Comparing Autism Symptom Severity Between Children With a Medical Autism Diagnosis and an Autism Special Education Eligibility

Abstract

Rates of children identified as having autism spectrum disorder (ASD) continue to increase in both medical and school settings. While procedures for providing a medical diagnosis are relatively consistent throughout the United States, the process for determining special education eligibility under an ASD classification varies by state, with many states adopting looser identification criteria than medical taxonomies. This study included a sample of 73 school-age children with ASD and sought to examine differences in ASD symptom severity, adaptive functioning, and challenging behaviors between those identified in the medical system versus those identified in schools. Results indicate that children identified as having ASD only by their school had less severe clinician-rated ASD symptomatology than children with a medical ASD diagnosis but that caregiver reports of adaptive functioning and challenging behavior did not differ between the two groups. These findings do not appear to have been influenced by demographic factors including caregiver education, household income, or health insurance status. Implications and directions for future research are discussed.

Keywords

elementary age identification assessment IDEA policy

Introduction

The number of children identified with autism spectrum disorder (ASD) has increased greatly in recent years. This increase has been seen both in the general population via medical diagnoses, as well as in schools via special education eligibility for services under an autism classification. According to the most recent Centers for Disease Control and Prevention (2018) report that uses data collected in 2014, an average of 1 in 59 8-year-old American children is diagnosed with ASD. These numbers have risen dramatically even in the last 15 years. For example, data from 2002 indicated that 1 in 150 children of this age had been diagnosed, and data from 2010 indicated that 1 in 68 children had been diagnosed. This represents roughly a 150% increase in ASD diagnoses between 2002 and 2018. This large and seemingly sudden increase in prevalence has pressured schools, who are responsible for providing free and appropriate public education to all students identified with special education eligibility (Individuals with Disabilities Education Act [IDEA], 2004). The number of students served under an autism special education label in the U.S. public schools rose from 5,145 cases in 1991–1992 to 370,011 cases in 2010–2011 (Volker, 2012) to approximately 617,000 cases in 2015–2016 (National Center for Education Statistics, 2017).

Medical Diagnosis versus Special Education Eligibility

There may not be agreement in identification of children across the school and medical settings because different criteria are typically employed. A medical diagnosis of ASD is based on specific criteria defined in the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; American Psychiatric Association [APA], 2013). In contrast, school criteria of eligibility for special education services under an autism classification are based on federal and state laws. According to federal special education laws established initially under the IDEA, students diagnosed with ASD are eligible for specialized instruction and other educational supports and services when they demonstrate needs that are consistent with the federal definition of ASD (Sullivan, 2013). A major difference between the medical diagnosis and educational classification is that the DSM-5 requires the presence of communication, socialization, and behavioral deficits, while the federal definition only requires communication and social deficits that adversely affects a child’s educational performance. In addition, individual states can modify the special education criteria for ASD as long as it falls under the federal definition, which has resulted in very different requirements and procedural specifications to determine eligibility in different states (Mandell & Palmer, 2005). A national investigation found that 17 states explicitly included all or portions of DSM criteria in their eligibility criteria, while 34 did not (MacFarlane & Kanaya, 2009).

Furthermore, school psychologists who often lead the multidisciplinary teams who are tasked with making ASD eligibility determinations often rely on ASD screening measures rather than comprehensive “best practices” diagnostic measures (Aiello et al., 2017). Results from a large national survey of school psychologists indicated that the majority did not engage in the comprehensive assessment of ASD, which the authors of the survey defined as assessments that utilized ASD-specific instruments and consider all areas of development (Aiello et al., 2017). Another survey of 117 school psychologists found that most respondents assessed ASDs from a psychoeducational perspective and were rarely or inconsistently including ASD-specific instruments, developmental questionnaires, and parent guardian interviews in the ASD evaluation process (Allen et al., 2008).

