USING DIFFERENTIAL ITEM FUNCTIONING TO VALIDATE A JUDGEMENT-BASED ASSESSMENT OF EMERGENT LITERACY FOR STUDENTS WITH AUTISM SPECTRUM DISORDER ABSTRACT

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1 USING DIFFERENTIAL ITEM FUNCTIONING TO VALIDATE A JUDGEMENT-BASED ASSESSMENT OF EMERGENT LITERACY FOR STUDENTS WITH AUTISM SPECTRUM DISORDER Kerry Woods and Masa Pavlovic Assessment Research Centre, Melbourne Graduate School of Education ABSTRACT The co-occurrence of autism spectrum disorder with other disabilities blurs boundaries between patterns of learning related to, or impacted by, this disorder, and those related to other developmental or intellectual disabilities. This study extended previous research that suggested emergent skills among children with autism spectrum disorder could be fairly and meaningfully assessed using the same judgement-based protocols as for children with other learning disabilities. One aim of the current research was to further investigate the validity of these assessment protocols, but with a larger group of students (n = 7967, 3145 of whom had a diagnosis of autism spectrum disorder) and with emphasis on learning in the early school years. A corollary aim was to use differential item functioning analyses within a partial credit item response modelling approach to examine patterns of relative strength, challenge, and progress in literacy skills for children with autism. The outcome of the research builds knowledge of the assessment of literacy in young children with autism This is expected to help schools and teachers monitor and teach these foundational skills for children with both autism spectrum disorder and broader learning difficulties. Introduction Since its emergence as a diagnostic category to describe a symptom of severe schizophrenia (Bleuler, 1911/1950), autism spectrum disorder (ASD) has consistently been used as a term to signify one of the most puzzling forms of developmental difference in children (Evans, 2013). Descriptions of the nature of disabilities associated with ASD have changed over time, from Kanner s (1943) observations of the object-orientation, and relative lack of social interest, of children with this disorder, to Rutter s (1968) identification of three key markers, encompassing abnormality in language development, ritualistic and compulsive behaviours, and a disturbance in interpersonal interactions, to more recent propositions that children with autism lack a theory of mind, which leads to a profound inability to cope with the social environment and impaired verbal and non-verbal communication (e.g., Baron- Cohen, Leslie, & Frith, 1985). Autism is currently acknowledged in the American Psychiatric Association s (APA) (2013) Diagnostic and Statistical Manual of Mental Disorders: DSM-V as a spectrum of neurodevelopmental disorders characterised by marked deficits in social communication and interaction, and restricted or repetitive patterns of behaviour, interests, or activities, that are present from an early age, cause significant impairment in social, occupational, or other important areas of functioning, and are not better explained by a diagnosis of intellectual disability or global developmental delay. One common thread over time, however, has been an emphasis on atypical development and behavioural difference when describing children with autism. A report published by the United States Centers for Disease Control and Prevention (CDCP) (2012) noted that changes in clinical definitions of ASD, in combination with a lack of biologic markers for diagnosis and the complex nature of the disorder, have contributed to difficulties in monitoring its prevalence, assessing its implications, or establishing a clear understanding of how to best support children and their families. Further, it seems that ASD and other forms of disability may co-occur, in much the same way that a child may be both blind and have an intellectual, physical, or health impairment. While these are clearly separate forms of disability, both may need to be taken into account, independently and in terms of the ways they interact to intensify or complicate a child s AARE Conference 2016 Melbourne, Victoria Page 1 of 11

2 difficulties, when devising programs of educational assessment, support, and intervention. AARE Conference 2016 Melbourne, Victoria Page 2 of 11

