Iona University Journal of Allied Health Articles

November 2020

  1. Bilingual Education in Children with Autism: Shedding Light on Misconceptions
    • Jhovana Figueroa, MA, CCC-SLP
    • Shahmeen Khan, MA, CCC-SLP
  2. A Case of Lagging Inattention: Revisiting the Testing Protocol of Autism Spectrum Disorder
    • Michael Bruno

Bilingual Education in Children with Autism: Shedding Light on Misconceptions

Jhovana Figueroa, MA, CCC-SLP
Shahmeen Khan, MA, CCC-SLP

Jhovana Figueroa is an alumna of Iona University’s graduate program in Communication Sciences and Disorders. She is employed as a speech-language pathologist at the McCarton Center in the Bronx, N.Y.
Email: jhovanafigueroa@gmail.com

Shahmeen Khan is an alumna of Iona University’s graduate program in Communication Sciences and Disorders. She is employed as a speech-language pathologist at Stepping Stones Group in Boston, MA.

According to the Center for Disease Control and Prevention Autism Spectrum Disorder (ASD) affects approximately 1-2% of the population worldwide. For children from bilingual environments who are diagnosed with ASD, caregivers are faced with an additional dilemma. What language should I speak with my child? Some health professionals and doctors make a surprising recommendation: they encourage the caregiver to only speak in one language so as not to confuse their child. In this commentary we will discuss the previous and current research about bilingual education as well as answer the following question: What language(s) should be spoken to a bilingual child with ASD?  

The turn of the twentieth century saw an uptick in immigrants from other (primarily European) nations which led to an increase in the linguistic diversity of Americans. Researchers took this opportunity to study the effects of bilingualism on speech and language development using psychometric measures. The research implied that immigrants newly arrived in the United States of America were less intelligent than monolingual American citizens (Hakuta, 1986). One of the chief flaws of this research was that this testing focused only on the abilities of bilinguals to speak in English and neglected to test their abilities in their native languages (Hakuta, 1986). The research participants were also primarily immigrants whose language ability varied depending on the amount of time spent living in the United States (Hakuta, 1986). Researchers described bilinguals as “feeble-minded,” or as having the “language handicap” of bilinguals (Hakuta, 1986). Further research done in the mid-twentieth century attributed this deficit and language handicap to bilingualism (Thompson, 1952 as cited in Hakuta,1986). These researchers discouraged bilingualism as they believed it was detrimental to language development. They reasoned that low intelligence was caused by bilingualism (Hakuta, 1986).

Even when research showed the opposite conclusion, the studies were not well designed enough to provide conclusive evidence.  Research conducted in the 1980s utilized cognitive tests to re-evaluate the abilities of bilinguals and began to find that bilinguals perform better than monolingual English-speaking children (Hakuta, 1986). This research, however, had its own flaws.  Participants were not studied longitudinally and the degree of language proficiency in both languages was not examined (Hakuta, 1986). Thus, heightened cognitive abilities could not be attributed to bilingualism as the children the researchers studied coincidentally may have had higher cognitive skills (Hakuta, 1986). However, this research led researchers to further study a possible relationship between cognitive abilities and bilingualism.

Research conducted within the last 15 years supports the link between bilingualism and heightened cognitive ability, suggesting that being bilingual is not a “handicap” as early research had suggested, but in fact, it is a strength as the following examples highlight. Given an optimal environment, children can develop reading and writing skills in their second language without much difficulty (Pray et al., 2009). An optimal environment would consist of an instructional model that focuses on building reading and writing skills in both languages, such as a dual language program (Pray et al., 2009).  Findings from recent studies support the notion that bilingualism does not cause a language delay and, intellectually, bilingual individuals perform better than their monolingual peers in the school setting (Al-Amri, 2013). Overall researchers have found that bilingual children have higher cognitive skills than monolingual children (Al-Amri, 2013).

Despite recent advances in research that have overturned longstanding misconceptions about the effect of bilingualism on learning, the earlier flawed research has left a lasting impression on clinicians and other researchers that bilingualism produces a negative impact on children’s language development (Kremer-Sadlik, 2005). This may be because the early research has been around longer and more people are familiar with it along with the fact that bilinguals are a minority and professionals have limited experience with them and consequently less insight into their lives. For example, many speech-language pathologists report that the caregivers of their bilingual clients are advised by health professionals to speak in only one language with their children (Kremer-Sadlik, 2005; Zhou et al., 2019).

This is reportedly more common among parents of children who have been diagnosed with Autism Spectrum Disorder (ASD) (Kremer-Sadlik, 2005). ASD consists of social pragmatic deficits and expressive-receptive language deficits that are not typically present within other language impairments or most other cognitive disorders (Hambly & Fombonne, 2012; Park, 2014). Impairment associated with ASD includes difficulty with maintaining attention and focusing on voices other than theirs, which are critical skills for language acquisition. These children display their impairment in symptoms that range in severity from mild to severe (Park, 2014).

