Educational Placement for Children with Autism Spectrum Disorders in ...

Education and Training in Autism and Developmental Disabilities, 2013, 48(4), 469 – 478 © Division on Autism and Developmental Disabilities

Educational Placement for Children with Autism Spectrum Disorders in Public and Non-Public School Settings: The Impact of Social Skills and Behavior Problems Stacy Lauderdale-Littin

Erica Howell

Monmouth University

California State University, Fullerton

Jan Blacher University of California, Riverside Abstract: This study examined relationships among behavior problems, social skills, and educational placement within a sample of children with autism spectrum disorder or ASD in public (mean age 7) and non-public (mean age 8) school settings (n ⫽ 56). Parent and teacher agreement on child characteristics ratings tended to be similar while differences between parent ratings of students in public and non-public school settings found more behavior problems and poorer social skills for students in non-public school placements compared to public school settings. Furthermore, logistic regression analyses indicated that child age, family income, and social skills were predictive of educational placement, with overall prediction success at 87.5%. Current eligibility and diagnosis of ASD, in both clinical and educational settings, is also discussed. Educational placement for children with Autism Spectrum Disorder (ASD) is a pertinent topic in light of federal legislation mandating all students be educated within the least restrictive environment (LRE). Advocates for full inclusion of students with ASD believe that the general education setting is most appropriate in order to encourage overall success, both academically and socially (Starr, Foy, & Cramer, 2001). In contrast, proponents of specialized schools in more restrictive settings such as non-public schools (NPS) specializing in ASD, argue that NPS

This paper was based, in part, on the activities of the SEARCH family autism resource center in the Graduate School of Education, University of California, Riverside. Funding was provided in part by the Doug Flutie, Jr., Foundation for Autism, Inc., and The Community of Riverside. We are indebted to our staff, to the doctoral students who worked on this study, and to the families who participated in this research. Correspondence concerning this article should be addressed to Erica Howell, Department of Special Education, CP 570-25, College of Education, California State University, Fullerton, PO Box 6868, Fullerton, CA 92834. E-mail: [email protected]

services offer more concentrated educational programming, thus, providing a greater overall benefit to the child (Harrower, 1999). In light of the controversy, it is important to understand the variables that may differentiate students from participating in public versus non-public school settings. Autism Eligibility and Diagnosis Autism is a neurodevelopmental disorder where symptoms are apparent before the age of three. Diagnostic criteria for autism in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR; American Psychiatric Association, 2000), at present, include qualitative impairments in communication, social interaction, and restricted, repetitive, and stereotyped patterns of behavior, interests, and activities. Although the DSM-IV-TR requires impairments in all three categories to receive a diagnosis, the upcoming DSM V (2013) reduces the broad categories of impairment to only two, social communication deficits and restricted, repetitive behaviors. However, per the Individuals with Disabilities Education Act (IDEA, 2004), individuals within the school system are able to receive educational services

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under the eligibility category of autism if they have a disability “significantly affecting verbal and nonverbal communication and social interaction, generally evident before age three, that adversely affects a child’s educational performance. Other characteristics often associated with autism are engagement in repetitive activities and stereotyped movements, resistance to environmental change or change in daily routines, and unusual responses to sensory experiences” (IDEA, 2004). Students are also eligible to receive educational services under the eligibility category of autism if the previously mentioned characteristics are present after the age of three. Based broadly on the IDEA (2004) definition, states have their own special education criteria to find individuals eligible for special education services. California, for instance, identifies children eligible for educational services under a broader category defined as “Autistic Like Behaviors” (Title 5, California Code of Regulations, section 3030g). This includes any combination of an inability to use oral language in order to communicate appropriately, an obsession to maintain sameness, impairment in social interaction from infancy through early childhood and history of extreme withdrawal relating to people, inappropriate use of objects and/or preoccupation with objects, and ritualistic behaviors. These individual categories and differences are important to discuss because the Center for Disease Control (CDC) recently reported an increased prevalence of autism from 1:110 (2006) to 1:88 (2008) following the DSMIV-TR definition. This reported number by the CDC includes individuals with an educational diagnosis as well as clinical diagnosis and those meeting the criteria for Autistic Disorder, Pervasive Developmental Disorder– Not Otherwise Specified (PDD-NOS, including Atypical Autism), or Asperger Syndrome. In addition, the United States Department if Education (2011) reported similar findings in an increase in autism prevalence from .3 percent of the special education population in 2003–2004 to .7 percent in 2008 –2009. With this reported increased autism prevalence, educational placement for children with ASD is a relevant topic as IDEA (2004) and the No Child Left Behind Act (P.L. 107-110, Section 1001) require that schools educate students

