Author: Crepeau-Hobson, Franci; Vujeva, Hana
Date published: November 1, 2012
Journal code: NASP
The assessment of cognitive ability in students with the most severe disabilities presents a challenge to the clinicians who are charged with this task. This article is the second of a two-part series that summarizes what is currently known about effective assessment of the cognitive ability of students with significant impairments in order to improve service delivery to them. Part 1 presented background information and addressed assessment of cognitive ability in individuals with visual andhearing impairments. Part 2 summarizes the professional literature examining a variety of tests of cognitive ability that can be used with students with language impairments, motor impairments, and significant intellectual disabilities.
Language impairments can involve difficulty with grammar (syntax), vocabulary (semantics), the rules and system for speech sound production (phonology), units of word meaning (morphology), and the use of language, particularly in social contexts (pragmatics). Expressive language delays may exist without receptive language delay, but they can also co-occur in mixed expressive/receptive language disorders (American Speech-Language-Hearing Association, 1993).
As a consequence of the heavy verbal loading of most standardized cognitive assessments, children with severe language impairments are unable to be adequately assessed utilizing traditional cognitive assessment techniques. Indeed, research utilizing factor analyses has demonstrated that nearly 50% of the variance in children's performance on cognitive tests is accounted for by language abilities (Losch & Dammann, 2004), leaving little roomfor accurately assessing the cognitive ability of children with significant language impairments utilizing standard cognitive batteries. This is of particular concern for children with autism, especially those who are low functioning, as significant language deficits are inherent to the disorder. Research indicates that scores obtained from traditional measures of intelligence have underestimated the intellectual ability of children with autism spectrum disorders (see Edelson, 2006 for a review), perhaps because language deficits may be independent of cognitive ability (Dodd & Thompson, 2001; Lord & Paul, 1997). Thus, the use of measures of intelligence that take into account the interference of autism, particularly the significant language impairments, is of critical importance in identifying core cognitive impairments and for educational and treatment planning for this population.
For children with a lack of intelligible expressive communication because of profound articulation difficulties, the Leiter International Performance Scale-Revised (Leiter-R; Roid & Miller, 1997) or the Comprehensive Test of Nonverbal IntelligenceSecond Edition (CTONI-2; Hammill, Pearson, & Wiederholt, 2009) may be used (Quinn, 2010; Sattler, 2008). The Wechsler Nonverbal Scale of Ability (WNS; Wechsler & Naglieri, 2006) may also be considered (Sattler, 2008), although this assessment has less research supporting its use in populations with disabilities and may, in fact, not be a valid measure for children with any disability.
For both receptive and/or expressive language difficulties, the Universal Nonverbal Intelligence Test (UNIT; Bracken 8c McCallum, 1998) is an optimal choice, (Farrell 8c Phelps, 2000; Sattler, 2008). However, it is important to note that the UNIT has dated and consequently questionable norms. While matrices-based tests such as Raven's Progressive Matrices (Raven, Raven, 8c Court, 1998) and the Test of Nonverbal Intelligence (TONI-4; Brown, Sherbenou, 8c Johnsen, 2010) are often used with this population, these and other figural-reasoning tests should not be used in place of comprehensive measures of cognitive ability because they measure intelligence based on figure reasoning only. They should only be used as a screening measure of nonverbal ability (Sattler 8c Hoge, 2006).
Research utilizing factor analyses has estimated that nearly 20% of the variance in performance on cognitive assessment batteries is explained by children's motor abilities (Losch 8c Dammann, 2004). Although motor skill and sensory function are not essential qualities in determining overall cognitive functioning, they are certainly critical to performance on assessment protocols that determine overall cognitive ability (Decker 8c Davis, 2010) because most of these tests include some manipulation of objects such as blocks and/or the use of a pencil. Children with motoric impairments are at a particular disadvantage when tasks requiring motor skill are timed (Sattler, 2001). Therefore, cognitive batteries must be carefully selected in order to obtain an accurate estimate of cognitive ability, and tasks heavily reliant upon motor skill or speed should be avoided. Unfortunately, there are few accurate and fair tests for children with motor impairments (Ruiter, Nakken, van der Meulen, 8c Lunenberg, 2010).