In the states that are not using DSM criteria in their eligibility decisions, it is particularly important to examine differences in the characteristics of the children being identified by the medical and school systems, as they may represent two unique subsets of children with ASD. There are several potential explanations for how these differences manifest. It may be that the medical systems are catching children who are the most severely impacted by ASD, while schools are identifying less-severe cases that may simply slip through the cracks in early identification within the medical system. Alternatively, there is the possibility that schools are over-identifying or over-labeling students as having ASD to allow them to access special education services, when in reality they might not meet more rigorous DSM-based standards for diagnosis. Finally, it could be that the children who are only identified within the school system are an overrepresentation of lower socioeconomic status (SES) backgrounds and that families that have less formal education or do not have health insurance simply do not have the means to navigate the medical system to obtain the medical diagnosis for their child. Understanding differences between children with ASD who are identified in the medical and school systems may play a role in determining the types of interventions and other services that are required to meet the unique needs of the children in each of these settings.

Current Study

This study compared children who had a medical diagnosis of ASD with those who received special education services under an ASD eligibility without a medical diagnosis. Based on a review of current literature, this appears to be the first study to examine differences between groups of children with ASD identified under the medical and educational systems. The goal of the study was to examine differences in child and family characteristics based on diagnostic system. Specifically, we asked three research questions: (a) Do children with medical ASD diagnoses differ from children with special education autism eligibility on ASD symptom severity as rated by clinicians? (b) Do children with medical ASD diagnoses differ from children with special education autism eligibility differ on adaptive behavior and behavioral challenges as rated by parents? (c) Do children in these two groups vary on key demographic factors? Given the comprehensive nature of the medical diagnostic evaluations for ASD and the DSM-5 criteria, it was hypothesized that children with medical diagnoses of ASD would have more autism symptoms, lower adaptive skills, and greater behavioral challenges in comparison to children with special education eligibilities of autism (without medical diagnoses). We also hypothesized that the children with medical diagnoses might come from higher SES status backgrounds, with factors such as parent education and health insurance status helping to explain why these children received medical diagnoses while the others did not.

Methods

Sample

The data for this study were collected as part of the Oregon Early Autism Project, an ongoing study investigating child and family variables associated with ASD identification and service utilization in the state of Oregon, one of the aforementioned 34 states that does not include DSM criteria in ASD special education eligibility. In this state, eligibility for special education under the category of ASD was determined based on whether the child demonstrated characteristics of: impairments in communication; impairments in social interaction; patterns of behavior, interests, or activities that are restricted, repetitive, or stereotypic; and unusual responses to sensory experiences (Oregon Administrative Rules for Special Education, 2013). This is in contrast to the more specific DSM-5 criteria that the child demonstrates all three of a list of social communication deficits and at least two of four of a list of restricted, repetitive patterns of behavior (APA, 2013).

The current sample included 73 elementary school-age children (M = 7.83, SD = 1.62) and their primary caregiver. Most of the children in the sample were male (82.9%), and racially/ethnically either White (67.1%) or Bi/Multiethnic (22.9%). Full demographics are reported in Table 1. The recruitment of participants was done in two waves. Approximately half of participants (n = 38) were recruited in Wave 1 sample after being referred for suspected ASD by early childhood providers and remained with the study for follow-up during Wave 2. The other half of participants (n = 35) were added to the study during Wave 2 after being referred by schools based on having an ASD eligibility. Research team members served as clinicians in the study, assisting parents to complete intake forms related to demographic information and service utilization, and assessing the children on ASD symptomatology. These team members were all school psychology doctoral students who received training and supervision on use of the measure from the second author, who is a licensed psychologist.

Table 1.

Descriptive Statistics for Children With a Medical ASD Diagnosis or Special Education Eligibility for ASD.

