Educational achievement in non-verbal children: Are they learning disabled?

Educationai Achievement in Non-Verbal Children: Are They Learning Disabled? Wayne Fisher, PhD and Larry Burd, MS

From a data-base of all nonspeaking children in North Dakota we analyzed the data on those children functioning above the retarded range to determine the prevalence of children meeting the main inclusion criteria for learning disabilities (LD), a severe discrepancy between IQ and achievement. The mean IQ for this group of 38 nonspeaking, nonretarded children of 104.0 was significantly higher than IQ equivalent scores in the academic subjects of reading (66.4), math (70.4), written language (65.2), and spelling (71.2). Using a stringent criterion for an IQ-achievement discrepancy of2 standard deviations, 27 of 38 (71%) met this criterion in at least one subject. Academic subjects dependent upon higher linguistic functioning, written language and reading, were more affected than spelling and math. While the vast majority (73%) of these 38 children were deaf, the prevalence of IQ-achievement discrepancies was also found in 57% of the nonspeaking children without hearing impairments. These data suggest the common practice of excluding a diagnosis of a learning disability in a deaf child on the basis of the child's hearing impairment may not be appropriate. Further research is needed on the role of speech in academic achievement. Key words: Learning disabilities, hearing impairment, deafness, speech and language disorders, etiology, nonverbal, nonspeaking. Fisher W, Burd L. Educational achievement in non-verbal children: Are they learning disabled? Brain Dev 1991 ;13:428-32

Learning disabilities (LD) continue to be an etiologic enigma. Genetic factors, visual processing dysfunction, central nervous system cytoarchitectonic abnormalities and speech and language problems have all been implicated [1-6]. Approximately four percent of school-age children are afflicted with LD which prevent them from achieving at a level commensurate with their intellectual ability [7]. While both speech and language impairments have been related to LD, the influence of speech defects has been studied less. It is known, however, that school-aged deaf children in addition to the speech defects also have marked impairment in reading performance [5]. Children with unilateral hearing loss tend to have lower verbal IQs and 50% have either failed a grade or received resource From the Kennedy Institute, Johns Hopkins University School of Medicine, Baltimore (WF); Child Evaluation and Treatment Program Medical Center Rehabilitation Hospital and the Departments of Pediatrics and Neuroscience and the School of Medicine and the Center for Teaching and Learning, Grand Forks (LB). Received for publication: July 9, 1990. Accepted for publication: October 30, 1991. Correspondence address: Dr. Larry Burd, Medical Center Rehabilitation Hospital, 1300 South Columbia Road, Grand Forks, North Dakota, 58202, USA.

room assistance [8]. Finally, follow-up studies have consistently found a significant association between early speech problems and later LD [9-10]. What is unclear from the above findings is whether or not a strong relationship exists between severe speech impairment and ill which is independent of other neurodevelopmental disorders associated with deafness. The current definition of LD consists of a single inclusion criterion, a severe discrepancy between ability and achievement, and numerous exclusion criteria (e.g., severe sensory impairment, inadequate educational experience, mental illness, etc) [11]. These exclusionary conditions are supposed to rule out the diagnosis of a LD only when the severe ability-achievement discrepancy has resulted from these conditions. In practice, however, the mere presence of one of these co-occurring conditions often precludes consideration of the diagnosis of LD. Such practices are valid only if all children with these co-occurring conditions show a severe ability-achievement discrepancy. Currently, there exists no definitive empirical evidence supporting the common presumption that the presence of a marked hearing impairment in a child with severe academic delay would rule out the possibility of aLD. While there may be a number of ways to provide such evidence, most such experiments would be impractical.