For children with severe motor impairments that restrict ability to engage with manipulatives or use a writing instrument, the CTONI-2 is considered the best option because it was designed to limit motor involvement and subjects' responses require pointing only (Aylward, 1998; Hammill 8c Pearson, 2009). The UNIT and Leiter-R are valid considerations for children with more moderate motor impairments as they are not timed and children with motor impairments are not penalized when completing tasks tapping motor skill (McCallum, 2003; Roid, Nellis, 8cMcClellan, 2003). However, neither psychometric properties nor norms for this disability group are available for these measures.
Another choice when assessing children with motor impairments is to make adaptations to commonly used instruments in terms of item instructions, response format, etc. to more accurately portray the child's performance. Some research suggests that decreasing the motor demands of some standardized tests may not impact the validity of the test results (Ruiter et al, 2010). However, it is important to keep in mind that methodological research on test adaptations for physical disabilities is sparse and, as a result, neither normative data nor recommendations for standard modifications are available (Hill-Bnggs, Dial, Morere, 8c Joyce, 2007). "While obtaining only informative data under these conditions, making adaptations can still S*ve ^e c^n^an an idea of the child's potential cognitive abüity.
LANGUAGE AND MOTOR IMPAIRMENTS
As discussed above, both language and motor skills are significant factors in Perfo(TM)ance °n assessments of cognitive ability. When both are impaired, it is nearly impossible to administer most standardized assessments of cognitive ability. Cerebral palsy (CP), the leading cause of disability affecting mot or function and development, refers to a range of motor impairment syndromes secondary to brain lesions or anomalies (Mutch, Alberman, Hagberg, Kodama, 8c Perat, 1992) and typically affects both language and motor abilities, including, in most cases, apraxia. Test results must be interpretedin the context of the motor, speech, visual, and auditory difficulties that maybe present in children with CP (Fennell 8c Dikel, 2001). Consequently, only school psychologists who are experienced in the appropriate approaches should be evaluating children in this population. This is particularly important as the sparse literature on the assessment of children with CP and the limited choices for assessment techniques often leads to underestimates of the cognitive abilities of children with CP (Warschausky, 2006). Children with CP also have high rates of diagnoses of intellectual disabilities (ID) and learning disabilities (LD), yet the motor and communication barriers very likely invalidate results on traditional cognitive assessment instruments. As a consequence, there are many children who maybe mistakenly diagnosed with an intellectual disability within this population. This variability may also be related, in part, to delayed or deficient language skills because of the incoordination of the muscles used in speech production and significant gross motor handicaps seen in some types of CP (Fennell 8c Dikel, 2001). Targeted educational, psychosocial, and vocational interventions depend upon accurate assessment of cognitive strengths and weaknesses (Warschausky, Van Tubbergen, 8c Donders 2008), so differentiating these from motor and language deficits is very important.
At present, there are few measures to accurately assess children with CP. The LeiterR and the CTONI-2 maybe the most appropriate choices while UNIT and the WNS may not be suitable because of the degree of motoric skill required, even if not timed.
SIGNIFICANT INTELLECTUAL DISABILITY
Although many standardized cognitive assessments can be used to assess children with ID, most are not appropriate for truly gauging the cognitive strengths and weaknesses of most children with ID who are in the mild range (55-69), and many are not capable of assessing children with more than moderate ID (IQs of approximately 40-55). For the most part, assessments are lacking for this population because they do not include a sufficient number of children with ID in their norming processes and are likewise highly susceptible to floor effects (Edgin et al., 2010). In addition, these assessments may lack factorial validity and measurement invariance when used with this population (MacLean, McKenzie, Kidd, Murray, 8c Schwannauer, 2011). These limitations can result in inaccurate representations of cognitive ability in these children. However, because an appropriate assessment of intellectual ability of these students is critical for making placement and treatment decisions, the evaluation process must be approached very thoughtfully.
