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Abstract
A large body of research has documented the relationship between attention-deficit hyperactivity disorder (ADHD) and reading difficulties in children; however, there have been no studies to date that have examined errors made by students with ADHD and reading difficulties. The present study sought to determine whether the kinds of achievement errors made by students diagnosed with ADHD vary as a function of their reading ability. The participants in this study were 91 students in the ADHD clinical validity standardization sample of the Kaufman Test of Educational Achievement–Third Edition (KTEA-3), as well as a control group of 63 students selected from the larger standardization sample. Students with ADHD and reading difficulties demonstrated a statistically significant greater amount of errors across tests of academic achievement. Findings from the study are discussed within the context of past research, as well as implications for the field of school psychology and practitioners.
Attention-deficit hyperactivity disorder (ADHD) is currently a major public health concern as about 10% of school age children meet criteria for diagnosis (Center for Disease Control, 2015). Symptoms of ADHD include inattention, distractibility, motor restlessness, hyperactivity, and impulsivity (American Psychiatric Association, 2013). These symptoms, both directly and indirectly, negatively impact students’ academic performance (Frazier, Youngstrom, Glutting, & Watkins, 2007; Hinshaw, 1992; Loe & Feldman, 2007; Kent et al., 2011), particularly in reading (Brock & Knapp, 1996). The incidence of reading disabilities in children with ADHD occurs at a rate higher than the normal population, with reported rates of comorbidity between 25% and 40% (August & Garfinkel, 1990; Dykman & Ackerman, 1991; McGee & Share, 1988; Semrud-Clikeman et al., 1992; Willcutt & Pennington, 2000). In addition, a recent review of the literature estimated that approximately 31% to 45% of children with ADHD have a comorbid learning disability (DuPaul, Gormley & Laracy, 2013).
A large body of research has shown the relationship between ADHD and reading difficulties is likely multifactorial and is due to a combination of cognitive, psychological, and neurobiological anomalies (McGrath et al., 2011; Pennington, 2006; Willcutt et al., 2010). Due to these contributing factors, children and adolescents with ADHD frequently demonstrate poorer performance in reading comprehension (Kempe, Gustafson, & Samuelsson, 2011; Martinussen & Mackenzie, 2015; Stern & Shalev, 2013), word reading (Bental & Tirosh, 2007; Lombardino, Riccio, Hynd, & Pinheiro, 1997; Willcutt, Pennington, Olson, Chhabildas, & Hulslander, 2005), and reading fluency (Ghelani, Sidhu, Jain, & Tannock, 2004; Jacobson et al., 2011; Willcutt, Pennington, Olson, & DeFries, 2007). Although many studies have explored the relationship between ADHD and reading difficulties, there have been no studies to date that have examined the types of errors students with ADHD commit in reading and other academic areas (i.e., writing and math). Given the prevalence of reading difficulties in children with ADHD, it is important for researchers and practitioners to understand and identify these types of errors to design and implement the most appropriate and effective interventions.
Behavioral analysis of errors made on achievement tests dates back to the early 1900s, but interest in this topic has fluctuated over time. The investigation of the causes of errors in mathematics computation have historically drawn a significant amount of attention (Brown & Burton, 1978; Young & O’Shea, 1981), while reading and spelling studies have been much more sporadic and less unified in their approach to the topic (Fowler, Liberman, & Shankweiler, 1977). Nonetheless, observations of children’s reading and spelling errors have led to the development of well-known theories of literacy (Ehri, 1986; Frith, 1985). In addition, researchers have used error analysis for diagnostic purposes. McCloskey and colleagues in Kaufman and Kaufman (1985) suggested that the information provided by an analysis of a student’s incorrect responses could assist many beneficial purposes for the clinician who desires to carry the diagnostic assessment procedure beyond the interpretation of standard scores. They posited that diagnosticians can use error analysis information to identify skill areas in which interventions should be planned or further diagnostic testing should be carried out. Additionally, the authors noted that error analysis information could be utilized to prepare interim instructional objectives for classroom intervention or short-term Individualized Education Program (IEP) objectives for special education students, and to help identify teaching techniques that effectively increase skill mastery levels. More recently, McGeown, Medford, and Moxon (2013) used error analysis to identify individual differences in children’s reading and spelling strategies, and to identify the cognitive skills underlying their strategy use. Based on their research, the authors concluded that different cognitive skills predicted reliance upon different strategies.