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with disabilities in the least restrictive environment (LRE). The benefits of a fully inclusive placement versus a more segregated classroom environment continue to be determined, as research has produced conflicting results. Furthermore, little research to date exists that examines how student-specific characteristics influence decisions on placement in the least restrictive environment. Educational Placement Educational placement of students with ASD has, in recent years, become a topic of heightened interest. With a large educational continuum, parents and teachers are charged with the task of determining the most beneficial placement for all students (Tissot, 2011). Recently, inclusion in the general education classroom has been thought to fulfill this requirement. Within the preschool inclusive setting, children with ASD have shown significant increases in intellectual quotient (IQ), social, and communication skills (Stahmer & Ingersoll, 2004). There is also evidence of increases in adaptive functioning (Fisher & Meyer, 2002) and length of social interactions with typically developing peers (Whitaker, 2004). Although this belief is becoming more widespread, there are still individuals who question if the inclusion model can fulfill the educational and social needs of students on the autism spectrum (Leyser & Kirk, 2004). There has also been limited outcome research on inclusion of students with ASD to support this belief (Ferraioli & Harris, 2011). Proponents of a more restrictive placement for students with ASD point to the need for a more highly structured learning environment for some students in order to meet their educational and social needs (Mesibov & Shea, 1996) and question whether specialized learning models can be effectively implemented within the inclusive setting (Marks, 2007). They also emphasize the need for specialized instruction to maximize student growth and the positive results that have been yielded from such instruction (Reed, Osborne, & Corness, 2007). Within this debate, understanding of the appropriateness of the educational continuum and the characteristics associated with children who end up in these various place-

Education and Training in Autism and Developmental Disabilities-December 2013

ments is vital. Recent research suggests that student characteristics may actually underlie the determination of educational placement in the LRE, such as placement in the general education setting, in a more restrictive placement in a special education classroom or in the separate NPS. In order to determine characteristics associated with educational placement, White, Scahill, Klin, Koenig, and Volkmar (2007) examined 101 individuals with autism, with an average age of twelve, to determine which characteristics accounted for their placement in special education or inclusion classrooms within the public school. Findings, utilizing a logistical regression, indicated that classroom placement was associated with intellectual quotient (IQ) and symptom severity in the areas of communication and socialization, with students who had lower IQs and more severe symptoms placed into the more restrictive environments. They also found that the setting in which students were originally placed (either the general education with full inclusion or in a special education classroom) was most likely where they would remain. The few students who did switch to a less restrictive placement tended to have higher levels of socialization. In addition, students who were initially placed into a special education environment, and remained in this placement, had lower levels of adaptive functioning, more restrictive and repetitive behaviors, and lower IQs as compared to students who moved into a more restrictive placement. Another study examined the child variables of age and IQ in relation to educational placement, in either inclusion or special education classrooms (Eaves & Ho, 1997). The sample consisted of 76 students with an average age of 11.6 years. The findings indicated that level of functioning, specifically IQ and age, were factors in determining educational placement. Students with a higher IQ were more likely to be fully included as compared to their lower functioning peers. In addition, students with a higher IQ, who were younger, were also more likely to be placed in an inclusive environment as compared to older students with the same IQ. Level of social functioning is likely as important as IQ as a determinant of educational placement. Lyons, Cappadocia, and Weiss

(2011) found that elementary students who were fully included with their typical peers had higher levels of social skills and more friends as compared to students in a more restrictive environment. Although, without a longitudinal study, it is difficult to know the direction of effect in most of these studies; are social skills fostered or enhanced in the inclusive environment or are the children who have better theory of mind or social skills better candidates for inclusion? Supporting the social and cognitive aspects of the previous studies, Aljunied and Frederickson (2011) found that both theory of mind and IQ were determining factors in level of special education need. With theory of mind being described as the cognitive structure underlying social understanding (Bjorklund, 2005), by some researchers, it can be seen as a key characteristic associated with the child characteristic of social skills. Purpose of Study The educational environment where children with ASD learn has considerable impact on their academic, social, and communication development. The current study examined social and behavioral characteristics of students with ASD in public and non-public school placements, as well as the agreement between parents and teachers on reports of these characteristics. In addition, specific demographic and child characteristics associated with public versus non-public school placement were also addressed. The comparison between placement in a public versus nonpublic school setting builds upon prior research, and provides a different dimension to the “inclusion” story focused on these placement options. Since placement in a nonpublic school is considered more restrictive than public school special education classroom placements, discussed in the previous mentioned papers, new insight will be provided on this topic. Three primary research questions were posed in order to address these issues: 1. To what extent do parents and teachers agree on ratings of child behavior problems and social skills? 2. Do students with ASD in public and non-