Demographic	Medical diagnosis (n = 58)	Special education eligibility (n = 15)	t	p
Demographic	M (SD) or %	M (SD) or %	t	p
Child age	7.88 (1.53)	7.40 (1.96)	0.88	.389
Parent age	39.45 (7.71)	38.93 (6.55)	0.26	.796
Household income	$49,520 ($30,667)	$59,457 ($62,209)	−0.58	.571
Maternal years of education	14.83 (2.33)	15.67 (3.42)	−0.90	.381
			χ²
Child sex (male)	86.21%	73.33%	1.44	.230
Race/ethnicity			3.36	.645
White	63.79%	86.67%
Black/African-American	3.45%	0%
Hispanic/Latino	5.17%	0%
Multiple	24.14%	13.33%
80% or more time in gen. ed.	36.84%	40.00%	0.05	.822
Has health insurance	94.83%	100%	0.81	.368

Note. There were no significant between-group differences on any demographic variables. ASD = autism spectrum disorder.

Measures

ASD label

The independent grouping variable in this study is the source of a child’s ASD label (medical diagnosis or special education eligibility of autism). This information was reported by parents during their intake appointment for the research study. All 73 parents were asked to identify the source of their child’s ASD label while completing intake forms. Parents who indicated their child had school eligibility but no medical diagnosis were coded as special education eligibility only (n = 15). In contrast, parents who indicated their child had only a medical diagnosis, or both a medical diagnosis and a school eligibility, were coded as medical diagnosis (n = 58). The vast majority (91%) of those with medical diagnoses also had special education eligibilities.

Autism symptoms

Autism symptom severity was measured using the total scores on the Childhood Autism Rating Scale, Second Edition (CARS-2; Schopler et al., 2010). CARS-2 consists of 15 rating scale items completed by a trained clinician. The tool is designed to help identify children with ASD and evaluate their symptomatology, or severity of symptoms, through quantifiable ratings based on direct observation (Schopler et al., 2010). Examples of items include rating whether the child’s verbal communication, adaptation to change, and body use are age and situation appropriate. Each item on CARS-2 is scored from 1 to 4 with 1 representing behavior typical of that age and 4 representing behavior severely abnormal for the age, and thus greater ASD symptom severity. Total scores on CARS-2 range from 15 to 60, and 30 is considered the cutoff score for mild ASD. Internal consistency reliability for the CARS-2 in the present sample was α = 0.81.

Adaptive functioning

To assess child functioning and adaptive behaviors apart from ASD symptomatology and outside of the clinic setting, parents were also administered the Survey Interview Form of the Vineland Adaptive Behavior Scales, Second Edition (Vineland-II; Sparrow et al., 2005). Research assistants conducted the Vineland-II as a parent interview to assess the adaptive functioning of the child in the domains of communication, daily living skills, socialization, and motor skills. These domains are combined to determine an overall Adaptive Behavior Composite standard score, with a mean of 100 and standard deviation of 15. Prior research has indicated strong reliability and validity for this widely used measure of adaptive behavior (Sparrow et al., 2005).

Challenging behavior

In addition, parents self-completed the Strengths and Difficulties Questionnaire (SDQ; Goodman, 1997), a brief measure of the behavioral attributes of their child. This tool consists of 25 items that break down into five subscales of five items each. The subscales are emotional symptoms, conduct problems, hyperactivity/inattention, peer relationship problems, and prosocial behavior. All items are scored from 0 to 2, with 2 representing greatest expression of that attribute. Subscale totals therefore range from 0 to 10, and a total difficulty score from 0 to 40 is calculated by summing the first four subscales. Previous studies have provided evidence of reliability and validity for this instrument in general (Goodman, 1997; Janssens & Deboutte, 2009), and for children with ASD (Iizuka et al., 2010; Jones et al., 2014).

Data Analysis

IBM SPSS Statistics 25 was used for data review and analysis. To test the null hypothesis, a Welch’s t’ test was selected. This test was used to compare the difference between sample means on the quantitative dependent variables (CARS-2, Vineland-II, SDQ) when grouped by the independent variable (ASD label source) with two levels (medical diagnosis vs. special education eligibility). This test allows for comparisons when the sample sizes of the groups are not equal. Statistical significance testing was used with a p value set at .05. The distributions of CARS-2, Vineland-II, and SDQ in this sample were all unimodal and approximately normal with no severe skew or outliers, and thus the use of parametric testing methods is appropriate.