For example, one might attempt to identify a group of monozygotic twins wherein one of the siblings acquired a severe hearing loss early in life and then assess and compare the affected and non-affected twins for the presence of LD. Alternatively, if one could find a population of children with severe hearing impairment and severe academic delay for whom prescribing and fitting hearing aids would eliminate the hearing loss, then one could determine whether such an intervention would also, over time, eliminate the academic delay. Given the implausibility of such studies, we attempted to test this assumption in a less direct manner. In this study we attempted to utilize prevalence data from a total population study of nonverbal children to determine whether children whose lack of speech was associated with deafness were much more likely to show a severe discrepancy between IQ and achievement. The goals of the current study were to a) ascertain the prevalence of LD (defined as a severe discrepancy between IQ and achievement, regardless of other sensory or physical impairments) in nonverbal children with and without severe hearing loss; and b) determine whether a LD was ever diagnosed in either group of children.

METHODS Subjects In a previous study, we attempted to identify all the nonspeaking, school-age children within the State of North Dakota [12]. We surveyed all of the educational and developmental disability service providers in the State of North Dakota and asked them to complete a data-base questionnaire on all North Dakota children who spoke 15 or fewer intelligible words. This criterion was based on the assumption that with normal development, speech is becoming a functional means of communication toward the end of the single-word stage when a child's expressive vocabulary is typically around 15 to 20 words [13]. That is, the term (non-speaking) was used to describe children who do not have functional oral communication. The agencies surveyed for this study included each special education district in the state, all child group homes in the state, the state school for the mentally retarded, the state hospital, the state schools for the blind and deaf, three private residential institutions serving multiply-handicapped children as well as the files of the state's comprehensive evaluation center where the authors work. Children excluded from this study were those who spoke a foreign language and those who had a temporary loss of speech (e.g., laryngitis, recent head injury, meningitis, etc.) Children placed in residential facilities in other states were not excluded from the study and information was acquired through the child's local school district.

Procedures Service providers who did not respond to the initial inquiry were contacted by mail twice, then by phone, and finally visited by one of the investigators. Using these procedures, all of the service providers in the state completed the survey (i.e., the response rate was 100%) and we are confident that all of the non-speaking, school-age children who met our inclusion/exclusion criteria were identified. For purposes of the current investigation, we utilized this data-base to identify those non-speaking children in the state who were functioning above the mentally retarded range in order to examine the following questions: 1) what percentage of nonspeaking, deaf children show a severe discepancy between IQ and achievement in one or multiple academic areas; 2) what percentage of nonspeaking, hearing children show a severe discrepancy between IQ and achievement in one or multiple academic areas; 3) among this population of nonspeaking children, were academic delays more severe in certain academic areas; and 4) what percentage of each of these groups received a diagnosis of aLD. In order to answer these questions, each child's test scores in the areas of reading, math, written language and spelling were converted first to percentile scores and then to IQ equivalent scores using the tables from Sattler [14]. This conversion was done because different academic measures were used across subjects to ascertain measures of academic achievement. Finally, IQ-achievement discrepancy scores were calculated for each child in each academic subject area using the Z-score discrepancy method [15] . While this formula has been criticized because it does not correct for test reliability or regression toward the mean, the formula is still the most widely used in public education and thus the most appropriate for answering the above questions [15]. The IQ tests used in this study are described in our paper on the prevalence of nonverbal children in North Dakota [12]. The I Q tests were often nonverbal tests and in some cases the verbal portion of the Weschler scales was not administered. RESULTS Forty-two children, 20 males and 22 females, met the inclusion/ exclusion criteria of being between 7 and 21 years of age, nonspeaking, and having an IQ above the mentally retarded range (i.e., an IQ of 71 or greater). The average age for this group was 169 months (14 years, 1 month). Of these children, IQ scores were available for all 42 and achievement scores were available on 36 children for reading, 34 for math, 38 for written language, and 38 for spelling. The means and standard deviations for IQ and achievement scores for this group are presented in Table 1. A one-way, within subjects ANOV A was computed to test for main effects differences among these