Despite a revived interest in error analysis and its potential as a diagnostic tool, as well as for developing targeted interventions, current research on the topic is sparse. The purpose of the current study is to determine whether the kinds of achievement errors made by students diagnosed with ADHD vary as a function of their reading ability. We hypothesized that children with ADHD and reading difficulties will exhibit a significantly greater amount of errors on tasks of reading, writing, math, and language in comparison with students with ADHD without reading difficulties, as well as a normative control group.
Method
Participants
The participants in this study were students in the ADHD clinical validity sample who were tested during the standardization and validation of the Kaufman Test of Educational Achievement–Third Edition (KTEA-3; Kaufman & Kaufman, 2014), between August 2012 and July 2013, as well as a control group selected from the larger standardization sample. Demographic data for these samples are provided in the KTEA-3 Technical and Interpretive Manual (Kaufman, Kaufman, & Breaux, 2014). As stated in the Manual,
Individuals included in the ADHD sample had cognitive ability scores in the average range or above with no previous diagnosis of language disorder or learning disability. Approximately two thirds of the sample was taking a prescription medication for ADHD on a regular basis. (Kaufman et al., 2014, p. 85)
The overall ADHD sample (N = 91) included 52 males and 39 females in Grades K-12, who ranged in age from 5 to 18 (M age = 11.2; SD = 3.6). The sample was 69.2% Caucasian, 11.0% Hispanic, 5.5% African American, 3.3% Asian, and 11.0% “other” (e.g., Native American). Parents’ education (mostly mothers, used as an estimate of socioeconomic status) was 8.8% with < 12 years of schooling, 7.7% high school graduates, 36.3% with 1 to 3 years of college or technical school, and 47.3% college graduates. All participants lived in the United States with 29.7% residing in the midwest, 11.0% in the northeast, 48.4% in the south, and 11.0% in the west. Diagnoses were made according to the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR; American Psychiatric Association, 2000).
The ADHD sample was split into two groups: children with ADHD without reading problems (n = 46) and those with reading difficulties (n = 45). Classifications of ADHD with reading problems were verified by meeting one of two criteria. The first criterion was a standard score of below 90 on two or more reading subtests. The second criterion was a standard score of below 85 on one reading subtest. The reading subtests included Letter and Word Recognition, Nonsense Word Decoding, Reading Comprehension, Reading Vocabulary, Word Recognition Fluency, Decoding Fluency, and Silent Reading Fluency. Just under half the students in this group were identified as having ADHD with reading problems (n = 45).
Demographic information for the three groups is presented in Table 1. A review of the data indicated that the ADHD subsamples were similar on important demographic variables. To select a matched control group of “normal” children and adolescents, a random sample—stratified by grade level—was selected from the KTEA-3 age-based and grade-based standardization samples. First, 578 students without ADHD in Grades 1 through 12 who had minimal missing data on the error analysis factor scores (dependent variables for the analyses) were identified. Then, a control group of 63 students in Grades 1 to 12 (median grade = 6) was selected to match the grade distributions and to approximate the gender, ethnicity, and parents’ education distribution of the ADHD subsamples.
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Table 1. Demographics for the Experimental and Control Samples.
Measures
KTEA-3
The KTEA-3 (Kaufman & Kaufman, 2014) is a measure of academic achievement for children ages 4 to 25. The KTEA-3 also includes an error analysis component that allows the administrator to denote if an answer is wrong and also to identify the type of error made by the examinee and whether there is a consistent pattern of errors made within and across subtests. The present study utilized error analysis from the following subtests: Letter and Word Recognition, Spelling, Nonsense Word Decoding, Written Expression, Oral Expression, Reading Comprehension, Language Comprehension, Phonological Processing, Math Concepts and Applications, and Math Computation.