Results

Data were analyzed with a Welch’s t’-test for independent observations. The independent variable was the source of the child’s ASD label with two levels, medical diagnosis and special education eligibility. The dependent variable was autism symptom severity as measured by research staff/clinicians using CARS-2 total scores. Descriptive statistics for symptom severity by source of label are included in Table 2. Children who had a medical diagnosis of ASD were rated as having significantly greater symptom severity than those who only had a special education eligibility for ASD, t’ (18) = 2.39, p = .028, 95% confidence interval [CI] = [0.59, 9.06]. In addition, 8 of the 15 (53.3%) children who only had special education eligibility for ASD had total CARS-2 scores below 30, which is considered the clinical cut point for mild ASD on the measure. In contrast, only 10 of the 58 (17.2%) children with medical diagnoses fell below this threshold. This difference was significant, χ² (1, N = 73) = 8.36, p < .01.

Table 2.

CARS-2 Item Level and Total Scores.

Item	Medical diagnosis (n = 58)	Special education eligibility (n = 15)	t	p
Item	M (SD)	M (SD)	t	p
1. Relating to people	2.19 (0.65)	2.33 (0.86)	−0.61	.553
2. Imitation	1.47 (0.64)	1.50 (0.53)	−0.23	.833
3. Emotional response	2.47 (0.64)	2.23 (0.75)	1.14	.270
4. Body use	2.59 (0.75)	2.27 (0.88)	1.32	.202
5. Object use	2.05 (0.87)	1.73 (0.75)	1.41	.171
6. Adaptation to change	2.68 (0.84)	2.20 (0.94)	1.80	.086
7. Visual response	2.25 (0.73)	1.83 (0.67)	2.10	.047*
8. Listening response	2.56 (0.71)	2.00 (0.94)	2.15	.046*
9. Taste/touch/smell response and use	2.69 (0.91)	2.30 (0.86)	1.54	.136
10. Fear or nervousness	2.57 (0.86)	2.07 (0.84)	2.05	.052
11. Verbal communication	2.41 (0.80)	2.23 (0.70)	0.82	.421
12. Nonverbal communication	2.16 (0.80)	1.93 (0.78)	0.98	.338
13. Activity level	2.20 (0.78)	1.87 (0.74)	1.52	.142
14. Level/consistency of intellectual responses	2.72 (1.01)	2.20 (0.82)	2.06	.049*
15. General impressions	2.65 (0.57)	2.13 (0.77)	2.43	.005**
Total	35.66 (5.57)	30.83 (7.29)	2.39	.028*

Note. All 15 CARS-2 items are rated 1 to 4, with 4 meaning greatest ASD symptom severity. CARS-2 = Childhood Autism Rating Scale, second edition; ASD = autism spectrum disorder.

p < .05. **p < .01.

Welch’s t’-tests for independent observations were also conducted for the 15 individual items on CARS-2. Significant item-level differences were found between the groups on 4 of the 15 items, with those children with a medical diagnosis being rated as having greater symptom severity. The items with significant differences (p < .05) were visual response, listening response, level/consistency of intellectual responses, and general impressions. These results are reported in Table 2.

Welch’s t’-tests for independent observations were additionally run for the Vineland-II and SDQ subscale and overall scores. No significant differences were found between the medical diagnosis group and the special education eligibility group on the Vineland-II Adaptive Behavior Composite standard score t’ (19) = −0.18, p > .05, 95% CI = [−9.59, 8.05], or on any of the Vineland-II subscales. There were also no significant differences between groups on the total difficulties score of the SDQ, t’ (18) = 1.60, p > .05, 95% CI = [−0.81, 5.93], or any of the SDQ subscales. These results are summarized in Table 3.

Table 3.

Vineland-II and SDQ Subscale and Total Scores.