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public settings differ in their social and behavioral characteristics based on parent and teacher ratings? 3. What child specific characteristics predict concurrent educational placement in public and non-public school settings? Method Participants Participants were 56 students, 56 mothers, and 40 teachers with samples drawn from Southern California (48%) and Massachusetts (52%). The non-public school sample (n ⫽ 29) was recruited from a bi-coastal study investigating the school adaptations for children with autism spectrum disorders. Both nonpublic schools involved confirmed the clinical diagnosis of autism spectrum disorder for each child. The public school sample was recruited (n⫽27) from five public-schools in Southern California with children who were classified with an educational diagnosis of “autistic-like” characteristics. While psychologists, psychiatrists, and other mental health/ medical professionals qualify individuals as having autism based on the DSM-IV-TR, school personnel classify children as having “autistic like behaviors” in order to qualify them for special services. According to California Education Code, Section 56846.2, an educational diagnosis describes a “pupil with autism” as a pupil who exhibits autistic-like behaviors, including, but not limited to, any of the following behaviors, or any combination thereof: 1. An inability to use oral language for appropriate communication. 2. A history of extreme withdrawl or of relating to people inappropriately, and continued impairment in social interaction from infancy through early childhood. 3. An obsession to maintain sameness. 4. Extreme preoccupation with objects, inappropriate use of objects, or both. 5. Extreme resistance to controls. 6. A display of peculiar motoric mannerisms and motility patterns. 7. Self-stimulating, ritualistic behavior (Title 5, California Code of Regulations, section 3030g).

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Non-public school placement is a consideration made by the child’s IEP team when the needs of the student cannot be met in a public school setting (PL 94-142). A child with exceptional needs who requires specialized services is more likely to be educated in a nonpublic school setting. This setting must provide “appropriate special educational facilities, special education or designated instruction and services required by the individual with exceptional needs if no appropriate public education program is available” (California Education Code § 56365). Tuition is paid for by the local education agency. If parents decide to place their child in a nonpublic setting without the agreement of the IEP team, they are responsible for the cost of the education. Table 1 summarizes demographic information for the children and mothers in the public and non-public school settings. For the combined sample, 80% of the children were boys with a mean age of 8.1 years (SD ⫽ 2.1). Grade levels ranged from kindergarten to eighth grade. Seventy-one percent of the children were Caucasian, 4% were AfricanAmerican, 7% were Asian, 9% were Hispanic, 9% identified with the “other” category, which generally represented families of mixed ethnicity. In regard to education, 50% of mothers had a bachelor’s degree or higher. Eighty-five percent of families earned more than $35,000 annually. Public school teachers were largely female (92%) with 7 years of teaching on average. Students who attended public schools were members of classrooms with varying levels of restriction composed of the general education (26%), classrooms deemed mild/moderate (11%), classrooms described as moderate-specialized (26%), classrooms listed as moderate/severe (22%), and settings teachers described as “other” (11%) or as a generic SDC (4%). Based on the small sample sizes in each public school classroom category, participants were included together in the public school group for comparisons. Significant differences were found between the two groups (public school vs. non-public school) on the characteristics of child age and race, with children in non-public schools about a year and a half older, on average, and more often Caucasian. Mothers of children in non-public schools also had higher income,


TABLE 1 Public and Non-Public School Group Comparisons on Demographics

Demographics Age (mean years) Child sex: % boys Child race: % white, non-Hispanic Mother education: % High school Some college College and beyond Family income: % ⬎ 35,000 per year