According to national surveys, 1 in 40 children are diagnosed with ASD. Moreover, as of 2018 there are 1,135,334 students in the NYC school system, the largest school district in the United States. Of those students:

  • 13.5% of students are English Language Learners
  • 19.7% are students with disabilities
  • 74.0% are economically disadvantaged
  • Race or ethnicity:
    • 40.5% Hispanic
    • 26.0% black
    • 16.1% Asian
    • 15.0% white (New York City Department of Education, 2019)

Further breakdown of this data reveals that Asian and Latino students are more prone to be diagnosed with language impairment among students with disabilities. Specifically, 38% of Asian students with disabilities and 36% of Latino students with disabilities are diagnosed with language impairment, compared to 26% of black and 28% of white students (DOE, 2019).

Why the disparity? This disparity in diagnosis rates can be attributed to the cultural and linguistic differences of Asian and Latino groups (Estrem & Zhang, 2010). Asian and Latino students are more likely to be diagnosed with ASD, a disorder that impacts language development. Because ASD affects language development, caregivers and professionals are concerned about the acquisition of a second language in a child with ASD, when language acquisition can be a challenge no matter the language (Durán, Kohlmeier & Lund, 2017).

However, advising families to speak only one language to their child results in social stigmatization from members of their linguistic community and deprives a child of the benefits of both languages in terms of diverse cultural experiences (Zhou et al., 2017). When monolingual Spanish families are instructed to speak in English to their children with ASD, it results in children being isolated from their family and thereby decreases the amount of linguistic interaction the child receives. For example, a child may be sitting at the dinner table having a conversation primarily in the non-English language. Because the child does not have proficiency in the non-English language, they are not able to interact or interact in the experience at the dinner table. This in turn affects language development as these children do not have access to typical social interactions within the home if their families are communicating in the non-English language (Kremer-Sadlik, 2005).

In our clinical experience, we have observed that many Spanish speakers are afraid to speak Spanish to their children with ASD because they believe that the use of a second language would create a more difficult language learning environment. As a result, language input can diminish, impacting language learning and development (Kremer-Sadlik, 2005).

Students diagnosed with ASD are not all monolingual. In fact, numerous bilingual Spanish speakers diagnosed with ASD are educated in public schools. Many of these children are inhibited academically and socially because their caregivers are instructed to strictly speak English at home. This leads to the abandonment of fluent Spanish language interactions for less fluent English interactions. These limited experiences and poor language models affect the child’s social and academic performance in school. On the other hand, current research suggests that exposing children to both languages is beneficial. In fact, bilingual children perform better than monolingual children. However, much of the research examines cognitive skills or intelligence but does not examine receptive and expressive language skills in children with ASD. Since the most common advice given to bilingual caregivers is to only speak in English to their child, we would like to demonstrate that bilingualism has a positive impact on receptive and expressive language skills for those diagnosed with ASD. Therefore, we propose a study that examines the receptive and expressive language abilities of bilingual Spanish-English school age children with ASD in comparison to their monolingual peers who are diagnosed with ASD.

Current research suggests that children presenting with ASD can function in a bilingual environment and that bilingualism does not cause or further any language disorder. When compared to monolingual peers, bilinguals diagnosed with ASD had similar or better receptive and expressive language (Duran, Kohlmeier, & Lund, 2017). Duran and Lund (2017) conducted a systematic review of the current research regarding children with ASD who come from bilingual or multilingual homes. They found that bilingualism has no adverse effect on language development (Lund & Duran, 2017). These findings were mirrored in a number of other studies (Hambly & Fombonne, 2012; Ohashi et al., 2012; Peterson, Marinova & Mirenda, 2012). There is ample research to support bilingualism in individuals diagnosed with ASD.

Research also suggests that when a bilingual child presenting with ASD is raised to be monolingual, negative effects for both the child and their families were present such as poor vocabulary development and reduced receptive and expressive language development.  Hambly and Fombonne (2014) studied vocabulary size in bilingual children with ASD by using the MacArthur-Bates Communicative Development Inventories (Macarthur CDI) to measure expressive language. The researchers divided the participants into groups based on their scores (high score, low score) and whether they were bilingual or monolingual. They found that compared to their monolingual peers, the bilingual participants who scored high had larger vocabularies than the monolingual participants (Hambly & Fombonne, 2014). It should be noted that bilingual participants had stronger L2 skills which in turn led to their higher expressive language scores (Hambly & Fombonne, 2014). The study therefore concluded that children presenting with ASD who are born in a bilingual context should be brought up in a bilingual environment (Beuchamp & MacLeod, 2017).

However, these studies examined participants from diverse linguistic backgrounds and ages. Therefore, the interpretations cannot be generalized to a specific language-speaking population or age group. Additionally, the previous research studied language abilities of children with disabilities varying in severity, making clinical application difficult.