3 The characteristics of ASD identified by the DSM-V do not include reference to cognitive impairment or specific learning difficulties in foundational skill development such as basic literacy or numeracy (APA, 2013). However, co-occurrence of autism with other forms of developmental or intellectual disability seems to be widely observed, and in ways that blur the boundaries between patterns of behaviour clearly related to the disorder and those that are attributable to other forms of impairment (e.g., Carnahan, Williamson, & Christman, 2011; Lanter, Watson, Erickson, & Freeman, 2012; Woods, Mahdavi, & Ryan, 2013). For example, children with autism seem to be disproportionately affected by language impairment (Kjelgaard & Tager-Flushberg, 2001). They have been described as showing strengths in processing local information, with associated implications for their capacity to search for a more general or global meaning (White, O Reilly, & Frith, 2009). Further, and in terms of their development of literacy, several studies have documented specific difficulties for these children in more advanced skills, such as reading comprehension, despite being able to demonstrate adequate decoding skills (e.g., Chiang & Lin, 2007; Nation, Clarke, Wright, & Williams, 2006). By contrast, some relative strengths in discrete skills of emergent literacy, such as letter or word identification, have also been attributed to children with autism (Lanter et al.). These observations form part of the puzzle of autism for teachers and parents: why are these children able to read many words with relative ease but without comprehending their meaning? Emergent literacy is a term used to denote a set of foundational skills (e.g., oral language proficiency and fluency, motivation to read and write, letter name identification, phonological awareness, and understanding of letter-sound correspondence and concepts about print) that are believed to precede and support the development of more advanced literacy skills such as reading fluency, reading comprehension, and written expression (National Centre for Family Literacy, 2008; Whitehurst & Lonigan, 1998). The current research aimed to build knowledge of how young children with autism develop emergent literacy skills and, in particular, how these skills can be assessed and monitored by teachers. It extended a body of research that commenced with an Australian Research Council Linkage Project conducted by the Assessment Research Centre, University of Melbourne, in partnership with the Victorian Department of Education and Training s (DET) Student Wellbeing Division and the Centre for Advanced Assessment and Therapy Services. That research profiled the development of literacy, language, and communication skills (Woods, 2010; Woods & Griffin, 2013) among students with a range of intellectual and developmental disabilities. It developed assessment protocols based on teacher judgment and observation of student performance in everyday classroom interactions, and calibrated progressions of learning for students who were aged from four to eighteen years, enrolled in either mainstream or special education schools, and diverse in terms of the form and severity of their disabilities. Of particular relevance to the current study, it designed and validated an observation framework to describe the way that students learn to make and interpret meaning from symbols and printed text (Woods). In other words, the assessment related to both the way that students learned to recognise symbols, such as letters and words, and the way they interpreted them as the foundation of reading comprehension. In 2011, the output of that earlier study was released to all Victorian schools as the Abilities Based Learning and Education Support (ABLES) program (Victorian DET, 2016). It formed part of the Victorian government s policy for provision of equitable educational opportunities for all students, including those who have disabilities and additional learning needs (Victorian DET, 2015). The study described in this paper, therefore, was designed to build on earlier research to describe and monitor patterns of strength, challenge, and expected progress in emergent literacy particular to students who have ASD. As noted earlier, children with autism are often described predominantly in terms of their differences and disabilities in comparison with other students, including both those described as typical learners and those who have learning difficulties. However, a component of the research described by Woods (2010) and conducted with a sample of 547 students, 169 of whom had a diagnosis of ASD, suggested that emergent literacy may unfold in much the same way for students with autism as for other students with additional learning needs, although with some evidence of disability-specific patterns of strengths and challenges. The current study extended that earlier research, using a large database of Victorian students with additional learning needs derived from use AARE Conference 2016 Melbourne, Victoria Page 3 of 11