Data Analysis Plan
A multi-step process was used to investigate the relationship between students with ADHD and reading problems and their corresponding KTEA-3 errors scores on tasks of academic achievement. The first analytic step in this process was the derivation of factor scores, which is described in detail elsewhere (Choi et al., 2017; Hatcher et al., 2017; O’Brien et al., 2017). Briefly, the KTEA-3 utilizes a unique error analysis methodology based on the specific subskills measured by a given subtest. For 10 of the KTEA-3 subtests, curriculum experts identified the different categories of errors students are likely to make on each subtest. For each subtest, the total number of an examinee’s errors per category is transformed into a descriptive categorization (weakness, average, or above average) based on a normative comparison. Each student’s total number of errors per category was compared with that of other students in their grade who completed the same items on the same form as determined by the student’s basal and ceiling, and was then dichotomized as either a weakness (0) or average/above average (1). Factor groups were then derived using exploratory factor analysis (EFA) or principal components analyses (PCA). Related to the current study, four factors were extracted from the comprehension and expression subtests, three factors for Letter and Word Recognition and Math Concepts and Applications, and two factors for Nonsense Word Decoding, Spelling, Phonological Processing, and Math Computation subtests. R version 3.2.3 generated Bartlett factor scores for each of the extracted factors.
In this analysis, the kinds of achievement errors made by students diagnosed with ADHD were examined to determine if they varied as a function of students’ reading ability. To test this hypothesis, several one-way analyses of covariance (ANCOVA) were conducted with subtest error factor scores as dependent variables and grouping variable (ADHD with reading problems, ADHD without reading problems, and control) as the independent variable. To adjust for crystallized knowledge (Gc), the KTEA-3 Oral Language Index was used as a covariate for the majority of analyses. As Listening Comprehension and Oral Expression are utilized in the development of the Oral Language Index, analyses for these subtests were conducted using a one-way analysis of variance (ANOVA) without adjusting for the Oral Language Index. In addition, four subtests (Nonsense Word Decoding, Phonological Processing, Math Concepts and Applications, and Math Computation) had statistically significant age and group interaction effects with the Oral Language Index. Thus, for these subtests as well, ANOVAs were conducted, without adjusting for the Oral Language Index.
To examine the assumption of homogeneity of within group covariance matrixes, a two-step analysis process was utilized (Huberty & Petoskey, 2000). First, for each analysis, the Box F test was calculated. The Box tests were statistically significant for all subtests, except Nonsense Word Decoding, Phonological Processing, and Listening Comprehension. However, as noted by Huberty and Petoskey (2000), the Box test is an extremely powerful test. Therefore, as a follow up analysis, the natural log of the determinant of the covariance matrix for each level of the independent variable was compared with the natural log determinant of the pooled matrix (Huberty & Petoskey, 2000; Olejnik, 2010). In the judgment of the researchers, the differences were relatively close, with the largest difference between a given group and the pooled natural log determinant equal to −1.9.
Results
Differences Between Groups and KTEA-3 Reading, Spelling, and Phonological Processing Error Scores
Table 2 shows group means and standard deviations on the KTEA-3 subtests. Group means and standard deviations on error factors for each KTEA-3 subtest are presented in Table 3, while Table 4 shows pairwise comparisons by KTEA-3 subtests and error factors. Students in the ADHD-reading problems group had the lowest mean scores on Letter and Word Recognition and its three corresponding error factors, Contextual Vowel Pronunciation, Intermediate Letter-Sound Knowledge, and Consonant-Pattern Knowledge, compared with the ADHD-no reading problems group and the matched control group. Statistically significant differences were present for Contextual Vowel Pronunciation, F(2, 136) = 12.16, p < .001. There were no statistically significant differences between groups for Intermediate Letter-Sound Knowledge, F(2, 136) = 1.72, p > .05, or Consonant-Pattern Knowledge, F(2, 136) = 2.23, p > .05. Post hoc comparisons showed that students in the ADHD-reading problems group had significantly higher error factor scores than students in the ADHD-no reading problems group (p < .01) and the matched control group (p < .001) for Contextual Vowel Pronunciation. The difference between students in the ADHD-no reading problems group and the matched control group was not statistically significant (p > .05).