To better understand the impact of language exposure on bilingual children diagnosed with ASD, we propose a comparative study of monolingual children with ASD and bilingual Spanish speaking with children with ASD. This study would determine the impact of the acquisition of a second language (Spanish) on the speech and language development of school-age children with ASD. This cross-sectional study could elucidate the question of how Spanish-English bilingualism impacts language development in school-age children diagnosed with moderate to severe ASD. Since children exhibit differing expressive and receptive language skills based on where they are on the spectrum, we have chosen to focus on moderate to severe ASD as this group is more prone to expressive and receptive language deficits as compared to children with mild severity ASD.

Because of the early research conducted on bilingualism, many health care professionals today provide misguided advice to parents about the effects of bilingualism on children with ASD. In order to dispel these misconceptions, we propose a comparative study examining the effects of Spanish-English bilingualism on expressive language development in school-aged children presenting with moderate to severe ASD, when compared to age-matched English monolingual school-aged children (5;0-10;0 years) presenting with moderate to severe ASD.  We suggest that the researchers utilize a checklist of communication skills and an expressive vocabulary assessment in Spanish and English to gain information on the participant's’ home lives, language skills, and the amount of exposure to two or more languages (LeCouteur et al., 2003). Similar to previous studies that examined language development in bilingual children with ASD, we will use the MacArthur CDI - a parent-report instrument for assessing communicative skills in infants and toddlers (Fenson et al., 2007). Although this instrument is not meant for this age group, the questionnaire is designed in such a way that it is effective in collecting descriptive information to determine communication skills and expressive language output of the participants. In addition, researchers would administer the Expressive Vocabulary Test Second Edition (EVT-2) and use it to assess expressive vocabulary skills of all participants in both languages.

We hypothesize that it is likely that a bilingual language environment does not contribute to expressive language deficits in Spanish-English speaking children with ASD. In fact, if our results support those of recent studies, a bilingual language environment would likely enhance expressive language abilities in Spanish-English speaking children with moderate to severe ASD. Research of this nature may also impact the structure of the educational system and improve the educational experience of bilingual children due to the prevalence of ASD in school-age Spanish-English bilinguals. Through these research efforts we hope to improve the linguistic experience of bilingual children diagnosed with ASD and provide grounds to advocate for dual language programs in public schools.

Al-Amri, M. N. (2013). Effects of bilingualism on personality, cognitive and educational developments: A historical perspective. American Academic & Scholarly Research Journal, 5(1), 1-7.

Beauchamp, M. L., & MacLeod, A. A. (2017). Bilingualism in children with autism spectrum disorder: Making evidence-based recommendations. Canadian Psychology/psychologie canadienne, 58(3), 250.

Estrem, T., & Zhang, S. (2010). Prevalence and disproportionality of autism spectrum disorders among English language learners in Minnesota. Multiple Voices for Ethnically Diverse Exceptional Learners, 12(2), 5-20.

Fenson, L. (2007). MacArthur-Bates communicative development inventories. Baltimore, MD: Paul H. Brookes Publishing Company.

Hakuta, K. (1986). Cognitive development of bilingual children. Center for Language Education and Research, 5-11.

Hambly, C., & Fombonne, E. (2012). The impact of bilingual environments on language development in children with autism spectrum disorders. Journal of autism and developmental disorders, 42(7), 1342-1352.

Kremer-Sadlik, T. (2005). To be or not to be bilingual: Autistic children from multilingual families. In Proceedings of the 4th International Symposium on Bilingualism (pp. 1225-1234.)

Le Couteur, A., Lord, C., & Rutter, M. (2003). The autism diagnostic interview-revised (ADI-R). Los Angeles, CA: Western Psychological Services, 659-685.

Lund, E. M., Kohlmeier, T. L., & Durán, L. K. (2017). Comparative language development in bilingual and monolingual children with autism spectrum disorder: A systematic review. Journal of Early Intervention, 39(2), 106-124.

DOE Data at a Glance - NYC.gov

Ohashi, J. K., Mirenda, P., Marinova-Todd, S., Hambly, C., Fombonne, E., Szatmari, P., ... & Volden, J. (2012). Comparing early language development in monolingual-and bilingual-exposed young children with autism spectrum disorders. Research in Autism Spectrum Disorders, 6(2), 890-897.

Park, H. I., & Ziegler, N. (2014). Cognitive shift in the bilingual mind: Spatial concepts in Korean–English bilinguals. Bilingualism: Language and Cognition, 17(2), 410-430.

Pray, L., Jiménez, R. T., & Cummins, J. (2009). Developing Literacy in Second-Language Learners/A Response to Developing Literacy in Second-Language Learners. Educational Researcher, 38(5), 380.