4 of the ABLES (Victorian DET, 2016) materials in schools. To date, and by comparison, few studies have examined the development of emergent literacy skills in students with autism, and these have mainly been in-depth case studies of development conducted with a relatively small number of children (e.g., Koppenhaver & Erickson, 2003; Lanter et al., 2012; Wolfberg, 1999). The analytical framework for the study drew on the use of item response modelling techniques (Rasch, 1980) to identify patterns of relative strength and challenge in the development of emergent literacy skills for students whose literacy learning may be difficult for teachers to recognise and interpret. This supported the aims of the research, which were to investigate the validity of assessment protocols designed for children with additional learning needs when used by teachers to assess emergent literacy skills for children with autism; and use differential item functioning (DIF) analyses within a partial credit item response modelling approach (Masters, 1982) to examine patterns of relative strength, challenge, and expected progress in emergent literacy skills particular to this group of children. Method Participants Participants in this study were 7967 students with additional learning needs, enrolled in 573 Victorian primary and special schools, and assessed over a time period starting in 2011 and ending in Students were assessed in March each year using an observational instrument developed to measure emergent literacy skills and understanding (Woods & Griffin, 2013). At the time of the assessment, all of the participants were aged between 5 and 8 years. The number of students by school type, gender and whether they were diagnosed with ASD or had additional learning needs but not autism (Not ASD) is presented in Table 1. Table 1 Sample demographics School Type ASD Not ASD Male Female Male Female Primary Special Total Context and materials The study was conducted in 573 Victorian schools that used an observation instrument (Woods & Griffin, 2013) to assess the emergent literacy skills of their students as part of the ABLES (Victorian DET, 2016) assessment and reporting materials. These materials took the form of a criterionreferenced judgement framework presented as a set of multiple choice questions, and that drew on teachers observations of student performance rather than asking students to participate directly in testing. The measure of emergent literacy comprised 20 questions that were delivered online and that took teachers, on average, between five and ten minutes to complete. Teachers responses to the questions were scored using a partial credit approach (Masters, 1982). An example of one of the questions is shown in Figure 1. AARE Conference 2016 Melbourne, Victoria Page 4 of 11

5 Analysis Item response modelling Figure 1. An example question from the online observation survey The partial credit model (PCM) is an extension of the Rasch (1960) simple logistic model frequently used to create a measure of students achievement (or latent trait) when items in the assessment (instrument) have more than one scoring category. PCM is derived from the notion that each two adjacent categories in a polytomously scored item can be treated as dichotomous categories, and therefore the probability of a student (conditioned on his or her ability) being in category k rather than category k-1 can be obtained by applying the dichotomous Rasch model to the adjacent pairs of scores. From that idea, Masters (1982) derived a model that gives the probability that student n, with the ability θ n, will reach category k on item i with total of m i scoring categories. This is given by Eq. 1. P( X ni = k) = m i exp( k exp( t= 0 h h= 0 t= 0 ( θ δ ) n ( θ δ ) n it it Eq.1 0 where exp ( θ δ ) = 0. t= 0 n it and δ it, t = 1,2,...mi are the points at which probabilities for adjacent categories in the item i are equally likely. Since PCM is a Rasch-like model, the total score is sufficient for estimating student ability which allows for separation of item and student estimates. Thus, δ it can be estimated independently of person parameters in the model. This separability of item and person parameters yields to the property of parameter invariance. AARE Conference 2016 Melbourne, Victoria Page 5 of 11