Fisher and Burd: Non-verbal children and LD 429

scores and significant differences were found, F (4,148) = 55.57 P < .0001. A Hotelling-Lawley-Trace was used as an additional measure of mutivariate test of significance. The T2 value was 3.1584 and the associated F-value (4,34) was 26.85 a p of< .0001. Additional comparisons between individual means were done using one tailed paired t-tests with Bonferroni correction. The mean IQ score of 104.0 was significantly higher than each of the academic quotients (all t-values > 7.91; all p-values < .001). The IQ scores of this group appeared to remain stable over time. In several children longitudinal IQ scores support the data presented in this paper which is crosssectional and not longitudinal. However, different trends for IQs over time have been observed in children with IQs below 70 where in several children IQs tend to decline over time. To determine the number of nonverbal children meeting the inclusion criterion for LD, we applied two frequently utilized cutoffs for a significant discrepancy between intelligence and achievement: an IQ-achievement discrepancy score of 1.5 standard deviations (SD) or greater, and IQ-achievement discrepancy score of 2 SD or greater. These results are summarized in Table 2 for each of the four achievement measures. As can be seen, a high percentage of non-speaking children met the inclusion criterion for a LD in each subject, with the highest percentage occurring in the subjects of written language and reading, and fewer occurring in spelling and math. In Fig 1, data are presented regarding the number of children who met the inclusion criterion for a LD in one

or more academic subject areas using the same 2 SD cutoff criterion. As can be seen, 71 % of nonspeaking children functiOning above the retarded range met criterion for at least one LD when the cutoff score of 2 standard deviations was utilized. An even higher percentage, 84%, met the 1.5 SD criterion in at least one of the academic subject areas . As can be seen in Fig 1, most of these children met the criteria for a LD when the more stringent cri-

Percent of Nonverbal Children with 0,1,2,3 or 4 Learning Disabilities N = 38

100

~

o

80

c '"~

1.5 SO Discrepancy y 2.0 SO Discrepancy y

60

'"

a.. 40

20

o

o Number

of

2

3

4

learning d isabil ities

Fig 1 The graph demonstrates the numbers of non· verbal children (N = 38) with either 0, 1,2, 3, or 4 learning disabilities for both

the 1.5 SD and 2 SD cutoffs.

Table I The means and standard deviations on IQ and achievement scores for non-speaking children IQ

Mean Standard deviation

Written Spelling Reading Math quotient quotient language quotient quotient

104.0*

66.4

70.4

65 .2

71.2

16.7 n = 42

20.01 n = 36

12.7 n = 34

17.9 n = 38

22.5 n = 38

Regression Lines Demonstrating the Relationship between Age and IQ and Academic Quotients

120

100

* Significantly greater than each achievement mean (all p < .001). Table 2 The numbers and percentage of children meeting the two standard deviation cutoff criteria for a learning disability by subject

AQ < IQ by at least 2 SDs AQ < IQ by at least 1.5 SDs AQ:

Reading

Math

Written language

Spelling

23 of 36 63.9%

15 of 34 44.1%

25 of 38 65.8%

20 of 38 52.6%

27 of 36 75%

22 of 34

30 of 38 78.9%

25 of 38 65.8%

64.7%

academic quotient.

430 Brain & Development, VoIU, No 6, 1991

40 20

.................

Reading Quotients

•.••••••

Math Quotients

•.•.• .•.

Spelling Quotients

-- . . -

Written Language Quotients

--

IQ

0+-------------------------------------, 7

Years

21

Fig 2 Regression line depicting the strong negative relationship between age, IQ, and academic quotients.