Petersen, J. M., Marinova-Todd, S. H., & Mirenda, P. (2012). Brief report: An exploratory study of lexical skills in bilingual children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 42(7), 1499-1503.

Zhou, V., Munson, J. A., Greenson, J., Hou, Y., Rogers, S., & Estes, A. M. (2019). An exploratory longitudinal study of social and language outcomes in children with autism in bilingual home environments. Autism, 23(2), 394-404.


A Case of Lagging Inattention: Revisiting the Testing Protocol of Autism Spectrum Disorder

Michael Bruno

Michael Bruno is a 2020 graduate of Iona University. He is a Psychology and Adolescent Education double major with a concentration in English Literature.
Email: mbruno2iona@gmail.com

Autism Spectrum Disorder (ASD)-related diagnoses have risen substantially in the last decade. In 2018 the estimated prevalence ratio was 1:59, a rise of 15% since 2012 when it was 1: 68 (Pathak et al., 2019). The various disorders of the spectrum range from Autism to Asperger's to pervasive developmental disorders. Despite the advance of a spectrum-diagnostic based perspective, screening instruments have not kept abreast with this progress in reclassifying these disorders. This lag may be attributed to the reluctance to develop representative and more holistic tests; such an effort requires time-consuming and costly research.

Consequently, screenings have remained reliant on categorical measures and have not advanced to more nuanced multidimensional measures, even though researchers and clinicians have recommended this change for the past 4 years. While categorical measures do identify the presence of specific symptoms and increase the validity of diagnoses, they also create the possibility of comorbidity, misdiagnosis, or underdiagnosis in genetically and behaviorally overlapping disorders. With their ability to assess the severity of behavioral, cognitive, and emotional symptoms, such multidimensional measures have been proposed as the first step towards an individualized therapeutic and long-term care treatment plan for ASD patients (Xavier, Bursztejn, Stiskin, Canitano, & Cohen, 2015). Multidimensional screenings importantly identify which personality traits are affected and to what degree a disorder affects functioning. Overall, greater detail is given within multidimensional testing than in categorical screening to identify the social skills constructs that affect behavior.

Of greater concern is that current ASD categorical screenings display both production and symptom-based confounds. First, the standardization processes of ASD instruments (and, in fact, all psychological screenings standardizations) display gender bias and minority underrepresentation. Females and Hispanics are underrepresented within standardization samples in psychological measurements. Second, categorical measures result in high comorbidity and a failure to distinguish between disorders and similar symptoms from other medical conditions. Categorical screening instruments for the disorder lack the comprehensiveness and depth of holistic diagnosis. They also lack specificity and divergent validity, thereby making it difficult for the psychological community to distinguish among multiple constructs. This outcome then results in impaired construct validity and generalizability.

In order to more fully address the weaknesses of current testing protocols, I will focus on two screenings and the problems posed by them. These screenings are the Childhood Autism Rating Scales, second edition (CARS2) and the Social Responsiveness Scale, second edition (SRS-2). I chose these tests because they are each anchored at the respective ends of the theoretical spectrum encompassing categorical and multidimensional viewpoints. At one end of the spectrum is CARS2, a categorical test, that is widely used and provides a simple checklist of ASD related behaviors and constructs. At the other end of the spectrum is SRS-2, a two-dimensional screening that can assess ASD’s degree of impairment on social and communication skills. It represents an advance, but it does not yet deliver the benefits gained from multidimensional and holistic diagnostic criteria. I address each screening’s ability to identify minority groups and intellectually challenged individuals. and the similarity of the scales to other ASD screening instruments. Finally, I propose a screening protocol that allows for accurate diagnosis and long-term treatment planning to correct and enhance the quality of ASD screenings that patients receive. This protocol uses both categorical and multidimensional screenings for ASD. This protocol will also allow for convergent and divergent validity to be higher than either test type alone. Convergent validity will be increased within each test type and divergent validity will be increased within multidimensional screenings, thereby solving the second confound of comorbidity and low distinction between certain symptoms and disorders.

Implications of the Lag in the Testing Protocol:

Outmoded and inaccurate testing leaves minorities, particularly females and Hispanics, undiagnosed or misdiagnosed. A disparity of ASD diagnoses exists between Caucasians and Hispanics and between genders in the United States. The weighted population prevalence for ASD-related disorders in America from the years 2014 to 2016 is estimated at 2.47%. Boys had a prevalence percent of 3.63% and females had a prevalence of 1.25% for ASD. Hispanics had a 1.82% prevalence, non-Hispanic African-Americans had a 2.49% prevalence, and non-Hispanic whites had a 2.76% prevalence (Xu, Strathearn, Liu, & Bao, 2018). Another bias of CARS2 might be that ASD standardization is regional at the TEACH Center in North Carolina.