6 Item parameter invariance Item parameter invariance is a property of Rasch-like models, in which parameter estimates remain invariant irrespective of the sample from the population used to obtain the item estimates. However, it is important to note that a property of parameter invariance is theoretical idealisation and, in practice, it holds only approximately. In order to make valid and generalizable inferences from the assessment, the degree to which parameter invariance in practical implementations of Rasch models deviates from the theoretical idealisation must be assessed. The invariance of item parameters can be assessed by a comparison of the model item parameter obtained for different subgroups in the population for which an assessment is intended. This information can be captured graphically and is typically presented by bi-variate plots (Hambleton et al., 1991). When item parameter estimates are invariant, the bi-variate plot should be linear with a slope of one, and the amount of scatter should reflect errors that are due to standard error only. Differential item functioning Differential item functioning (DIF) is a common method used to examine measurement invariance for individual items. That is, DIF is a statistical term that refers to differences in group performance on an item that cannot be directly explained by the trait measured by items. If some items are more difficult or less difficult, relative to the other items for a specific group in the sample, then the interpretation of a measured trait can be affected. In PCM, the interpretation of DIF depends on the parametrization used (Penfield, Myers and Wolfe, 2008). Under the parametrization used in Eq. 1, DIF is interpreted as between group differences in the difficulty of advancing from category k-1 to k and is also called differential step functioning (DSF). Another interpretation of DIF is associated with parametrization in which δ it is decomposed into average item difficulty (δ.) and step parameter τ t, where τ t is a step parameter that shows the distance of a partial credit score category from the average item difficulty (Wu & Adams, 2007). As opposed to earlier step level interpretations of DIF, DIF associated with δ. is interpreted at item level. This distinction in interpretation of DIF can aid better understanding of the source of invariance in an affected item. Mantel Haenszel statistics Mantel Haenszel (MH) statistics were introduced by Mantel and Haenszel (1959), as a measure of association developed to test the null hypothesis of independence between two dichotomous variables. Holland and Thayer (1988) extended the application of Mantel Haenszel statistics to DIF analysis of both dichotomous and polytomous items. The MH method provides a χ 2 statistic as well as a measure of effect size Δ. It groups data across different levels of the matching variable and calculates the odds ratio for each level. While any relevant criterion can be used to match students on ability, in most applications of the MH method matching is based on raw scores. In the Conquest IRT software (Adams, Wu & Wilson, 2012), however, population matching is based on population ability estimates (Australian Council for Education Research [ACER], 2012). The groups are categorized as reference and focal group, where performance of a reference group provides the standard performance on the item. Furthermore, in accordance with the original parametrization of PCM, MH statistics are reported between adjacent score categories. Thus, Conquest provides the Educational Testing Service (ETS) classification of DIF. According to this classification, an item belongs to category A DIF (negligible) if χ 2 is not significant at the 5% level or Δ is smaller than 1 in absolute value. In order for an item to qualify as category C, Δ 1 must be significantly greater than 1 in absolute value. An item that does not meet either criteria is considered to be category B. 1 Holland and Thayer (1988) showed that 1 Δ unit corresponds to logits AARE Conference 2016 Melbourne, Victoria Page 6 of 11

7 Results and discussion Item fit and reliability Item parameter estimates for the sub-sample of students with ASD were obtained using PCM and Conquest software (Adams, Wu & Wilson, 2012). The overall scale was evaluated considering fit to the model using the convention of 0.77 to 1.3 (Adams & Khoo, 1996). Under this convention, all items, with the exception of items 12 and 20, showed evidence of good fit. These two items showed underfit to the model and a degree of randomness in teacher response patterns. Item 12 was designed to measure student s expressed interest in drawing and writing, using conventional materials or computers, while item 20 was designed to measure student ability to use a computer keyboard/mouse. While item 12 underfit was not unexpected, as it could be challenging for teachers to interpret attitudes to literacy activities for students diagnosed with ASD, it was also worth noting that both items referred to the use of technology in literacy activities. There is a possibility that, in a young cohort of students, this emphasis introduced a degree of randomness to teacher responses not observed when items were analysed for a cohort of students across all school year levels (Woods, 2010). The overall item difficulties, associated standard errors and weighted (infit) mean