Two measures have been recommended for the assessment of children with ID - the Stanford-Binet Intelligence Scales-Fifth Edition (SB-5; Roid, 2003a) and the Differential Ability Scales, Second Edition (DAS-II; Elliott, 2007) - because of improved sampling of children with ID in their norming populations, as well as their lower limits of measurement (Dulcan, 2010).
The SB-5 is unique in that it allows for the calculation of IQ scores as low as 10 through an "Extended IQ" score (EXIQ; Roid 8c Barram, 2004). These scores are derived from a transformation of the child's total raw score on the test, rather than the use of traditional standard scores, and allowfor the estimation of a Full Scale IQ score for individuals whose cognitive functioning is extremely low. The ability to assess children who have IQ scores below 40 may be important in the context of educational planning because individuals with more severe ID are more likely to have concomitant neuromuscular and visual conditions (American Psychiatric Association, 2000) . Tables for obtaining the EXIQ are provided in the SB-5 Interpretive Manual (Roid, 2003b).
The DAS-II is another good choice for use with children with ID, primarily because the norming sample was more diverse in terms of ability and thus the test is able to formally assess IQs down to 30. Furthermore, the structure of the DAS-II incorporates a great deal of flexibility by allowing the Upper Early Years battery (geared toward preschool-age children) to be extended to lower functioning children through 9 years old. Lastly, a less well known feature of the DAS-II is the availability of norms for children older than 9 years utilizing the Upper Early Years battery. These norms are available via request from DAS-II customer service representatives (http://www .pearsonassessments.com/pai/ca/research/resources/faqs/DAS-II_FAQs).
Additional measures to consider with the low-functioning ID population are the CTONI-2 and the Bayley Scales of Infant and Toddler Development-Third Edition (Bayley-III; Bayley, 2006). The CTONI-2 is useful with children with ID who may also have severe verbal or motor impairments, as only pointing is required for responses and instructions can be given in either pantomime or verbally. Furthermore, the test has been normed on and information is available on the performance of children with significant intellectual disabilities (Hammill 8c Pearson, 2009). The Bayley-III can be used to approximate the intellectual level in infants and young children within the profound range of intellectual disability (Mash 8c Barkley, 2007), but is best viewed as a measure of developmental level rather than intelligence (Sattler 8c Hoge, 2006).
For clinicians who are looking for a quick screening instrument, the Rapid Assessment for Developmental Disabilities (RADD; Walsh et al., 2007) may be considered. The RADD, comprising low-difficulty items from published tests, can be administered in 10-15 minutes, has adequate reliability, and according to the test authors, the RADD total score and individual subtests can differentiate between various levels of cognitive impairment (Walsh et al., 2007).
One final and rather new approach to the assessment of children with ID comes in the form of utilizing standard cognitive assessments, such as the Wechsler Intelligence Scale for Children, 4th Edition (WISC-IV; Wechsler, 2003), and then transforming the raw data with z-score normalization, rather than using the standard scores provided with the assessment. Thus far, this approach has been utilized primarily in research applications, and that has demonstrated that z-score transformations are able to eliminate the significant floor effects and data skew that come with utilizing standard scores in children with ID (Hessl et al., 2009). This approach has yet to be adapted or formalized for use in a strictly clinical setting, but it is an approach that has promise.
The task of assessing the cognitive ability of students with severe and low-incidence disabilities is one that must be approached with care. With the exception of deaf/hard of hearing, research in the area of cognitive ability assessment of children with low incidence disabilities has been nascent at best, providing practitioners with little in the way of guidance in this area.
The administration of most comprehensive cognitive test batteries with these populations cannot be accomplished without modifying standardized test administration procedures; however, this practice can result in scores of questionable validity that may not be useful in educational programming. Unfortunately, little research has been conducted in this area, so the extent of the effect of these adaptations on psychometric integrity is unclear. Furthermore, the utilization of tests that assess a limited range of cognitive abilities may not provide a comprehensive picture of the child's functioning. Practitioners charged with assessment of students with low-incidence disabilities must be aware of the limitations of various cognitive assessment tools when working with these children.