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Table 2. Group Means and Standard Deviations on KTEA-3 Subtest Scores.
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Table 3. Group Means and Standard Deviations on Error Factors for Each Subtest.
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Table 4. ANCOVA and ANOVA Results Summary and Pairwise Comparisons by KTEA-3 Subtests and Error Factors.
Students in the ADHD-reading problems group had the lowest mean scores on Nonsense Word Decoding and its two corresponding factors, Letter-Sound Knowledge and Basic Phonic Decoding. There was a statistically significant difference between groups for Basic Phonic Decoding, F(2, 119) = 4.42, p < .01, but not for Letter-Sound Knowledge, F(2, 119) = 2.62, p > .05. Post hoc comparisons showed that students in the ADHD-reading problems group had statistically significant higher error scores than students in the ADHD-no reading problems group (p < .05) and the matched control group (p < .05) on Basic Phonic Decoding. The difference between students in the ADHD-no reading problems group and the matched control group was not statistically significant (p > .05).
Students in the ADHD-reading problems group had the lowest mean scores on Spelling and its two corresponding factors, Sound to Letter Mapping and Phonological Awareness. There was a statistically significant difference between groups for Sound to Letter Mapping, F(2, 112) = 8.94, p < .001, but not for Phonological Awareness, F(2, 112) = 2.24, p > .05. Post hoc comparisons showed that students in the ADHD-reading problems group had significantly higher error scores than students in the ADHD-no reading problems group (p < .01) and the matched control group (p < .001) for Sound to Letter Mapping. The difference between students in the ADHD-no reading problems group and the matched control group was not statistically significant (p > .05).
Students in the ADHD-reading problems groups had the lowest mean scores on Phonological Processing and the error factor Basic Phonological Awareness. There was a statistically significant difference between groups for Basic Phonological Awareness, F(2, 141) = 3.38, p < .05, but not for Advanced Phonological Processing, F(2, 141) = .15, p > .05. Interestingly, post hoc comparisons showed no statistically significant differences between groups for Basic Phonological Awareness (p > .05).
Differences Between Groups and KTEA-3 Comprehension and Expression Error Scores
Students in the ADHD-reading problems groups had the lowest mean scores on Reading Comprehension and its two corresponding factors, Expository/Literal and Narrative/Inferential. There was a statistically significant difference between groups for the Expository/Literal, F(2, 133) = 5.07, p < .01, and Narrative/Inferential, F(2, 133) = 4.23, p < .05, error factors. Further, post hoc comparisons showed significantly higher error scores in the ADHD-reading problems group than students in the matched control group for the Expository/Literal (p < .01) and Narrative/Inferential (p < .01) error factors. The differences between students in the ADHD-reading problems group and the ADHD-no reading problems group, as well as between the ADHD-no reading problems group and the matched control group were not statistically significant for either factor (p > .05).
Students in the ADHD-reading problems groups had the lowest mean scores on Listening Comprehension and the Narrative/Inferential factor. However, there were no statistically significant differences between groups on either error factors of Listening Comprehension: Expository/Literal, F(2, 130) = 2.48, p > .05, Narrative/Inferential, F(2, 131) = .70, p > .05.
Students in the ADHD-reading problems groups had the lowest mean scores on Written Expression and its two corresponding factors, General Written Expression and Writing Mechanics. There was a statistically significant difference between groups for Writing Mechanics, F(2, 140) = 4.33, p < .01, but not for General Written Expression, F(2, 140) = 2.59, p > .05. Post hoc comparisons showed significantly higher error scores in the ADHD-reading problems group than students in the matched control group for Writing Mechanics (p < .01). The differences between students in the ADHD-reading problems group and the ADHD-no reading problems group, as well as between the ADHD-no reading problems group and the matched control group were not statistically significant for this error factor (p > .05).