square (MNSQ) statistics are shown in Table 2. Table 2 Infit mean square and average item difficulty Item Skill Average Delta SE Infit MNSQ item:1 Responding to photographs and pictures item:2 Choosing reading material item:3 Enjoying and relating to reading item:4 Recognising that print has consistent meaning item:5 Identifying letters and numbers item:6 Matching sounds to letters item:7 Blending sounds in words item:8 Matching words that rhyme item:9 Predicting the meaning of words item:10 Recognising form of words/word parts item:11 Re-telling stories item:12 Showing interest in drawing or writing item:13 Showing pride in drawing or writing item:14 Matching symbols and meaning item:15 Ordering symbols to express ideas item:16 Producing letter forms item:17 Matching letters to sounds item:18 Knowing how to present writing item:19 Using pens/pencils for writing item:20 Using computer keyboard/mouse The item separation index was high at and, coupled with a skills analysis, was taken as evidence of content and construct validity. In a similar way, the extent to which students could be separated along the scale based on their ability estimates was taken as evidence of criterion validity. This is given by the person separation index, which was The alpha reliability was 0.97, which indicated that the instrument as a whole displayed strong internal consistency. AARE Conference 2016 Melbourne, Victoria Page 7 of 11

8 Parameter invariance Parameter invariance is typically examined by using bi-variate plots of item parameter estimates obtained from calibration of responses from different subgroups in the population. The goal of the research reported in this paper was to examine the suitability of a learning progression, developed to describe emergent literacy skills and understanding of students with additional needs (Woods, 2010), for use with children diagnosed with ASD. The hierarchy of emergent skills described by this learning progression was obtained by calibrating the item responses of a student population who were diverse in terms of the type and severity of their disability. Moreover, in many cases those students had more than one type of additional learning need. In order to examine whether the developed learning progression could be used by teachers to support development of emergent literacy skills in children diagnosed with ASD, then, item parameter estimates obtained from a calibration of responses for students diagnosed with ASD were compared to the item parameter estimates underpinning the developed learning progression. Figure 2 shows bi-variate plots for average item difficulties (δ.). Figure 2. Comparison of average item difficulty estimates obtained by calibrating responses for students with ASD to the original calibration Figure 2 displays the relationship between item level difficulties obtained by calibrating responses for students with ASD to the original calibration. It can be seen that the slope of the line of best fit is very close to 1 and the correlation between the two sets of parameters is high at This demonstrates a very high level of similarity, rather than difference, in the hierarchy of emergent skills for students with ASD when compared to the broader group of students. Of the twenty items in the measure, all but two items were close to the line of best fit; however, given the large sample size even small differences in parameter estimates can be found to be significant. This may lead to the inflation of Type I error. As examination of item expected score curves revealed that potential differences in group performance on suspected items were uniformly spread across the ability range, possible differences in item performance were also evaluated using effect size. Items flagged in Figure 2 (i.e., shown as a red square rather than a blue dot) were marked for further investigation using a Type I error rate of 0.05 and effect size (standardized difference in δ. parameters) greater than 0.4. The choice of the 0.4 criterion was to make the results comparable to ETS classification criteria; however, it is more stringent than the frequently used cut offs of 0.5 or 0.6. Items 5 and 13, marked in Figure 2, were found to fall outside the 95% confidence interval with the absolute value of the effect size of and respectively. From Figure 2, it can be seen that item 5 (i.e., the skill of identifying letters and numbers) was found to be somewhat easier for students AARE Conference 2016 Melbourne, Victoria Page 8 of 11