terion was utilized. Finally, a sizable proportion, 39%, met the 1.5 standard deviation criteria for a LD in all four of the academic areas measured and 34% had a LD in all four areas when the 2 SD cutoff was utilized. In Fig 2, the relationships between age and IQ and achievement quotients are presented. As can be seen, IQ scores do not appear to vary across the age ranges of these children. However, academic quotients show significant, negative correlations with age. While these are crosssectional data, the results raise the question of whether LDs in these children tend to worsen over time. In fact, all children over 11.6 years of age met criteria for a diagnosis of LD in at least one subject. The numbers of children are fairly evenly distributed across the age range and in fact slightly larger numbers of children are found at the older end of the age range. The prevalence of children meeting the inclusion criterion for LD was high for both the hearing and deaf children in the popUlation, but was considerably higher among the deaf children. Among the 31 deaf children with complete data, 90% met the 1.5 SD cutoff for LD and 84% met the 2 SD cutoff. Among the 7 hearing children with complete data, 57% met the 1.5 SD cutoff and 14% met the 2 SD cutoff. None of the children in this study had been diagnosed as having aLD. DISCUSSION

In this total population study (every non-verbal, nonretarded child in the State of North Dakota), a very strong relationship was observed between non-speaking status and severe academic impairments. Academic impairments were negatively correlated with age, suggesting that the academic impairments tend to worsen over time. None of these children were identified as LD and it is unclear whether identification and intervention might lessen this widening IQ-achievement discrepancy. The authors are not aware of earlier studies demonstrating that academic declines can exist in children with speech deficits without impairments in IQ scores. In addition, the prevalence of children who may meet criterion for a LD in this popUlation may be higher than previous reports on children with speech defects [5, 10, 13]. This is probably due to the severity of speech defects in our population. Also interesting is the presence of mUltiple LD in a population of children with normal IQ scores. In families selected for autosomal dominant LD, the rate of affected persons was not as great as in this sample [1, 2, 11, 16]. Indeed, the authors are aware of no marker which so routinely and regularly predicts such a devastating academic failure other than mental retardation. How might being non-verbal and of average intelligence exert such a significant effect on learning? Clearly, it is not due solely to hearing impairment. In this popula-

tion we separated those children with deafness from those who had no known hearing impairment and we still found a high rate of LD in both groups. Nor can the academic delays found in this population be easily attributed to global language deficits as one would expect IQ to be significantly affected by pervasive impairments in language functioning. Age was an important factor in this group as children above the age of 11 1/2 years were all LD. Rapin and others have suggested a strong relationship between speech and language skills and LD [17]. It may be that the central nervous system problems which cause speech and language problems also adds an extra layer of academic learning difficulties. Alternatively, it is possible that speech is critical to the acquisition of academic skills, but plays less of a role in intellectual functioning. This latter explanation would be more consistent with the finding that some children with elective mutism often will demonstrate significant improvement in academic skills when they begin to speak [18]. From a practical standpoint, these results suggest that nonspeaking, hearing and hearing-impaired children show marked academic problems which worsen over time. If at least some of these problems are the result of LD, then it is inappropriate to rule-out the diagnosis of LD based solely on the presence of a hearing impairment or the lack of speech. It is important that educational programs assess nonspeaking, hearing and hearing impaired children for ability-achievement discrepancies and, at a minimum, consider the diagnosis of a LD in children with a descrepancy between these scores. Regardless of whether the diagnosis of LD is applied, the high rates of severe academic problems in this popUlation suggest that additional attention and resources are needed to address these problems. Replication of this work is needed among populations with comparable ascertainment rates. We are currently conducting a prevalence study of elective mutism to further explore the relationship between speech and academic achievement. Perhaps the most important finding of this study is the need to identify intervention strategies that can prevent the development of the gap between ability (IQ) and academic achievement that appears to increase with age in this population. REFERENCES 1. Smith SD, Goldgar DE. Single gene analyses and their application to learning disabilities. In: Smith SD, ed. Genetics of learning disabilities. San Diego: College-Hill Press, 1986: 47-65. 2. Smith SD, Kimberling WI, Pennington BF, Lubs HA. Specific reading disability: identification of an inherited form through linkage analysis. Science 1983;219: 1345-7. 3. Ayres AI. Learning disabilities and the vestibular systems. J Learn Dis 1978;11:30-41. 4. Galaburda AM, Sherman GF, Rosen GD, Aboitizi F, Gesch-

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Brain & Development, Vol 13, No 6, 1991

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