research, authorship, and/or publication of this article.</bibtext> </blist> </ref> <aug> <p>By Matthew C. Lambert; Antonis Katsiyannis; Michael H. Epstein; Douglas Cullinan and Erkko Sointu</p> <p>Reported by Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib62" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib34" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib22" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib23" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib29" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib30" firstref="ref6"></nolink> <nolink nlid="nl7" bibid="bib53" firstref="ref7"></nolink> <nolink nlid="nl8" bibid="bib65" firstref="ref8"></nolink> <nolink nlid="nl9" bibid="bib14" firstref="ref10"></nolink> <nolink nlid="nl10" bibid="bib48" firstref="ref11"></nolink> <nolink nlid="nl11" bibid="bib35" firstref="ref13"></nolink> <nolink nlid="nl12" bibid="bib52" firstref="ref14"></nolink> <nolink nlid="nl13" bibid="bib60" firstref="ref15"></nolink> <nolink nlid="nl14" bibid="bib54" firstref="ref17"></nolink> <nolink nlid="nl15" bibid="bib36" firstref="ref19"></nolink> <nolink nlid="nl16" bibid="bib45" firstref="ref21"></nolink> <nolink nlid="nl17" bibid="bib46" firstref="ref22"></nolink> <nolink nlid="nl18" bibid="bib56" firstref="ref23"></nolink> <nolink nlid="nl19" bibid="bib69" firstref="ref24"></nolink> <nolink nlid="nl20" bibid="bib26" firstref="ref25"></nolink> <nolink nlid="nl21" bibid="bib43" firstref="ref26"></nolink> <nolink nlid="nl22" bibid="bib47" firstref="ref27"></nolink> <nolink nlid="nl23" bibid="bib68" firstref="ref28"></nolink> <nolink nlid="nl24" bibid="bib32" firstref="ref29"></nolink> <nolink nlid="nl25" bibid="bib70" firstref="ref33"></nolink> <nolink nlid="nl26" bibid="bib27" firstref="ref37"></nolink> <nolink nlid="nl27" bibid="bib64" firstref="ref38"></nolink> <nolink nlid="nl28" bibid="bib51" firstref="ref39"></nolink> <nolink nlid="nl29" bibid="bib24" firstref="ref40"></nolink> <nolink nlid="nl30" bibid="bib28" firstref="ref41"></nolink> <nolink nlid="nl31" bibid="bib13" firstref="ref42"></nolink> <nolink nlid="nl32" bibid="bib31" firstref="ref43"></nolink> <nolink nlid="nl33" bibid="bib74" firstref="ref44"></nolink> <nolink nlid="nl34" bibid="bib12" firstref="ref45"></nolink> <nolink nlid="nl35" bibid="bib42" firstref="ref48"></nolink> <nolink nlid="nl36" bibid="bib59" firstref="ref49"></nolink> <nolink nlid="nl37" bibid="bib67" firstref="ref50"></nolink> <nolink nlid="nl38" bibid="bib19" firstref="ref53"></nolink> <nolink nlid="nl39" bibid="bib18" firstref="ref56"></nolink> <nolink nlid="nl40" bibid="bib21" firstref="ref57"></nolink> <nolink nlid="nl41" bibid="bib16" firstref="ref58"></nolink> <nolink nlid="nl42" bibid="bib17" firstref="ref59"></nolink> <nolink nlid="nl43" bibid="bib20" firstref="ref65"></nolink> <nolink nlid="nl44" bibid="bib39" firstref="ref67"></nolink> <nolink nlid="nl45" bibid="bib40" firstref="ref68"></nolink> <nolink nlid="nl46" bibid="bib41" firstref="ref69"></nolink> <nolink nlid="nl47" bibid="bib38" firstref="ref70"></nolink> <nolink nlid="nl48" bibid="bib58" firstref="ref71"></nolink> <nolink nlid="nl49" bibid="bib44" firstref="ref72"></nolink> <nolink nlid="nl50" bibid="bib10" firstref="ref73"></nolink> <nolink nlid="nl51" bibid="bib57" firstref="ref74"></nolink> <nolink nlid="nl52" bibid="bib61" firstref="ref75"></nolink> <nolink nlid="nl53" bibid="bib11" firstref="ref84"></nolink> <nolink nlid="nl54" bibid="bib25" firstref="ref85"></nolink> <nolink nlid="nl55" bibid="bib66" firstref="ref87"></nolink> <nolink nlid="nl56" bibid="bib72" firstref="ref88"></nolink> <nolink nlid="nl57" bibid="bib33" firstref="ref93"></nolink> <nolink nlid="nl58" bibid="bib73" firstref="ref95"></nolink> <nolink nlid="nl59" bibid="bib55" firstref="ref97"></nolink> <nolink nlid="nl60" bibid="bib49" firstref="ref98"></nolink> <nolink nlid="nl61" bibid="bib37" firstref="ref100"></nolink> <nolink nlid="nl62" bibid="bib50" firstref="ref101"></nolink> <nolink nlid="nl63" bibid="bib71" firstref="ref102"></nolink> <nolink nlid="nl64" bibid="bib15" firstref="ref104"></nolink> <nolink nlid="nl65" bibid="bib63" firstref="ref105"></nolink>
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Profiles of Emotional Disturbance across the Five Characteristics of the Federal Definition
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Lambert%2C+Matthew+C%2E%22">Lambert, Matthew C.</searchLink><br /><searchLink fieldCode="AR" term="%22Katsiyannis%2C+Antonis%22">Katsiyannis, Antonis</searchLink><br /><searchLink fieldCode="AR" term="%22Epstein%2C+Michael+H%2E%22">Epstein, Michael H.</searchLink><br /><searchLink fieldCode="AR" term="%22Cullinan%2C+Douglas%22">Cullinan, Douglas</searchLink><br /><searchLink fieldCode="AR" term="%22Sointu%2C+Erkko%22">Sointu, Erkko</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Behavioral+Disorders%22"><i>Behavioral Disorders</i></searchLink>. Aug 2022 47(4):223-235.