Students in the ADHD-reading problems groups had the lowest mean scores on Oral Expression and its two corresponding factors, Oral Expression Grammar and General Oral Expression. There was a statistically significant difference between groups for Oral Expression Grammar, F(2, 151) = 4.30, p < .05, and General Oral Expression, F(2, 151) = 17.38, p < .001. Post hoc comparisons showed statistically significant higher error scores in the ADHD-reading problems group than students in the matched control group for Oral Expression Grammar (p < .05); however, there were no statistically significant differences between the ADHD-reading problems group and the ADHD-no reading problems group on this factor (p > .05). Additionally, students in the ADHD-reading problems group had significantly higher error scores than students in the ADHD-no reading problems group (p < .05) and the matched control group for General Oral Expression (p < .05). There was no statistically significant difference between the ADHD-no reading problems group and the matched control group for General Oral Expression (p > .05).
Differences Between Groups and KTEA-3 Math Error Scores
Students in the ADHD-reading problems groups had the lowest mean scores on Math Concepts and Applications and two of its three corresponding factors, Math Calculation and Complex Math Problems. There was a statistically significant difference between groups for Math Calculation, F(2, 116) = 6.79, p < .01, but not for Geometric Concepts, F(2, 151) = .93, p > .05, or Complex Math Problems, F(2, 116) = 1.56, p > .05. Post hoc comparisons showed significantly higher error scores in the ADHD-reading problems group than students in the ADHD-no reading problems group (p < .01) and the matched control group (p < .01) for Math Calculation. There were no statistically significant differences between the ADHD-no reading problems group and the matched control group for Math Calculation (p > .05).
Students in the ADHD-reading problems groups had the lowest mean scores on Math Computation and the Basic Math Concepts error factor. There was a statistically significant difference between groups for Basic Math Concepts, F(2, 102) = 6.32, p < .01, but not for Addition, F(2, 107) = 1.45, p > .05. Post hoc comparisons showed significantly higher error scores among students in the ADHD-reading problems group than among students in the ADHD-no reading problems group (p < .05) and the students in the matched control group (p < .05) for Basic Math Concepts. There were no statistically significant differences between the ADHD-no reading problems group and the matched control group on this factor (p > .05).
Discussion
The present study sought to investigate the differences between children with ADHD with and without comorbid reading difficulties in a large standardization sample. It was hypothesized that children with ADHD and reading difficulties would have greater impairment on the KTEA-3 error factors than those without reading difficulties. Consistent with our hypotheses, we found that students with ADHD and reading difficulties demonstrated greater errors across reading, writing, and math subtests. More specifically, there were statistically significant differences between the ADHD groups on six of the 22 error factors: Contextual Vowel Pronunciation, Basic Phonic Decoding, General Oral Expression, Sound to Letter Mapping, Math Calculation, and Basic Math Concepts. When compared with the control group, children with ADHD and reading difficulties exhibited statistically significant greater errors across 10 of the error factors: Contextual Vowel Pronunciation, Expository/Literal Reading Comprehension, Narrative/Inferential Reading Comprehension, Basic Phonic Decoding, Writing Mechanics, Oral Expression Grammar, General Oral Expression, Sound to Letter Mapping, Math Calculation, and Basic Math Concepts.
Interestingly, there were no statistically significant differences on error factors between the ADHD group and the control group. This latter finding highlights that children with ADHD and comorbid reading difficulties are at considerably greater risk for academic underachievement when compared with those with ADHD alone, which is consistent with previous research (Katz, Brown, Roth, & Beers, 2011). Perhaps more importantly, this finding preliminary suggests that reading difficulties are the primary factor contributing to academic errors in children with ADHD.
In light of the present study’s findings, it is worth exploring the underlying cognitive processes that may contribute to the types of errors committed by children with ADHD and reading difficulties. McGrath and colleagues (2011) proposed a multiple deficit model to understand the relationship between ADHD and reading disability. Their findings showed that a combination of phoneme awareness, language skill, and processing speed accounted for an estimated 80% of the variance of reading difficulty symptoms (speed and accuracy of single word reading). It is interesting to note that in the present study, children with ADHD and reading difficulties exhibited significantly greater errors on factor types that are associated with phonological (e.g., Contextual Vowel Pronunciation, Basic Phonic Decoding, Sound to Letter Mapping) and language (e.g., General Oral Expression, Oral Expression Grammar) skill. Given that processing speed also appears to account for a significant portion of the variance between ADHD and reading difficulties (McGrath et al., 2011), this cognitive process may play a significant role across all error types in this population. Future research should examine if certain cognitive weaknesses contribute to specific error types in this clinical population.
