Patterns and correlates of personal, family, and prior placement variables in an interagency community based system of care

Cullinan, Douglas; Epstein, Michael H.; Quinn, Kevin P.

doi:10.1007/bf02234665

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Profi les may ultimately prove to be benefi cial in the creation of specifi c, targeted intervention programs (Dembo & Schmeidler, 2003). Previous studies using clustering procedures have sought to determine profi les across subtypes of disabilities, including learning disabilities (Satz & Morris, 1981), oral language impairments (Speece, Roth, Cooper, & De La Paz, 1999), social withdrawal (Harrist, Zaia, Bates, Dodge, & Pettit, 1997), and behavioral disorders (Cullinan, Epstein, & Quinn, 1996;Rosenblatt, et al, 1998). Moreover, clustering procedures have been used with children presenting academic and behavioral problems, including youth delinquency (Dembo & Schmeidler, 2003), children at-risk for school failure (Speece & Cooper, 1990), and antisocial preadolescents (Pevalin, Wade, & Brannigan, 2003).…”

Section: Cluster Analysis and Examination Of Riskmentioning

confidence: 99%

Profiles of Young Children Teacher-Identified as at Risk for Emotional Disturbance: A Pilot Study

Trout

Epstein

Nelson

et al. 2006

Behavioral Disorders

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As prevention and early intervention opportunities for young children at risk of emotional disturbance (ED) increase, questions regarding the effectiveness of these programs for specific subpopulations of children have emerged. To date, few investigators have examined young children entering early school prevention/intervention programs to determine if clear subpopulations can be identified, and if so, which characteristics are most distinguishing. This study examined the risk factors of 140 kindergarten and first-grade students, identified by teachers as at risk of ED, to determine if distinct profiles exist. Cluster analysis procedures using teacher, child, and parent reports of family, academic, and social/emotional data revealed five distinct clusters: Parent nominated (n = 24), Elevated risks (n = 43), Primarily behavior (n = 25), Primarily academic (n = 27), and False positives (n = 21). Validation techniques revealed that the five clusters were distinguished by clear profiles, which differed across level of severity (i.e., high or low levels of risk per domain) and primary focus of risk (i.e., academic, social/emotional, or familial). Similarities and differences across cluster groups, possible implications for targeted prevention or early intervention programs, study limitations, and directions for future research are presented.

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Section: Cluster Analysis and Examination Of Riskmentioning

confidence: 99%

Profiles of Young Children Teacher-Identified as at Risk for Emotional Disturbance: A Pilot Study

Trout

Epstein

Nelson

et al. 2006

Behavioral Disorders

Self Cite

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“…The development and testing of such interventions may benefit from an understanding of the diverse needs of students with EBD. Yet, minimal research supports understanding how students with EBD differ among each other regarding academic, behavioral, and demographic characteristics (Cullinan et al, 1996; Gage, 2013; Gresham et al, 1999; King et al, 2019; Rosenblatt et al, 1998; Trout et al, 2006). The goal of this study was to use a person-oriented approach to explore potential constellations of characteristics of middle school students with EBD by conducting a latent profile analysis of several hundred students.…”

Section: Discussionmentioning

confidence: 99%

“…Although research specific to students with EBD that considers multiple risk-related variables to identify of potential subgroups of students may better inform intervention modification and development, the majority of these types of studies about students with or at-risk of EBD are over 15 years old. These studies have identified and examined unique profiles of students based on referrals, academic, behavioral, familial, and setting-based variables (Cullinan et al, 1996; Gage, 2013; Gresham et al, 1999; Rosenblatt et al, 1998; Trout et al, 2006). Most recently, two studies employed cluster and profile analysis to explore the diverse needs of students with EBD, modeling distinct profiles of student subgroups.…”

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confidence: 99%

Profiles of Behavioral, Academic, and Demographic Characteristics of Middle School Students With Emotional or Behavioral Needs

Farley

Hurley

Lambert

et al. 2022

Journal of Emotional and Behavioral Disorders

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Often the characteristics of students with emotional and behavioral disturbances (EBDs) are described in terms of how they compare to students with and without other disabilities. However, such a conceptualization or categorization may overshadow differences that exist among these youth, and few studies examine unique subpopulations within this group. To explore meaningful differences and similarities among students with EBD, which could help to facilitate customization of interventions and supports, we conducted a latent profile analysis of over 300 middle school student receiving special education services for emotional or behavioral needs. Results identified four profiles using demographic variables, school setting (alternative school or not), teacher ratings of behavior and academic competence, and parent report on child suspensions. Criterion variables were then tested, with significant differences identified in use of mental health services, parent empowerment, and caregiver strain. However, significant differences were not found in parent satisfaction with school. Limitations, implications, and the need for additional research are discussed.

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“…Other cluster analyses involving children with serious emotional disturbance or behavior problems have focused on service-related variables such as service utilization patterns (Lambert, Brannan, Breda, Heflinger, & Bickman, 1998) and placement history (Cullinan, Epstein, & Quinn, 1996). Lambert et al (1998) reported that when clusters were compared, service use patterns were affected by age, level of social functioning, caregiver strain, number of presenting problems, social support, and number of conduct disorder symptoms.…”

Section: Erious Emotional Disturbancementioning

confidence: 99%

Describing the Population of Adolescents Served in Systems of Care

Liao¹,

Manteuffel²,

Paulic

et al. 2001

Journal of Emotional and Behavioral Disorders

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Cluster analysis was conducted to examine clinical characteristics of 10to 19-year-old boys (N = 1,961) and girls (N = 1,320) with serious emotional disturbance entering systems of care funded by the Center for Mental Health Services. Subscale scores from intake assessment using the Child and Adolescent Functional Assessment Scale and the Child Behavior Checklist were included in the cluster analyses to determine whether adolescents could be grouped into identifiable patterns based on functional and behavioral characteristics. Three clusters were identified for each gender. Clusters differed in the severity of subscale scores across boys and girls; however, unique characteristics of clusters were most apparent when both functional and behavioral profiles were examined. Demographic and clinical characteristics were compared across clusters to further explore characteristics of these groups.The authors make recommendations for future research on adolescents entering community-based system-of-care services.

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Patterns and correlates of personal, family, and prior placement variables in an interagency community based system of care

Cited by 7 publications

References 14 publications

Profiles of Young Children Teacher-Identified as at Risk for Emotional Disturbance: A Pilot Study

Profiles of Young Children Teacher-Identified as at Risk for Emotional Disturbance: A Pilot Study

Profiles of Behavioral, Academic, and Demographic Characteristics of Middle School Students With Emotional or Behavioral Needs

Describing the Population of Adolescents Served in Systems of Care

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