9 with ASD. This is consistent with studies that have demonstrated strengths in letter recognition and decoding skills for students with ASD (Chiang & Lin, 2007; Nation, Clarke, Wright, & Williams, 2006). On the other hand, item 13, which was designed to capture students attitudes to literacy, such as showing interest or pride in their drawing or writing, was found to be more difficult for students with ASD. In order to better understand these observed differences, flagged items were further investigated for DSF using the Mantel-Haenszel method. Differential step functioning (DSF) Estimates of DSF were obtained using the Mantel Haenszel method implemented in the Conquest software (Adams, Wu & Wilson, 2012). The statistics were calculated using students with additional needs but not ASD as the reference and students with ASD as the focal group. Each of these items was made up of four response choices for teachers. In both items, a score of 0 was given if teachers responded that the assessed student could not demonstrate any of the skills described in the response options. Item 5, designed to identify students emerging capacity to identify letters and numbers, asked teachers to select whether an individual student could sort and match pictures and shapes (scored as 1), or sort and match letters and numbers (scored as 2), or identify and match upper and lower case examples of letters and the same letters in different fonts (scored as 3). Item 13, which was designed to identify students attitudes towards participation in literacy activities, asked teachers to identify whether their student typically showed others his or her drawing and writing when asked (scored as 1), or asked for responses to his or her drawing and writing (scored as 2), or sought ways to improve his or her drawing and writing (scored as 3). The result of the analysis is shown in Table 3. Table 3 Output of step difficulty analysis using Mantel Haenszel statistics for items 5 and 13 Item 5- Identify number and letters Step Δ CHISQ df p Item 13- Show pride in drawing or writing ETS class Δ CHISQ df p ETS class A A between 0 and 1 between 1 and _BIG_ A _BIG_ A between 2 and _BIG_ A _BIG_ A As displayed in Table 3, negative values of Δ related to steps that were more difficult for students with ASD. It can be seen that, for item 5, the transition between 0 and 1 was easier for low functioning students with ASD to demonstrate than low functioning students who did not have ASD. However, this effect was small and not statistically significant. A somewhat larger and significant effect was found for steps 2 and 3. For item 13, all steps were found to be more difficult for students with ASD. As for item 5, the effects were small and significant only for steps 2 and 3. However, the impact of observed differences of performance on both items and the assessment as a whole was found to be negligible (i.e., ETS classification was A). Conclusion The primary aim of the study reported in this paper was to investigate the validity of assessment protocols designed for children with a broad range of additional learning needs (Woods, 2010; Woods & Griffin 2013) when used by teachers to assess emergent literacy skills for children with autism. The learning of these children has consistently been described with emphasis placed on their disabilities, atypical patterns of development, and behavioural differences (Evans, 2013). By contrast, few studies have emphasised the developmental similarities between children with autism and other children. However, this paper described analysis of data drawn from a large sample of students that demonstrated teachers responses for students with autism showed good fit to the PCM model and AARE Conference 2016 Melbourne, Victoria Page 9 of 11

10 strong evidence of construct validity. This, in conjunction with bi variate plots and DIF analysis, provided sound evidence that the assessment materials and learning progression could indeed be used equally well by teachers to monitor and support development of emergent literacy in students diagnosed with autism This is useful information for educators, who should be encouraged to look past the behavioural differences of students with autism to recognise the similarities between these and other students in the ways that core skills, such as emergent literacy, can be taught and learned. It echoes Vygotsky s (1929/1993) observation that all developmental uniqueness tends to approximate determined, normal, social types (p. 48) and that the responsibility of schools and teachers is to play a decisive role in this approximation. References Adams, R., & Khoo, S.T. (1996). Quest. Melbourne, Australia: Australian Council for Educational Research. American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders: DSM-V. 5 th ed. Arlington, Va: APA. Australian Council for Education Research (ACER) (2012). Conquest notes: Using Mantel-Haenszel statistics. Retrieved from HaenszelStatistics.pdf. Baron-Cohen, S., Leslie, A.M., & Frith, U. (1985). Does the autistic child have a theory of