Public School n ⫽ 27

Non-Public School n ⫽ 29

7.26 77.8 59.2

8.72 80 83.7

33.3 22.2 44.4 76.9

11.5 26.9 61.5 96.2

F or Chi Square F ⫽ 7.39** ␹2 ⫽ .038 ␹2 ⫽ 3.78* ␹2 ⫽ 2.96

␹2 ⫽ 4.127*

* p ⬍ .05; ** p ⬍ .01

with almost 100% (16.2) having an income over $35,000. These variables were correlated with educational placement to determine the need for co-varying in subsequent analyses. Both, child age and family income, correlated significantly with educational placement, (r ⫽ .34, p ⬍ .01 and r ⫽ .20, p ⬍ .05, respectively); therefore, they were co-varied in subsequent analyses. Measures The Child Behavior Checklist (CBCL; Achenbach & Rescorla, 2000, 2001) was administered to parents and teachers of elementary school children in order to assess child behavior problems. Parents of students aged 3 to 5 years received the preschool version with 99 items (Achenbach & Rescorla, 2000); students ages 6 to 18 received the school-aged version with 118 items (Achenbach & Rescorla, 2001). Behaviors problems were listed and participants rated each item on a 3-point Likert-type scale: not true (0), somewhat or sometimes true (1), or very true or often true (2). The CBCL for ages 6 –18 contains eight syndrome scales labeled as anxious/depressed, withdrawn/depressed, somatic complaints, social problems, thought problems, attention problems, rule-breaking behavior, and aggressive behavior. These syndrome scales can also be examined in two broad groupings of externalizing and internalizing behaviors. The externalizing behavior broadband is composed of rule-breaking and aggressive behavior. Anxious/depressed,

withdrawn/depressed, and somatic complaints make up the internalizing broadband. A total score broadband is composed of all items. A T-score with a mean of 50 and standard deviation of 10 is derived for the internalizing, externalizing, and total behavior problems broadband scores. CBCL reliability for total behavior problems is .84. According to the CBCL manual, the criterion-related validity is widely supported through multiple regressions, relative risk odd ratios, and discriminant analyses. The CBCL is highly correlated with other instruments such as the Conners Rating Scales and the Behavior Assessment System for Children and is a commonly used to determine the extent of maladaptive behaviors for children with autism spectrum disorders. The Teacher Report Form (TRF; Achenbach, 1991) is the teacher version of the CBCL. It provides a standardized measure of problem behavior and offers 112 items investigating a range of behavior problems. The classroom teacher rated each item on the same Likerttype scale as follows: is not true (0), somewhat or sometimes true (1) or very true or often true (2) currently or within the past two months. A total problem score, broadband externalizing and internalizing scores, and narrowband scales were generated. The total score consisted of a T-score with mean of 50 and standard deviation of 10. Test-retest reliability indicated a mean correlation of .90 for Academic Performance and Adaptive Functioning scores, and .92 for the Total Problems


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score. The TRF generated a score of teachers’ perceptions on student behavior that was compared to parent ratings. Social Skills Rating System-parent (SSRS-P; Gresham & Elliott, 1990). Child social skills were evaluated using the parent form of the Social Skills Rating System (SSRS-P; Gresham & Elliott, 1990). This scale yields scores that can be converted to standard scores (M⫽100; SD⫽15). The parent form measures the domains of Cooperation (10 items), Assertion (10 items), Self-control (10 items), and Responsibility (8 items). The Social Skills Total standard score was used in current analysis with higher scores being indicative of more positive social skills. The social skills total score has good internal consistency with an alpha of .90. The SSRS teacher form is moderately correlated with the Social Behavior Assessment with correlations in the .50s and .60s. The SSRS is commonly used to determine social skills for individuals with and without disabilities. Social Skills Rating System-teacher (SSRS; Gresham & Elliott, 1990). Child social skills were evaluated using the teacher form of the Social Skills Rating System, which measures the domains of Cooperation (10 items), Assertion (10 items), and Self-control (10 items). Scale scores are converted to standard scores (M ⫽ 100, SD ⫽ 15), with higher scores indicating better social skills. Good internal reliability is reported (alpha ⫽ 0.94), along with adequate discriminant validity (Gresham, Elliott, & Black, 1987). Content validity depends on the setting in which the behavior occurs with parents rating overall social skills and teachers rating social skills in a classroom context. The social skills total standard score was used in the current analyses. The Family Information Form (FIF; Baker, Blacher, Crnic, & Edelbrock, 2002) is a brief questionnaire that parents completed in order to provide demographic information such as child’s gender and ethnicity, marital status, mother and father’s age, education, job description, and employment status. Procedure School administrators and teachers notified families of the research investigation and interested parents returned a postcard to Uni-