It is important to highlight how the results of the present study can inform practice for school psychologists and practitioners. Given that children with ADHD and reading difficulties exhibited the greatest amount of errors across academic achievement tasks, these students will require individualized, intensive academic remediation to address their individual needs. As demonstrated in the present study, children with ADHD and reading difficulties not only had the greatest difficulties on tasks of reading, but also had the lowest scores on tests of writing, mathematics, and language skill when compared with their ADHD only counterparts and matched control group. Thus, practitioners working with children with ADHD and reading difficulties should conduct a comprehensive evaluation to determine if there are weaknesses in certain aspects of other academic skills beyond reading. It will crucial for these children to receive appropriate, evidence-based reading instruction to improve their reading skills, as well as performance across other academic skill areas.
In addition, analyzing error scores may elucidate what specific types of interventions are needed to address these academic difficulties. Results of the present study suggest that as a whole, children with ADHD and reading difficulties have difficulties with basic, foundational phonological skills, and as such, interventions should be implemented to address this specific skill deficit. Finally, by taking a more detailed and narrow view of individualized learning needs, practitioners can create clear and concise goals on IEPs to aid in the success of these students.
Interestingly, this was the first study to our knowledge to specifically analyze error scores for children with ADHD and reading problems. Rather than just relying on expert consensus alone, EFA and PCA analyses were employed to systematically define error factors. Additionally, given the large amount of evidence that overall intelligence can affect reading ability (e.g., Allen, 1944; Bond & Fay, 1950; Lennon, 1950), the current study controlled for this covariate using the Oral Language Index. Further, errors made by the ADHD with reading problems group were compared with both an ADHD without reading problems and a normative control group, which was stratified by grade level and was similar on all other demographic variables (i.e., gender, age, ethnicity, parental level of education).
Although each comparison group (i.e., ADHD with reading problems, ADHD without reading problems, control group) was similar on most demographic variables, there was little variability in ethnic background and level of parental education for each group, with a majority of the students identifying as Caucasian and having more educated parents. As research suggests that both ethnicity and parental education co-vary with, or may be factors that influence a child’s reading ability (e.g., Aud, Fox, & KewalRamani, 2010; Friend et al., 2009; Samuelsson et al., 2005), this may have lead to a skewed distribution of reading scores and warrant controlling for these variables in the model.
Further limitations include the somewhat arbitrary criteria that were set to distinguish between students with and without reading problems. For the current study, two scores below a 90 or one score below an 85 were used as a cut-off for group membership. However, these criteria come with a certain set of limitations. Specifically, these criteria set the standard that a one-point difference between subtest scores will mean the difference of being identified as having a reading problem versus not having a reading problem, which is likely not clinically meaningful. Similarly, this method may allow students who scored poorly on one subtest (i.e., below an 85), but within the average range or higher on all other subtests to be included in the reading problems group, though this may not necessarily signify reading difficulties.
The findings of the current study implicate the need for additional research on error analysis using ADHD clinical samples with reading difficulties to get a better understanding of the unique ways these individuals function and to inform the development of educational testing and reading interventions. Moreover, future studies should aim to include more heterogeneous samples in terms of variables like ethnicity and parental level of education or treat them as covariates. This will allow for a clearer dissemination whether the differences noted in current research are due to particularities of individuals that have an ADHD diagnosis and reading difficulties or due to differences in sample characteristics. In addition, future research should attempt to determine and implement a more systematically and practically defensible way of identifying students with reading difficulties to provide better insight into what the clinical implications of current research are and to better inform reading interventions for this population.
Acknowledgements
The authors wish to thank NCS Pearson for providing the standardization and validation data for the Kaufman Test of Educational Achievement–Third Edition (KTEA-3). Copyrights by NCS Pearson, Inc. used with permission. They also wish to thank Alan and Nadeen Kaufman for their supervision of the comprehensive error analysis research program.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
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