Except for some cognitive ability tests designed specifically for blind and visually impaired individuals (i.e., the Cognitive Test for the Blind, [Dial et al., 1990] and the Intelligence Test for Visually Impaired Children [Dekker, 1989; 1993]), few measures have been developed expressly for the various disability groups described in this paper. Furthermore, the inclusion of specific disability groups in test norms is quite limited. Although there is no agreement as to whether special norms should be preferred over general norms when assessing individuals with low incidence disabilities (Braden, 1992), having both available to practitioners could be of help in gauging the accuracy of their results.
Although the focus of this paper is on formal cognitive assessment in children with severe impairments, it should be noted that the assessments must be interpreted in concert with a broad body of evidence, including observational measures, interview data, and educational and developmental history, in order to best understand a child's level of functioning and in order to draw appropriate conclusions (Miller, 2007; Powell-Smith, Stoner, Bilter, 8c Sansosti, 2008). Furthermore, when there is no need for formal cognitive assessment, a number of authors emphasize other approaches such as ecological inventories, adaptive and life skills assessment, functional behavioral assessment, and community-based assessment techniques (Gresham, Watson, 8c Skinner, 2001; Shriver, Allen, 8c Mathews, 1999; Spears, Tollefson, 8c Simpson, 2001). These assessment methods may be preferable largely because the type of data collected is directly applicable to a student's current functioning and future academic and treatment planning, including the identification of the least restrictive environment for individual students and the implementation of positive behavioral supports. Formal cognitive assessment, when deemed appropriate, can still play a very important part of the assessment process in children with intensive needs, and understanding promising practices is key to making this formal assessment as valid and as useful as possible.
American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders (4th ed., text rev.). Washington, DC: Author.
American Speech-Language-Hearing Association. (1993). Definitions of communication disorders and variations. Retrieved from http://www.asha x>rg/docs/pdf/RP1 993-00208.pdf
Aylward, G. (1998). Review of the Comprehensive Test of Nonverbal Intelligence. In J. C. Impara & ?. Plake (Eds.), The thirteenth mental measurement yearbook (pp. 310-312). Lincoln, NE: Büros Institute of Mental Measurements, University of Nebraska Press.
Bayley, N. (2006). Bayley Scales of Infant and Toddler Development, Third Edition. San Antonio, TX: Pearson.
Bracken, B. A., & McCallum, R. S. (1998). Universal Nonverbal Intelligence Test. Itasca, II: Riverside.
Braden, J. P. (1992). Intellectual assessment of deaf and hard-of-hearing people: A quantitative and qualitative research synthesis. School Psychology Review, 27(1), 82-95.
Brown, L, Sherbenou, R. J., & Johnsen, S. K. (2010). Test of Nonverbal Intelligence, Fourth Edition. San Antonio, TX: The Psychological Corporation.
Decker, S., & Davis, A. (2010). Assessing and intervening with children with sensory-motor impairment. In D. Miller (Ed.) Best practices in school neuropsychology: Guidelines for effective practice, assessment, and evidence-based intervention (pp. 673-690). Hoboken, NJ: Wiley.
Dekker, R. (1989). Cognitive development of visually handicapped children. In R. Dekker, P. J. D. Drenth, & J. N. Zall (Eds.), Intelligence test for visually impaired children aged 6 to 15 (pp. 1-21). The Netherlands: Bartimeus Ziest.
Dekker, R. (1993). Visually impaired children and haptic intelligence test scores: Intelligence Test for Visually Impaired Children (ITVIC). Developmental Medicine & Child Neurology, 35, 478-489.
Dial, J., Mezger, C, Gray, S., Massey, T, Chan, F., & Hull, J. (1990). Manual: Comprehensive Vocational Evaluation System. Dallas, TX: McCarron-Dial Systems.
Dodd, B., & Thompson, L. (2001). Speech disorder in Down's syndrome. Journal of Intellectual Disability Research, 45, 308-316.