Minorities are already at a disadvantage in the U.S. because they lack access to healthcare. Dobbs (2011) claims that because ASD is diagnosed through behavioral expression observed by a clinician, children without access to healthcare may be underdiagnosed. Hispanic children, in particular, are pushed out of healthcare due to economic factors, including lack of insurance, higher household poverty rates, and lack of referrals to clinical specialists. Hispanic children are twice as likely to be uninsured than Caucasians and are three times more likely to live households below the poverty line (Dobbs, 2011).

More recent research further confirms ethnic disparities in ASD diagnosis. Healthcare inequality and ASD diagnosis disproportions held steadily from 2002 to 2010 with a positive SES and ethnicity gradient observed (Durkin et al., 2017). Researchers already know that a positive socioeconomic status gradient exists in ASD diagnosis in the United States; thus, the wealthier a person is, the more likely they are to be diagnosed early and receive treatment. Countries with free and universal healthcare, such as in France and Sweden, do not correlate wealth with ASD diagnosis (Durkin et al., 2017). There has been little or no measurable progress within the United States in the last eighteen years to make ASD screening more equitable and accessible.

In addition to diagnostic disparities in ethnicity, females are also diagnosed for ASD at lower rates than males, a phenomenon which has led researchers such as Baron-Cohen (2002) to investigate and conclude that lower diagnosis rates among females are a result of biological factors. In his theory, the “Extreme Male Brain” hypothesis, Baron-Cohen (2002) argues that boys observed with autism have heightened male-like behavioral expression which is caused by prenatal exposure to excessive androgens. While there is some empirical support for the Baron-Cohen (2002) hypothesis, other factors may affect the ASD gender ratio.

For example, current screenings may not be able to diagnose females with ASD because they posit and apply the wrong constructs (Wijngarrden-Cremers, Van Eetenm, Groen, Van Deurzen, Oosterling, & Gaag, 2014). Since the criteria for ASD diagnosis was developed exclusively around males, the screenings may be perpetuating a gendered clinical bias. Frazier, Georgiades, Bishop, & Hardan (2014) found that these symptom differences between genders could not be attributed to the scores of the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS) screenings. (Frazier et al., 2014). The possibility of ascertainment bias exists towards females because diagnosis is defined by male symptom profiles (Wijngarrden-Cremers et al., 2014).

Nevertheless, a female ASD symptom profile is slowly emerging from research. This female profile of ASD displays greater social-communication deficits, lower cognitive ability, weaker adaptive skills, lower levels of restricted interests, and greater externalized behavioral problems than boys (Frazier et al., 2014). More research and sampling standardization must take place that includes females with ASD who have an average to high intelligence because they were not included in the original sampling (Frazier et al., 2014). This inclusion of females in standardization samples will help determine whether the emerging female symptom profile is accurate or not.

To promote social equity within all phases of American healthcare delivery, researchers and clinicians need to provide updated, accurate, and culturally-responsive diagnostic tools that accurately diagnose all patients and lay the groundwork for effective long-term treatment plans. Healthcare professionals need to create more comprehensive testing instruments that use multidimensional screenings that are equitable to both females and to ethnically diverse sample groups.

Test Confounds Summarized:

Problems in diagnosis are further compounded by test confounds that afflict ASD test protocols. Current categorical screenings result in a low specificity distinction between ASD, ASD related conditions, and non-associated medical conditions. Autism, in particular, is highly co-morbid with Attention-Deficit-Hyperactive-Disorder. Test confounds in four prominent ASD screenings were found by Havdahl et al. (2016). Both the convergent and divergent validity of the tests are affected by the screening’s symptom confounds. The two symptom confounds of ASD tests affecting divergent validity were intelligence quotient and emotional and behavioral problems. The Autism Diagnostic Observation Schedule (ADOS) confounds concern emotional and behavioral problems. ADOS was found to be unable to assess differences between those with ASD and those with behavioral problems (Havdahl et al., 2016). ADOS, SRS, and Autism Diagnostic Interview-Revised (ADI-R) were all found to have this emotional behavior confound. SRS and ADI-R are the two scales that were influenced by IQ (Havdahl et al., 2016). All of these screenings displayed a confound with emotional and behavioral problems, a result that leads us to question whether CARS2 and other symptom-based tests have similar emotional and behavioral confounds.

CARS2 and SRS-2: A Tale of Two Tests

Despite the demonstrated problems with these instruments, categorical measures still predominate over multidimensional screenings. One possible reason is likely due to the health care industry’s mandate for higher profits and low costs, which gives them an economic incentive to insist upon hasty diagnostic screenings. Another reason is that it takes time, sometimes years, to create and standardize a new psychological screening tool, which serves as a costly deterrent to researchers in need of funding.

The Childhood Autism Rating Scale-2, and SRS-2 best represent the problems and benefits of categorical and two-dimensional screenings, respectively. As opposed to the first edition, CARS2 includes a new edition which allows for the diagnosis of high-functioning ASD. CARS2, however, still relies on previous data from the first edition as a basis for the technical justification of its scales. While CARS2 can assess for the presence of ASD, SRS-2 assesses for the disorder's presence as well as its impact upon social and communication skills.