mind? Cognition, 21, Bleuler, E. (1950). Dementia praecox or the group of schizophrenias. New York: International Universities. (Original work published in 1911). Carnahan, C.R., Williamson, P.S., & Christman, J. (2011). Linking cognition and literacy in students with autism Teaching Exceptional Children, 43(6), Centers for Disease Control and Prevention (2012). Prevalence of autism spectrum disorders Autism and developmental disabilities monitoring network, 14 sites, United States, MMWR 2012: 61 (No. 3), Chiang, H., & Lin, Y. (2007). Reading comprehension instruction for students with autism spectrum disorders. Focus on Autism and Other Developmental Disabilities, 22(4), Evans, B. (2013). How autism became autism: The radical transformation of a central concept of child development in Britain. History of the Human Sciences, 26(3), Hambelton, R., et al. (1991). Fundamentals of Item Response Theory. United States of America, SAGE Publications. Holland, P. W., & Thayer, D. T. (1988). Differential item functioning and the Mantel-Haenszel procedure. In H. Wainer & H. I. Braun (Eds.), Test validity (pp ). Hillsdale, NJ: Lawrence Erlbaum Associates. Kanner, L. (1943). Autistic differences of affective contact. Nervous Child, 2, Kjelgaard, M., & Tager-Flushberg, H. (2001). An investigation of language impairment in autism: Implications for genetic subgroups. Language & Cognitive Processes, 16, Koppenhaver, D. & Erickson, K. (2003). Natural emergent literacy supports for students with autism and severe communication impairments. Topics in Language Disorders, 23, Lanter, E., Watson, L.R., Erickson, K.A., & Freeman, D. (2012). Emergent literacy in children with autism: An exploration of developmental and contextual dynamic processes. Language, Speech, and Hearing Services in Schools, 43, Mantel, N. & Haenszel, W.M. (1959). Statistical aspects of the analysis data from retrospective studies of disease. Journal of the National Cancer Institute, 22, Masters, G. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, Nation, K., Clarke, P., Wright, B., & Williams, C. (2006). Patterns of reading ability in children with autism Journal of Autism and Developmental Disorders, 36, National Centre for Family Literacy (2008). Developing early literacy: Report of the National Early Literacy Panel. A scientific synthesis of early literacy development and implications for AARE Conference 2016 Melbourne, Victoria Page 10 of 11

11 intervention. Retrieved from Washington DC: National Institute for Literacy: Penfield, R. D., et al. (2008). Methods for assessing item, step, and threshold invariance in polytomous items following the Partial Credit Model. Educational and Psychological Measurement 68(5), Rasch, G. (1980). Probabilistic models for some intelligence and attainment tests. Chicago: The University of Chicago Press. (Original work published in 1960, Copenhagen, Danish Institute for Educational Research). Rutter, M. (1968). Concepts of autism. Journal of Child Psychology and Psychiatry, 9, Victorian Department of Education and Training (DET) (2015). Students with disabilities. Retrieved from spx. Victorian Department of Education and Training (DET) (2016). ABLES. Retrieved from Vygotsky, L.S. (1993). The collected works of L.S. Vygotsky, Volume 2: The fundamentals of defectology (abnormal psychology and learning disabilities) (R.W. Reiber & A.S. Carton, Trans.). New York: Plenum Press. (Original work published in 1929) White, S., O Reilly, H., & Frith, U. (2009). Big heads, small details and autism. Neuropsychologia, 47, Whitehurst, G., & Lonigan, C. (1998). Child development and early literacy. Childhood Development, 69, Wing, L., Gould, J., & Gillberg, C. (2011). Autism spectrum disorders in the DSM-V: Better or worse than the DSM-IV? Research in Developmental Disabilities, 32, Wolfberg, P. (1999). Play and imagination in children with autism. New York, NY: Teachers College Press. Woods, A.G., Mahdavi, E., & Ryan, J.P., (2013). Treating clients with Asperger s syndrome and autism. Child and Adolescent Psychiatry and Mental Health, 7, 32. Retrieved from Woods, K. (2010). The design and validation of measures of communication and literacy to support the instruction of students with learning disabilities. Unpublished doctoral thesis. The University of Melbourne, Australia. Woods, K., & Griffin, P. (2013). Judgment-based performance measures of literacy for students with additional needs: Seeing students through the eyes of experienced special education teachers. Assessment in Education: Principles, Policy & Practice, 20(3), Wu, M., & Adams, R. (2007). Applying the Rasch model to psycho-social measurement: A practical approach. Melbourne, Australia: Educational Measurement Solutions. AARE Conference 2016 Melbourne, Victoria Page 11 of 11