Scale	Medical diagnosis (n = 58)	Special education eligibility (n = 15)	t	p
Scale	M (SD)	M (SD)	t	p
Vineland-II composite score	74.63 (11.91)	75.40 (15.08)	−0.18	.857
Vineland-II communication	77.26 (16.84)	77.67 (14.95)	−0.09	.928
Vineland-II daily living	74.69 (15.37)	77.47 (24.43)	−0.42	.680
Vineland-II social	72.77 (12.43)	75.60 (13.87)	−0.72	.481
SDQ total difficulties	16.78 (4.24)	14.22 (5.83)	1.60	.128
SDQ conduct	2.16 (1.32)	2.28 (1.70)	−0.27	.789
SDQ hyperactivity	6.93 (2.07)	5.73 (2.81)	1.54	.140
SDQ emotional	2.90 (2.20)	2.40 (1.92)	0.87	.395
SDQ peer relations	4.79 (2.08)	3.80 (2.14)	1.61	.123
SDQ prosocial	5.57 (2.41)	6.20 (2.78)	−0.80	.431

Note. There were no significant between-group differences. Vineland-II = Vineland Adaptive Behavior Scales, second edition; SDQ = Strengths and Difficulties Questionnaire.

Differences in demographic variables were also compared using Welch’s t-tests for independent observations for categorical variables and chi-square tests for categorical variables. No significant differences were found between the two groups on the child’s age t’ (18) = 0.88, p > .05, parent’s age t’ (25) = 0.26, p > .05, parent’s years of education t’ (17) = −0.90, p > .05, or the family household income t’ (14) = −0.58, p > .05. Using the chi-square analyses, there was no significant difference in the proportion of families who had insurance between the medical diagnosis group and the special education eligibility group χ² (1, N = 73) = 0.81, p > .05, in the proportion of children in each group whose race/ethnicity was reported as White χ² (1, N = 73) = 3.36, p > .05, or in the proportion of children in each group who spent 80% or more of their school time in general education. These results are included as part of Table 1.

Furthermore, participants across both groups were compared based on the age at which they were first identified with ASD (in either a medical or school setting). There were no significant differences between those children identified at age 3 or before (n = 31) and those identified at age 4 or later (n = 37) on CARS-2, Vineland, or SDQ total scores, p > .05. There were also no differences on the CARS-2 or Vineland between children who had been recruited into the study in early childhood (n = 38), and those who had been recruited in later elementary school (n = 35), p < .05. However, caregivers of children recruited earlier into the study did report higherSDQ total scores, t’ (59) = 2.16, p = .035, 95% CI = [0.18, 4.52].

Discussion

Consistent with the primary research hypothesis, the results of the analyses indicate that children who received their ASD label via a medical diagnosis had more severe ASD symptomatology than those children a special education eligibility of ASD (without a medical diagnosis). This was found both in overall symptom severity on CARS-2, as well as on particular items pertaining to child visual, listening, and intellectual responses. However, results of this study failed to support the secondary hypothesis that parent-reported behavioral functioning of children in the two groups would also differ. Parent ratings of both adaptive behavior and behavioral challenges were not different between the groups. Taken together, these findings indicate that clinicians are perceiving differences in ASD-specific symptomatology in children with and without medical diagnoses, while parent reports of adaptive and maladaptive behaviors do not differ between groups.

Possible implications of these findings are that schools are identifying a subset of children with milder ASD that are missed by the medical community, or that schools are identifying children as having ASD who may not really have the disorder. Supporting the latter theory is the fact that over half of the children who only had special education eligibility for ASD had total CARS-2 scores below the diagnostic threshold on the measure. This theory is also supported by research that schools may need additional training in best practices for assessing ASD (Barton et al., 2016; Shtayermman, 2011). School evaluation teams often rely on parent- and teacher-completed rating scales of ASD symptomatology and behavior, without more comprehensive assessment or practitioner-completed measures (Aiello et al., 2017). The findings of this study indicate that parents and professionals may see things differently, further highlighting the importance of comprehensive ASD evaluations with multiple perspectives, as these may reveal unique characteristics and profiles. Individual school-based practitioners and even districts may want to consider including such criteria as mandatory during ASD special education evaluations.