Developed in 2010, CARS2 is a multidisciplinary tool that is frequently used by professionals to diagnose school-aged children for ASD. The main strength of CARS2 is its deference to research-based findings. CARS2 draws on professional autism diagnostic standards and symptoms to establish criterion-related validity. The replicability measure of CARS2 is high with a .93 internal consistency, which ensures that the different item types measure the same construct while producing similar scores to a 93 percent degree of accuracy. A low Standard Error of Measurement of .68 accompanies the test due to its short length. This results in a 68 percent confidence interval (Malcolm, 2014). A second strength of CARS2 is the short time of five to ten minutes required to administer the test. Quite user-friendly, the scale is utilized and administered by a wide variety of professionals including psychologists, physicians, special education teachers, and speech pathologists.

However, CARS2 exemplifies some confounds in autism screenings: outmoded, misrepresentative data that may no longer be generalizable to the current population and highly comorbid symptoms. The data sample of CARS2 is taken from the first edition which relied on data from 1980 (Malcolm, 2014). Prizant found a coefficient alpha of .81 in his review of the first edition of CARS (Prizant, 1992). This coefficient alpha demonstrates a high validity for the CARS. However, the data underlying CARS2 is nearly forty years old and taken from the screening’s first edition.

Moreover, CARS2 has restricted demographic and ethnic representation in the standardization sample. Few minorities were included in the development of the second standard edition of the assessment. Of the 1034 Autistic individuals who participated in the CARS2 sample, 60 percent were white, and 78 percent were male. Prizant (1992), Malcolm (2014) and McCellan (2014) propose that studies must be undertaken to rectify the samples' lack of demographic alignment with populations in their reviews (Malcolm, 2014; McCellan, 2014; Prizant, 1992). I concur with these reviewers who point out that CARS2 must be revised and standardized using more ethnically and gender-diverse samples. Screenings that diagnose Caucasians more often than minorities may attributed to a lack of representation inherent in the original, fairly homogenous sample (Malcolm, 2014).

Unlike CARS2, the Social Responsiveness Scale, second edition (SRS-2), developed in 2012, measures and identifies autism social symptoms within a naturalistic observation framework. The SRS-2 includes a parent survey of symptoms directed towards individuals older than two and a half years of age. Moreover, it obtains results from the professional observing the client in normal situations. The scale was developed using parent and teacher ratings of ASD-related behaviors. One strength of SRS-2 is that it has greater diversity than CARS2. SRS-2 ethnic diversity figures align with U.S. Census figures. A second strength is its high reliability shown by the alpha coefficient of .90. Inter-rater and test-retest were found to have modest to strong correlation coefficients. The scale is highly valid with sensitivity (.84-.93) and the publisher reported specificity to ASD (.90-.95). Another strength is that SRS-2 allows one to assess the severity imposed upon constructs of social impairment and social communication skills using a two-dimensional model (Hoff & Doepke, 2014).

However, the SRS-2 does demonstrate some weaknesses. Researchers have found SRS-2 to have construct confounds that lower its specificity. These constructs are low intellectual ability and emotional and behavioral problems, which resulted in lower discriminative thresholds for the screenings (Havdahl et al., 2016). Incidentally, the symptom confounds of these screenings are like those symptoms presented in the female profile. However, correlation does not prove causation. This case of matching confounds between elevated screening scores and females presenting symptom profile warrants further study. These researchers must first make sure that the standardization samples have more female participants than previous studies and samples. Future researchers must find what is causing female symptomatic profiles to be different from that of males. Screenings must be closely examined to assess whether their gender bias criteria may lead to misdiagnoses.

Despite the recommendations by recognized authorities to further study categorical and dimensional methods used together within ASD diagnosis, multidimensional screenings are lacking. There is currently very little research incorporating multidimensionality into autism screening and subsequent therapeutic techniques. One possible reason is that autism was categorized as a spectrum disorder in 2013, so it is still a relatively new diagnosis label.

However, one new diagnostic manual that takes multidimensionality into account and addresses the problems of diagnostic confounds and inaccurate severity specifiers is the Diagnostic Statistical Manual, 5th edition, and the International Classification of Diseases, 11th edition. In addition, the scale developed by Jodi Yager and Grace Iarocci, the Multidimensional Social Competence Scale (MSCS), measures social competence of young adults (Trevisan, Tafreshi, Slaney, Yager, & Iarocci, 2018). Validated for both ASD young adults and the general population, this scale measures social competence by seven domains which allow for the identification of which factors are indicative of social responsiveness, social understanding and emotional regulation. This scale may be the first step towards an individualized index of social functioning when assessed at a certain point in a person's life. Trevisan et al. (2018) state that more longitudinal research is needed to increase convergent validity for the MSCS screening. This effort will take years of test development and need appropriate standardization samples (Trevisan et al., 2018). Hopefully, the MSCS will ultimately allow a more holistic view of young adults' deficiencies in socialization skills than categorical tests permit.