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versity of California, Riverside, where an initial phone interview was scheduled with a parent. The purpose was to review study procedures, answer parent questions, and confirm the educational diagnosis of “autistic-like” behaviors. Consent forms and a packet of questionnaires were mailed to families. Upon receipt of signed consent and a teacher authorization form, teachers were mailed a packet of questionnaires. Teachers received a packet of measures for each child in his/her classroom whose parents were participating. The signed teacher consent form along with the completed measures, were returned to the researcher in a self-addressed, stamped envelope. Upon completion of the measures, participants (teachers and parents) from public school and non-public school samples received a $15 and a $25 Target gift card, respectively, as an honorarium. Teachers received one of these gift cards per packet of student measures that he or she completed. Results Prior to conducting the primary analyses, data were examined for outliers on the independent measures. Scores on student demographic variables were different across family income and child’s age at assessment. We correlated these with educational placement to determine whether any needed to be covaried in analyses. Both family income and child’s age significantly correlated with educational placement. Parent and Teacher Agreement on Behavior Problems and Social Skills In order to understand the extent to which parents and teachers agree on ratings of child problem behaviors and social skills, correlation coefficients were computed among the parent and teacher measures for the total scores on the Child Behavior Checklist/Teacher Report Form total score, CBCL/ TRF externalizing behavior subscales, CBCL/ TRF internalizing subscales and the Social Skills Inventory System standard score. Correlations between parents and teachers on ratings of total behavior problems (r ⫽ .51, p ⬍ .01), externalizing behaviors (r ⫽ .56, p ⬍ .01), and social skills (r ⫽ .62, p ⬍ .01) were all


TABLE 2 Parent and Teacher Ratings on Behavior Problems and Social Skills Between Public and Non-Public School Settings

Behavior Problems: Parent Report CBCL Total Behavior Problems Internalizing subscale Externalizing subscale Behavior Problems: Teacher Report TRF Total Behaviors Problems Internalizing subscale Externalizing subscale Social Skills Parent (SSRS-P): Teacher (SSRS-T)

Public School

Non-Public School

F or Chi Square

59.88 (8.81) 58.08 (8.64) 53.73 (10.46)

67.44 (5.84) 61.44 (7.53) 61.60 (8.80)

F ⫽ 18.2*** F ⫽ 2.83 F ⫽ 11.72**

61.70 (5.93) 61.48 (7.05) 57.65 (8.12)

64.39 (5.60) 60.26 (6.73) 62.22 (7.31)

F ⫽ 2.39 F ⫽ 3.41 F ⫽ 9.09**

74.80 (17.80) 78.77 (19.02)

59.88 (15.3) 75.35 (14.47)

F ⫽ 25.68*** F ⫽ 3.54

* p ⬍ .05; **p ⬍ .01; ***p ⬍ .001

significant; there was no relationship between parents and teachers ratings of internalizing behavior (r ⫽ .10, NS). In general, results suggested that parents and teachers tended to score the student/child similarly on measures of external behavior problems and social skills. Child characteristics and differences by educational setting. The second question investigated whether child social and behavioral characteristics differed between those in public versus non-public school settings. One-way analysis of co-variances were conducted to determine if there were significant differences between public and non-public school settings for parent reported SSRS and CBCL mean scores, controlling for child’s age at assessment and mother’s income. The parent reported SSRS and CBCL mean scores across the public and non-public school groups are shown in Table 2. The public school CBCL Total mean score (M ⫽ 59.88, SD ⫽ 8.81) was significantly lower than the non-public school score (M ⫽ 67.44, SD ⫽ 5.84) indicating that students in non-public school settings displayed higher levels of problem behavior, F(1, 49) ⫽ 17.20, p ⫽ .000). When specific subscales were examined, the CBCL internalizing subscale was not significantly different between the public (M ⫽ 58.08, SD ⫽ 8.64) and non-public school (M ⫽ 61.44, SD ⫽ 7.53) settings, however, when externalizing behaviors were examined, the public school students (M ⫽ 53.73,