– 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: http://sagepub.com
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 13
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2022
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Elementary+Secondary+Education%22">Elementary Secondary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Elementary+Education%22">Elementary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Secondary+Education%22">Secondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Emotional+Disturbances%22">Emotional Disturbances</searchLink><br /><searchLink fieldCode="DE" term="%22Definitions%22">Definitions</searchLink><br /><searchLink fieldCode="DE" term="%22Federal+Regulation%22">Federal Regulation</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Characteristics%22">Student Characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Heterogeneous+Grouping%22">Heterogeneous Grouping</searchLink><br /><searchLink fieldCode="DE" term="%22Disability+Identification%22">Disability Identification</searchLink><br /><searchLink fieldCode="DE" term="%22Profiles%22">Profiles</searchLink><br /><searchLink fieldCode="DE" term="%22Students+with+Disabilities%22">Students with Disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Students%22">Students</searchLink><br /><searchLink fieldCode="DE" term="%22Elementary+School+Students%22">Elementary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Secondary+School+Students%22">Secondary School Students</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/01987429211033567
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0198-7429
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Ensuring the provision of a free, appropriate public education (FAPE) to students qualified for services under the disability category of emotional disturbance (ED) has been both challenging and controversial. Examining this population in light of the five characteristics listed in the federal definition may provide useful insights to address needs and improve outcomes. The purpose of this study was to use latent class analysis to examine profiles across the five characteristics of the federal definition of ED for a sample of 491 students school-identified with ED. Key findings include that (a) students with ED are a heterogeneous group with distinct and qualitatively different subgroups; (b) latent classes representing the severe problems and the externalizing problems typologies tended to consist of younger students; (c) greater proportions of Black, Hispanic, and English-language learner students were found in the severe and externalizing latent classes; and (d) students in the externalizing and severe latent classes spent more time in special education classrooms and had worse ratings on social maladjustment. The findings highlight important implications for practice in regard to assessment, program differentiation, and preservice teacher training. Research limitations and directions for future research are also discussed.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2022
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1343406
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1343406
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/01987429211033567
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 223
    Subjects:
      – SubjectFull: Emotional Disturbances
        Type: general
      – SubjectFull: Definitions
        Type: general
      – SubjectFull: Federal Regulation
        Type: general
      – SubjectFull: Student Characteristics
        Type: general
      – SubjectFull: Heterogeneous Grouping
        Type: general
      – SubjectFull: Disability Identification
        Type: general
      – SubjectFull: Profiles
        Type: general
      – SubjectFull: Students with Disabilities
        Type: general
      – SubjectFull: Students
        Type: general
      – SubjectFull: Elementary School Students
        Type: general
      – SubjectFull: Secondary School Students
        Type: general
    Titles:
      – TitleFull: Profiles of Emotional Disturbance across the Five Characteristics of the Federal Definition
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Lambert, Matthew C.
      – PersonEntity:
          Name:
            NameFull: Katsiyannis, Antonis
      – PersonEntity:
          Name:
            NameFull: Epstein, Michael H.
      – PersonEntity:
          Name:
            NameFull: Cullinan, Douglas
      – PersonEntity:
          Name:
            NameFull: Sointu, Erkko
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 08
              Type: published
              Y: 2022
          Identifiers:
            – Type: issn-print
              Value: 0198-7429
          Numbering:
            – Type: volume
              Value: 47
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Behavioral Disorders
              Type: main
ResultId 1