A multidimensional approach to screening and later treatment could also allow for holistic treatment throughout a patient's lifetime. (Xavier et al., 2015). Certainly, more research and clinical practice of multidimensional developmental screenings and treatments are necessary to try to distinguish the symptoms of one disorder from another. Categorical screenings are certainly valid and effective, but they do not holistically describe the patient’s range of functioning.

ASD instruments and their standardization samples suffer from minority underrepresentation. Healthcare practitioners need to provide updated, accurate, and culturally responsive diagnostic tools so that all patients are accurately diagnosed and have effective long-term care plans. For example, outdated CARS data published in the first edition and included in the second, along with misrepresentative standardization samples may be contributing to a low diagnosis rate among Hispanics and females. Accurate and holistic diagnostic protocols utilizing both categorical and multidimensional measures that capture specific domain impairment are practically non-existent, despite the widespread acceptance of an ASD spectrum. Increasing multidimensionality in screening is a major challenge that will take time and investment.

Researchers and the healthcare community must rectify the uniquely American ethnic and social-economic ASD diagnosis disparity by offering revised screenings, protocols, and more affordable coverage for all Americans. The healthcare community and researchers must balance diagnosis with holistic descriptions, specifically considering how a disorder affects an individual’s behavioral and emotional functioning.

For future screening for ASD individuals, I propose a two- screening protocol. Firstly, categorical tests will be used to assess for autism. Secondly, multidimensional tests will be used to assess for severity of specific symptoms. This two-screening protocol for diagnosing ASD using both categorical and multidimensional screenings, respectively, will be useful to make an efficient and accurate diagnosis. The protocol would be a first step towards rectifying neglect by the health care industry and ASD researchers of Hispanics and females. Proper multidimensional screenings can bring about a revolution long overdue to healthcare fields by allowing for the individualization of long-term treatment plans.

The author thanks the editorial board of the Iona University Journal of Allied Health: Perspectives on Social Justice, and its general editor, for their continued dedication to publishing student work. The author also wishes to express gratitude to Dr. Sharon Kennedy-Nolle for her extensive writing and editing support throughout the revision process. The author also wishes to thank the faculty members of the departments of Psychology, English, and Education Departments at Iona University for their wonderful teaching and support throughout his undergraduate career.

American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

Baron-Cohen, S. (2002). The Extreme Male Brain Theory of Autism. Trends in Cognitive Science, 6 (6),248-254. doi:

Carlson, J. F., Geisinger, K. F., & Jonson, J. L. (Eds.). (2014). The Nineteenth Mental measurements yearbook. Nebraska: The Board of Regents of the University of Nebraska and the Buros Center for Testing.

Clark, L.A., Cuthbert, B., Lewis-Fernández, R., Narrow, W.E., & Reed, G.M. (2017). Three Approaches to Understanding and Classifying Mental Disorder: ICD-11, DSM-5, and the National Institute of Mental Health’s Research Domain Criteria (RDoC). Psychological Science in the Public Interest 18(2), 72–145. doi:

Division of Birth Defects, National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention. (2018). What is Autism Spectrum Disorder? United States Government.

Division of Birth Defects, National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention. (2018). Research on Autism Spectrum Disorder. United States Government.

Dobbs, R.W. (2011). Autism Spectrum Disorders in Hispanic Communities: Socio-Cultural and Health Care Barriers to Diagnosis and Treatment. The Residents Journal of American Journal of Psychiatry, 6 (11), 11-14.

Durkin, M.S., Maenner, M.J., Baio, J., Christensen, D., Daniels, J., Fitzgerald, R., Imm, P., Lee, L., Schieve, L.A., Braun, K.V.N., Wingate, M.S., & Yeargin-Allsopp, M. (2017). Autism Spectrum Disorder Among US Children (2002-2010): Socioeconomic, Racial, and Ethnic Disparities. American Journal of Public Health, 107 (11), 1818-1826. Hyperlink:

Frazier, T.W., Georigiades, S., Bishop, S.L., & Harda, A.Y. (2014). Behavioral and Cognitive Characteristics of Females and Males with Autism in the Simons Simplex Collection. Journal of the American Academy of Child and Adolescent Psychiatry 53 (3), 329-340. hyperlink:

Harker, C.M., & Stone, W.L. Comparison of the Diagnostic Criteria for Autism Spectrum Disorder across DSM-5, DSM-IV-TR, and the Individuals with Disabilities Education Act (IDEA) Definition of Autism. (2014). The IRIS CENTER.