SD ⫽ 10.46) were rated as demonstrating fewer behavior problems than non-public school students (M ⫽ 61.60, SD ⫽ 8.80), F(1, 49) ⫽ 11.99, p ⫽ .001. Social skills were investigated using the SSRS-P Standard Score, and parent ratings indicated significantly higher mean scores for public school students (M ⫽ 74.80, SD ⫽ 17.80) when compared to non-public school peers (M ⫽ 59.88, SD ⫽ 15.30) indicating that public school students displayed better social skills F(1, 47) ⫽ 15.87, p ⫽ .000. When teacher ratings on behavior problems were examined, no significant differences were found between public and nonpublic school students on ratings of total behavior problems and internalizing behaviors (see Table 2). However, significant differences emerged on externalizing behaviors with public school students (M ⫽ 57.65, SD ⫽ 8.12) displaying less externalizing behaviors than non-public school students (M ⫽ 62.22, SD ⫽ 7.31), F(1, 44) ⫽ 6.26, p ⫽ .01. Teacher ratings on social skills were not significantly different between public (M ⫽ 78.77, SD ⫽ 19.02) and non-public (M ⫽ 75.35, SD ⫽ 14.47) school students. Determining educational placement. The third question addressed predictors of concurrent educational placement. Scores on student demographic variables were different across family income and child’s age at assessment. Both family income and child’s age significantly


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TABLE 3 Correlations Among Child Age, Income, and Parent-Rated SSRS, and CBCL

Measure

Educational Placement

Child Age Income SSRS-P CBCL

.34** .20* .41** .46**

32.50, p ⬍ .000 with df ⫽ 4). Nagelkerke’s R2 of .66 indicated a moderate relationship between prediction and grouping. Prediction success overall was 87.5% (88% for public school and 87% for non-public school). Child age, income, and social skills were significant predictors of educational placement when examined. Older students who came from higher income families and who had poorer social skills were more likely to participate in a non-public school placement.

* p ⬍.05, ** p ⬍.01

Discussion correlated with educational placement (r ⫽ .34, p ⬍ .01 and r ⫽ .20, p ⬍ .05, respectively) and were included in the subsequent analysis. Furthermore, findings also indicated that social skills and behavior problems were significantly correlated with the outcome variable, educational placement (r ⫽ .41, p ⬍ .01, r ⫽ .46, p ⬍ .01, respectively) as shown in Table 3. A logistic regression analysis was conducted, with public school vs. non-public school placement as the outcome. The demographic variables of child age, and family income, and the parent ratings of externalizing child behavior problems (CBCL-Externalizing Subscale), and social skills (SSRS) were entered as predictors. As shown in Table 3, a test of the full model against constant only model was statistically significant, indicating that the predictors as a set reliably distinguished between public and non-public school students (chi square ⫽

TABLE 4 Summary of Logistic Regression Analysis for Parent-Rated Variables Predicting Educational Placement in Public and Non-Public Schools Predictor

B

SE B

eB

Child Age Income SSRS-P CBCL Externalizing Constant ␹2 df % predicted

.95* 3.76* ⫺.131* .04 ⫺8.31 32.504 4 87.5

.336 1.85 .053 .05

2.587 43.11 .87 1.04

* p ⬍ .05

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This study sought to determine agreement between parent and teacher ratings of behavior problems and social skills for students with ASD, in public versus non-public school settings. It also sought to determine some of the specific child and/or family characteristics associated with placement in a public versus non-public school setting. Upon comparing parent and teacher reports on behavior problems and social skills, it was determined that each party provided similar reports on measures of total problem behaviors, externalizing behaviors, and social skills. This would indicate that parents and teachers are witnessing similar behaviors at school and at home, and that both are reliable reporters of these behaviors. Parents and teacher reports of behavior problems and social skills were then compared across settings, public versus non-public school, to determine if students within these placements differed in the manifestation of these characteristics. After examining parentreported behavior problems, it was determined that students in a non-public school setting exhibited significantly higher levels of total behavior problems as compared to their peers in a public school. When internalizing and externalizing subscales were inspected individually, there were no reported differences between settings on internalizing behaviors; however, students in the public school were reported to exhibit fewer externalizing behavior, at least in comparison to their non-public school peers. There were also differences found in social skill levels between settings. Per parent report, students in public school exhibited higher levels of social skills as compared to students in a non-public school.