It is also noteworthy that our hypothesis around demographic differences between the groups was not supported in this sample, in that there were no significant differences between those identified in the medical or school system on parent race/ethnicity, education level, household income, or health insurance status. Furthermore, all 15 families of children who had a special education ASD eligibility but not a medical diagnosis did report having health insurance. These findings indicate that the observed differences were not likely to have been caused by a lack of access to medical or clinical services. Future research should further investigate the evaluation techniques being utilized by schools to determine special education eligibility with an ASD classification and why their results may differ from medical or clinical methodologies.

Decisions about whether to find students with more mild symptomatology eligible with an ASD label have lasting impact, as these students may face lower expectations than peers and general misconceptions about their abilities in inclusive classrooms. Witmer and Ferreri (2014) found that students with ASD were commonly expected to reach grade-level achievement on no or few academic content standards. Others have found that some teachers hold basic misconceptions about students with ASD and their needs in inclusive classrooms (Barned et al., 2011; Segall & Campbell, 2012). Furthermore, several studies have indicated that teacher expectations in general may act as a self-fulfilling prophecy and be determinant of student behavioral or academic success (Workman, 2012). Given that eligibility is only required in one category to access special education services, school personnel serving in this “gatekeeper” role in access to service may want to consider other eligibility categories that could account for the same educational performance difficulties as ASD but are less stigmatizing. This is particularly true for students, such as those in the special education subsample of this study, who have low ASD symptomatology but nonetheless struggle with adaptive functioning (i.e., Vineland-II) and challenging behavior (i.e., SDQ). Further research could investigate how school expectations differ between students with relatively similar phenotypes but different eligibilities.

There are also some limitations to the present findings. One limitation is that there were far more children in the medical diagnosis group than the special education only group, which may limit the generalizability of some of our findings. This sample was recruited as part of a larger study, and therefore we did not specifically seek out individuals who had been identified in one system or the other. Further research in this area could specifically target children who are receiving special education services under ASD but do not have a medical diagnosis, as this is a subgroup of children with ASD about whom very little is known. In addition, gold standard measures of ASD diagnosis such as the Autism Diagnostic Observation Schedule, second edition (ADOS-2; Lord et al., 2012) and the Autism Diagnostic Interview- Revised (ADI-R; Lord et al., 1994) were not included in the battery of measures completed as part of this project. These may be helpful in future research to build on the information provided by the CARS-2 about children’s ASD traits and symptom severity in the two label source groups. Finally, we did not measure intelligence quotient (IQ) scores of participants as part of this study. Additional information on cognitive functioning could further elucidate differences in the profiles of children identified in the medical and school settings. This is an area that future research could address.

Conclusion

Children who are considered to have ASD are generally diagnosed either in a medical/clinical setting or as part of their school special education eligibility determination. This study provides an initial examination of differences in ASD symptom severity and adaptive functioning between these two subsets of children with an ASD label. Despite some methodological limitations, findings indicate that children with medical ASD diagnosis display greater ASD symptom severity as rated by an independent clinician but are not rated any differently by their parents on measures of behavioral functioning. These findings indicate differences may exist in children who receive an ASD label in these two ways, though further research is needed to determine the implications of these discrepancies.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded in part by a grant from the Fairway Foundation and the National Institutes of Health (grant no. R01 HD059838) awarded to the second author.

References

Aiello

Ruble

Esler

(2017). National study of school psychologists’ use of evidence-based assessment in autism spectrum disorder. Journal of Applied School Psychology, 33(1), 67–88.

Allen

R. A.

Robins

D. L.

Decker

S. L.

(2008). Autism spectrum disorders: Neurobiology and current assessment practices. Psychology in the Schools, 45, 905–917.

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Publishing.

Barned

N. E.

Knapp

N. F.

Neuharth-Pritchett

(2011). Knowledge and attitudes of early childhood preservice teachers regarding the inclusion of children with autism spectrum disorder. Journal of Early Childhood Teacher Education, 32(4), 302–321.