Havdahl, Bal, Huerta, Pickles, Oyen, Stoltenberg, Lord, Bishop. (2016). Multidimensional influences on autism symptom measures: Implications for use in etiological research. Journal of the American Academy of Child and Adolescent Psychiatry, 55(12). 1054-1065. Hyperlink:

Hoff, K.E, & Doepke, K.J. Review of the Social Responsiveness Scale, second edition. J. F. Carlson, K. F. Geisinger, & J. L. Jonson (Eds.), The Nineteenth Mental Measurements Yearbook. Nebraska; The Board of Regents of the University of Nebraska and the Burros Center for Testing.

Holden, K., McGregor, B., Thandi, P., Fresh, E., Sheats, K., Belton, A., Mattox, M., & Satcher, D. (2014). Toward Culturally Centered Integrative Care for Addressing Mental Health Disparities Among Ethnic Minorities. Psychological Services, 11(4), 357-368. American Psychological Association. Doi:

Kramer, J. J., & . Conoley, J. C. (Eds.), (1992). The eleventh Mental measurements yearbook. Nebraska; The Board of Regents of the University of Nebraska and the Buros Center for Testing.

Lord, C., & Jones, R.M. (2012). Re-thinking the classification of autism spectrum disorders. Journal of Child Psychology and Psychiatry, 53 (5), 490-509. doi: 10.1111/j.1469-7610.2012.02547.x

Malcolm, K. K. (2014). 1st. Review of the Childhood Autism Rating Scale, second edition. J. F. Carlson, K. F. Geisinger, & J. L. Jonson (Eds.), The Nineteenth Mental Measurements Yearbook. Nebraska; The Board of Regents of the University of Nebraska and the Buros Center for Testing.

McCellan, M. J. (2014). 2nd. Review of the Childhood Autism Rating Scale, second edition. J. F. Carlson, K. F. Geisinger, & J. L. Jonson (Eds.), The Nineteenth Mental measurements yearbook. Nebraska; The Board of Regents of the University of Nebraska and the Buros Center for Testing.

Morgan, P.L., Staff, J., Hillemeier, M.M., Farkas, G., Maczuga, S. (2013). Racial and Ethnic Disparities in ADHD Diagnosis from Kindergarten to Eighth Grade. Pediatrics; Official Journal of the American Academy of Pediatrics, 132(1). doi:

Pathak, M., Bennett, A., & Shui, A. M. (2019). Correlates of adaptive behavior profiles in a large cohort of children with autism: The autism speaks Autism Treatment Network registry data. Autism, 23(1), 87-99.

Prizant, B. M. (1992). Review of the Childhood Autism Rating Scale. J. J. Kramer & J. C. Conoley (Eds.), The eleventh Mental measurements yearbook. Nebraska; The Board of Regents of the University of Nebraska and the Buros Center for Testing.

Reiersen, A.M., Constantino, J.N., Volk, H.E., & Todd, R.D. (2007). Autistic traits in a population-based ADHD twin sample. Journal of Child Psychology and Psychiatry 48(5).464–472.  doi:10.1111/j.1469-7610.2006.01720.x

Rommelse, N., Franke, B., Geurts, H.M., Hartman, C.A., & Buitelaar, J.K. (2010). Shared heritability of attention-deficit/hyperactivity disorder and autism spectrum disorder. European Child & Adolescent Psychiatry, 19 (3), 281-295. [PubMed: 20148275]

Trevisan, D.A., Tafreshi, D., Slaney, K.L., Yager, J., & Iarocci, G. (2018). A psychometric evaluation of the multidimensional Social Competence Scale (MSCS) for young adults. Plos One, 13(11). Hyperlink:

U.S. Department of Education, Office of Civil Rights. 2013-2014 Civil Rights Data Collection; A First Look (2016).

Wijngarrden-Cremers, P., Van Eetenm E., Groen, W.B., Van Deurzen,P.A., Oosterling, I.J., Gaag, R. (2014). Gender and Age Differences in the Core Triad of Impairments in Autism Spectrum Disorders: A systematic Review and Meta-analysis. Journal of Autism and Developmental Disorders, 44(3), pp. 627-635. Hyperlink:

Xavier, J., Bursztejn, C., Stiskin, M., Canitano, R., & Cohen, D. (2015). Review: Autism spectrum disorders: An historical synthesis and a multidimensional assessment toward a tailored therapeutic program. Research in Autism Spectrum Disorders 18, 21-33. hyperlink:

Xu, G., Strathearn, L., Liu, B., & Bao, W. (2018). Prevalence of Autism Spectrum Disorder among Us Children and Adolescents, 2014-2016. JAMA 319 (1), 81-82. doi:

Yetter, G. Review of the Social Responsiveness Scale, second edition. J. F. Carlson, K. F. Geisinger, & J. L. Jonson (Eds.), The Nineteenth Mental Measurements Yearbook. Nebraska; The Board of Regents of the University of Nebraska and the Burros Center for Testing.