Dulcan, M. (2010). Dulcan's textbook of child and adolescent psychiatry. Arlington, VA: American Psychiatric Publishing.
Edelson, M. G. (2006). Are the majority of children with autism mentally retarded? A systematic evaluation of the data. Focus on Autism & Other Developmental Disabilities, V. 21(2), 66-83.
Edgin, J., Mason, G., Allman, M., Capone, G., DeLeon, I., Maslen, C, .... Nadel, L. (2010). Development and validation of the Arizona Cognitive Test Battery for Down syndrome. Journal of Neurodevelopmental Disorders, 2, 149-164 doi:10.1007/211689-010-9054-3
Elliott, C. D (2007). Differential Ability Scales, 2nd edition. San Antonio, TX: The Psychological Corporation.
Farrell, M., & Phelps, L. (2000). A comparison of the Leiter-R and the Universal Nonverbal Intelligence Test (UNIT) with children classified as language impaired. Journal of Psychoeducational Assessment, 18, 268-274. doi:10.1177/073428290001800306
Fennell, E. B., & Dikel, T. N. (2001). Cognitive and neuropsychological functioning in children with cerebral palsy. Journal of Child Neurology, 76(1), 58-63. doi:10.1177/088307380101600110
Gresham, R., Watson, S. T, & Skinner, C. H. (2001). Functional behavioral assessment: Principles, procedures, and future directions. School Psychology Review, 30, 156-172.
Hammill, D., & Pearson, N. (2009). Comprehensive Test of Nonverbal Intelligence, Second Edition. In J. Naglieri & S. Goldstein. (Eds), Practitioner's guide to assessing intelligence and achievement. Hoboken, NJ: Wiley.
Hammill, D., Pearson, N., & Wiederholt, J. (2009). Comprehensive Test of Nonverbal Intelligence (2nd ed.). Austin, TX: Pro-Ed.
Hessl, D., Nguyen, D., Green, C, Chavez, ?., Tassone, F., Hagerman, R., ... Hall, S. (2009). A solution to limitations of cognitive testing in children with intellectual disabilities: The case of fragile X syndrome. Journal of Neurodevelopmental Disorders, 7, 33-45. doi:10.1077/s11689-008-9001 -8
Hill-Briggs, F., Dial, J., Morere, D., & Joyce, A. (2007). Neuropsychological assessment of persons with physical disability, visual impairment or blindness, and hearing impairment or deafness. Archives of Clinical Neuropsychology, 22, 389-404. doi:10.1016/j.acn.200701.013
Lord, C, & Paul, R. (1997). Language and communication in autism. In J. D. Cohen & F. R. Volkmar (Eds.), Handbook of autism and pervasive developmental disorders (pp. 195-225). New York, NY: Wiley.
Losch, H., & Dammann, O. (2004). Impact of motor skills on cognitive test results in very-low-birthweight children. Journal of Child Neurology, 79(5), 318-322. doi:10.1177/088307380401900502
Mash, E. J., & Barkley, R. A. (2007). Assessment of childhood disorders (4th ed.). New York, NY: Guilford Press.
McCallum, R. (2003). The Universal Nonverbal Intelligence Test. In R. McCallum (Ed.) Nonverbal intelligence tests (pp. 87-109). New York, NY: Kluwer Publishers.
MacLean, H., McKenzie, ?, Kidd, G., Murray, A L., & Schwannauer, M. (2011). Measurement invariance in the assessment of people with an intellectual disability. Research in Developmental Disabilities, 32(3), 1081-1085. doi:10.1016/j.ridd.2011.01.022
Miller, D. C. (2007). Essentials of school neuropsychological assessment. Hoboken, NJ: Wiley.
Mutch, L., Alberman, E., Hagberg, B., Kodama, K, & Perat, M. V (1992). Cerebral palsy epidemiology: Where are we now and where are we going? Developmental Medicine and Child Neurology, 34, 547-551.