Barton

E. E.

Harris

Leech

Stiff

Choi

Joel

(2016). An analysis of state autism educational assessment practices and requirements. Journal of Autism and Developmental Disorders, 46, 737–748.

Centers for Disease Control and Prevention. (2018). Prevalence of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2014. Morbidity and Mortality Weekly Report, 67(6), 1–23.

Goodman

(1997). The Strengths and Difficulties Questionnaire: A research note. Journal of Child Psychology and Psychiatry, 38, 581–586.

Iizuka

Yamashita

Nagamitsu

Yamashita

Araki

Ohya

. . . Matsuishi

(2010). Comparison of the Strengths and Difficulties Questionnaire (SDQ) scores between children with high-functioning autism spectrum disorder (HFASD) and attention-deficit/hyperactivity disorder (AD/HD). Brain & Development, 32, 609–612.

Individuals with Disabilities Education Act, 20 U.S.C. § 1400 (2004).

10.

Janssens

Deboutte

(2009). Screening for psychopathology in child welfare: The Strengths and Difficulties Questionnaire (SDQ) compared with the Achenbach System of Empirically Based Assessment (ASEBA). European Child & Adolescent Psychiatry, 18, 691–700.

11.

Jones

Hastings

R. P.

Totsika

Keane

Rhule

(2014). Child behavior problems and parental well-being in families of children with autism: The mediating role of mindfulness and acceptance. American Journal on Intellectual and Developmental Disabilities, 119, 171–185.

12.

Lord

DiLavore

P. C.

Gotham

(2012). Autism diagnostic observation schedule. Western Psychological Services.

13.

Lord

Rutter

Le Couteur

(1994). Autism Diagnostic Interview—Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. Journal of Autism and Developmental Disorders, 24, 659–685.

14.

MacFarlane

J. R.

Kanaya

(2009). What does it mean to be autistic? Inter-state variation in special education criteria for autism services. Journal of Child and Family Studies, 18(6), 662–669.

15.

Mandell

D. S.

Palmer

R. F.

(2005). Differences among states in the identification of autistic spectrum disorders. Archives of Pediatrics and Adolescent Medicine, 159, 266–269.

16.

National Center for Education Statistics. (2017). Children 3 to 21 years old served under Individuals with Disabilities Education Act (IDEA), Part B, by type of disability. https://nces.ed.gov/programs/digest/d17/tables/dt17_204.30.asp

17.

Oregon Administrative Rules for Special Education. (2013). Oregon Department of Education. https://secure.sos.state.or.us/oard/displayDivisionRules.action?selectedDivision=2556

18.

Schopler

Van Bourgondien

Wellman

Love

(2010). The Childhood Autism Rating Scale—Second edition. Western Psychological Services.

19.

Segall

M. J.

Campbell

J. M.

(2012). Factors relating to education professionals’ classroom practices for the inclusion of students with autism spectrum disorders. Research in Autism Spectrum Disorders, 6, 1156–1167.

20.

Shtayermman

(2011). Lee A. Wilkinson: A best practice guide to assessment and intervention for autism and asperger syndrome in schools. Journal of Autism and Developmental Disorders, 41(8), 1137.

21.

Sparrow

S. S.

Cicchetti

D. V.

Balla

D. A.

(2005). Vineland Adaptive Behavior Scales (2nd ed.). American Guidance Service.

22.

Sullivan

A. L.

(2013). School-based autism identification: Prevalence, racial disparities, and systemic correlates. School Psychology Review, 42(3), 298–316.

23.

Volker

M. A.

(2012). Introduction to the special issue: High-functioning autism spectrum disorders in the schools. Psychology in the Schools, 49, 911–916.

24.

Witmer

S. E.

Ferreri

S. J.

(2014). Alignment of instruction, expectations, and accountability testing for students with autism spectrum disorder. Focus on Autism and Other Developmental Disabilities, 29(3), 131–144.

25.

Workman

(2012). The progress of education reform: Teacher expectations of students [Report]. Education Commission of the States.