These findings of students in a non-public school setting having higher levels of total behavior problems and externalizing behaviors, as well as lower levels of social skills is supported by findings in previous studies indicating that these characteristics were associated with a more restrictive placement (White et al, 2007; Lyons et al., 2011). Teacher reports of behavior problems and social skills were then examined to determine if there were differences based on setting. Teachers did report more externalizing behaviors, only in students who attend nonpublic school settings. Findings differed from parent reports in that there were no differences found between settings in reports of total behavior problems, externalizing problems, and social skills. There were differences, however, in reports of externalizing behaviors, with students in a non-public school setting displaying higher levels of externalizing behaviors as compared to their peers in public school. Upon examining characteristics associated with educational placement child age, family income, and parent ratings of social skills were found to be reliable predictors. Students who were older, came from families with a higher income, and had lower levels of social skills were more likely to be placed in a non-public school setting. Findings related to child age and level of social skills were supported by previous researchers who found students placed in a public school setting were more likely to remain in that setting and, if students were going to be placed in a less restrictive environment at an older age, they were most likely going to have higher levels of social skills (Eaves & Ho, 1997; White et al., 2007). In addition, White et al. also found that students with lower levels of social skills were often placed in more restrictive placements in general. Family income was not a variable discussed in previous articles as a predictor of educational placement. This could be because the settings discussed, inclusion versus special education classroom, were all within the public school in the same general geographic and income area. Because the settings described in this paper are vastly different, public school setting versus a non-public school district setting, income might now play a larger role.

Income as a predictor of non-public school placement could be accounted for by parents of students in a non-public school having more access to legal services, more knowledge of placement options, and the resources to push for the placement of their choice. Many school districts do not want to place students within a non-public school because of the additional cost to the district. Litigation to achieve this placement might not be an option for many families of students with ASD. This issue could be examined in future research by gathering additional information from the parents of the children attending each type of school, their knowledge of placement options, and steps taken to secure their child’s current placement. Future research is needed to obtain information on other possible factors conceivably influencing placement. Information on IQ, communication skills, and theory of mind would be interesting to relate additional child characteristics associated with educational placement. In addition, as mentioned previously, supplementary information on family perspectives and characteristics could provide insight into how the role of family impacts placement decisions as well. With stakeholders, including parents and teachers, working to determine the most appropriate and beneficial placement for all students (Tissot, 2011), including those with autism, it is essential to understand some of the demographic and behavioral characteristics associated with those decisions. Gaining a broader knowledge base regarding this topic will assist in making the most appropriate choice on the educational continuum for each student, although the debate will likely continue regarding the most appropriate placement for students with ASD.

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for the ASEBA School-Age Forms & Profiles. Burlington, VT: University of Vermont, Research Center for Children, Youth, & Families. Aljunied, M., & Frederickson, N. (2011). Cognitive indicators of different levels of special educational support need in autism. Research in Autism Spectrum Disorders, 5, 368 –376. American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author. Baker, B. L., Blacher, J., Crnic, K., & Edelbrock, C. (2002). Behavior problems and parenting stress in families of three-year-old children with and without developmental delays. American Journal on Mental Retardation, 107, 433– 444. Bjorklund, D. F. (2005). Children’s thinking: Cognitive development and individual differences. Belmont: Wadsworth. California Education Code § 56356. Nonpublic, nonsectarian school services. California Education Code § 56846-56847. Autism training and information. Eaves, L. C., & Ho, H. H. (1997). School placement and academic achievement in children with autism spectrum disorder. Journal of Developmental and Physical Disabilities, 9, 277–291. Ferraioli, S. J., & Harris, S. L. (2011). Effective educational inclusion of students on the autism spectrum. Journal of Contemporary Psychotherapy, 41, 19 –28. Fisher, M., & Meyer, L. H. (2002). Development and social competence after two years for students enrolled in inclusive and self-contained educational programs. Research & Practice for Persons with Severe Disabilities, 27, 165–174. Gresham, F. M., & Elliott, S. N. (1990). Social Skills Rating System. Circle Pines, MN: American Guidance Service. Gresham, F. M., Elliott, S. N., & Black, F. L. (1987). Factor structure replication and bias investigation of the Teacher Ratings of Social Skills. Journal of School Psychology, 25, 81–92. Harrower, J. K. (1999). Educational inclusion of children with severe disabilities. Journal of Positive Behavior Interventions, 1, 215–230. Individuals With Disabilities Education Act, 20 U.S.C. § 300.8 (2004). Leyser, Y., & Kirk, R. (2004). Evaluating inclusion: An examination of parent views and factors influ-

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