Powell-Smith, K. A., Stoner, G., Bilter, K. J., & Sansosti, F. J. (2008). Best practices in supporting the education of students with severe and low-incidence disabilities. In A. Thomas & J. Grimes (Eds.), Best practices in school psychology V (pp. 1233-1248). Bethesda, MD: National Association of School Psychologists.
Quinn, M. (2010). Assessing and intervening with children with speech and language disorders. In D. Miller (Ed.), Best practices in school neuropsychology: Guidelines for effective practice, assessment, and evidence-based intervention (pp. 551-578). Hoboken, NJ: Wiley.
Raven, J., Raven, J. C, & Court, J. H. (1998, updated 2003). Raven's Progressive Matrices. San Antonio, TX: Harcourt Assessment.
Roid, G. (2003a). Stanford-Binet Intelligence Scales, Fifth Edition. Itasca, IL: Riverside.
Roid, G. (2003b). Stanford-Binet Intelligence Scales, Fifth Edition: Interpretive manual. Itasca, IL: Riverside.
Roid, G. H., & Barram, A. (2004). Essentials of Stanford-Binet Intelligence Scales (SB5) assessment. Hoboken, NJ: Wiley.
Roid, G. H., & Miller, L. J. (1997). Leiter International Performance Scale-Revised. Wood Dale, IL: Stoelting.
Roid, G., Nellis, L, & McLellan, M. (2003). Assessment with the Leiter International Performance Scale-Revised and the S-BIT In R. McCallum (Ed.), Nonverbal intelligence tests (pp. 113-140). New York, NY: Kluwer.
Ruiter, S. A. J., Nakken, H., van der Meulen, B. F, & Lunenberg, C. B. (2010). Low motor assessment: A comparative pilot study with young children with and without motor impairment. Journal of Developmental and Physical Disabilities, 22, 33-46. doi:10.1007/S10882-009-9165-5
Sattler, J. M. (2001). Assessment of children: Cognitive applications. San Diego, CA: Jerome M. Sattler Publishers.
Sattler, J. M. (2008). Assessment of children: Cognitive foundations, (5th ed.). San Diego, CA: Jerome M. Sattler Publishers.
Sattler, J. M., & Hoge, R. D. (2006). Assessment of Children: Behavioral, social, and clinical foundations (5th ed.). San Diego, CA: Jerome M. Sattler Publishers.
Shriver, M. D., Allen, K. D., & Mathews, J. R. (1999). Effective intervention of the shared and unique characteristics of children with autism. School Psychology Review, 28, 538-558.
Spears, R., Tollefson, N., & Simpson, R. (2001). Usefulness of different types of assessment data in diagnosing and planning for a student with high-functioning autism. Behavioral Disorders, 25, 227-242.
Walsh, D. M., Finwall, J., Touchette, P. E., McGregor, M. R., Fernandez, G. E. Lott, I. T, & Sandman, C. A. (2007). Rapid assessment of severe cognitive impairment in individuals with developmental disabilities. Journal of Intellectual Disability Research, 57(2), 91-100. doi:10.1111/j.1365-2788.2006.00853.x
Warschausky, S. (2006). Social development and adjustment of children with neurodevelopmental conditions. In K. Hagglund, & A Heinemann (Eds.), Handbook of applied disability and rehabilitation research. New York, NY: Springer.
Warschausky, S., Van Tubbergen, M., & Donders, J. (2008, February). Modified test administration using assistive technology: preliminary psychometric findings with typically developing children. Annual meeting of the International Neuropsychological Society, Hawaii.
Wechsler, D. (2003). Wechsler Intelligence Scale for Children-Fourth Edition. San Antonio, TX: The Psychological Corporation.
Wechsler, D., & Naglieri, J. (2006). Wechsler Nonverbal Scale of Ability (WNV). San Antonio, TX: PsychCorp.
FRANCI CREPEAU-HOBSON, PhD, NCSP, is an assistant professor and the program director of the school psychology program at University of Colorado Denver. Hana Vujeva is a school psychology student at the University of Colorado Denver.