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Abstract
An understanding of the strengths, weaknesses, and achievement profiles of students with giftedness and learning disabilities (G&LD) is needed to address their asynchronous development. This study examines the subtests and error factors in the Kaufman Test of Educational Achievement–Third Edition (KTEA-3) for strength and weakness patterns of students with G&LD in higher and lower level thinking skills by comparing G&LD students (n = 196) with academically gifted (GT; n = 69) and specific learning disability (SLD) students (n = 90). Several one-way MANCOVAs were conducted with subtest error factor scores as dependent variables and grouping variable (G&LD, GT, or SLD) as the independent variable. The G&LD means scores across subtests were in between the two control groups. On many higher level thinking tasks, the G&LD group scored similar to the gifted group. The results support the use of error analysis to gain further understanding into the profile of students with G&LD.
Students who exhibit giftedness along with a learning disability (gifted learning disabled or G&LD) often display asynchronous academic development. They possess both qualities of giftedness and learning difficulties; however, they do not solely function like a gifted student or as a student with learning disability. Instead, they display a unique combination of strengths and weaknesses, with the strengths often masking areas of struggle (Antshel, 2008; Antshel et al., 2007). Consequently, Al-Hroub (2011) recommended that
. . . teachers, gifted specialists, LD specialists, and school psychologists need to be trained to look less at the large-scale scores and gross indicators and to focus more on the patterns of scores that reflect the unique cognitive and academic processing qualities that differentiate gifted students with LDs. (p. 38)
Furthermore, Berninger and Abbott (2013) noted that “[s]uperior verbal reasoning may mask effects of dyslexia on oral and written language skills” (p. 228).
Early intervention is needed to provide a protective factor for the student who is G&LD against low self-confidence and motivation, ineffective self-efficacy, and most importantly, a loss of their love for learning (Reis, McGuire, & Neu, 2000). As such, developing a more comprehensive understanding of the unique patterns of strengths and weaknesses students with G&LD display will provide school professionals with the tools necessary to develop evidence-based interventions that utilize strengths to target weaknesses.
A Misunderstood Population
In a society that lauds strengths and laments weaknesses, the existence of both giftedness and a learning disability can be difficult for the student to navigate (Nicpon, Allmon, Sieck, & Stinson, 2011). A classification of G&LD is essential for students to receive access to measures that specify and guarantee specific intervention, such as Individualized Education Plans or 504 Plans (Nicpon et al., 2011). Without such identification, students’ needs will go unrecognized and their academic promise will be compromised (Gilman et al., 2013).
Students may not be targeted for identification if they are progressing with adequate achievement from grade to grade (Nicpon et al., 2011). When this phenomenon occurs, the term masking is utilized. Masking occurs when a gift compensates for a disability or a disability lessens the impact of a gift. It becomes a question of students not being properly identified because they do not fail enough (Assouline, Nicpon, & Whiteman, 2010). When a learning disability occurs in gifted students, the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; American Psychiatric Association, 2013) states,
These individuals may be able to sustain apparently adequate academic functioning by using compensatory strategies, extraordinarily high effort, or support, until the learning demands or assessment procedures (e.g., timed tests) pose barriers to their demonstrating their learning or accomplishing required task. (p. 69)
The effect is that achievement is more likely to be average than to either be at a deficit level that would result in a student receiving needed special education interventions or at an advanced level that would lead to a student receiving needed gifted education interventions (Baum, 1990; Brody & Mills, 1997; McCoach, Kehle, Bray, & Siegle, 2001).
Cognitive and Achievement Profiles of Students With G&LD
To meet the academic needs of G&LD students, a thorough understanding of their strengths, weaknesses, and achievement profiles is needed. Lovett and Sparks (2013) conducted a meta-analysis encompassing 17 studies and 983 participants that examined the cognitive and achievement scores of G&LD students. They found that the mean Wechsler full-scale intelligence quotient (FSIQ) was 122.8, with the average scores on the Wechsler Performance Intelligence Quotient (PIQ) and Verbal Intelligence Quotient (VIQ) at 125.9 and 118.6, respectively (Wechsler, 2003). Furthermore, Lovett and Sparks reported achievement scores in reading, math, and written language for the Woodcock–Johnson achievement tests (Woodcock, McGrew, & Mather, 2001). Students with G&LD had a mean of 95.8 in Reading, 111.1 in Math, and 93.0 in Written Language. The majority of studies did not report subtest scores. However, Ferri, Gregg, and Heggoy (1997) studied G&LD college students and reported mean scores for the following Woodcock–Johnson achievement subtests: (a) Word Attack = 104.5, (b) Reading Comprehension = 103.3, (c) Spelling = 90.3, and (4) Math Calculation = 106.6 (Woodcock et al., 2001). It should be noted that across indices and subtests, the G&LD students’ scores remained in the Average range, supporting the theory of masking.
Strengths and Weaknesses of Students With G&LD
Previous research has provided insight into the strengths and weaknesses of G&LD learners. Higher order thinking skills emerge as a consistent area of strength. G&LD students display strong metacognitive skills (Hannah & Shore, 1995, 2008), advanced reasoning and problem-solving skills (Munro, 2002), and divergent and abstract thinking skills (Ferri et al., 1997; Wood & Estrada-Hernandez, 2009). In addition, Munro (2002) reported that students with G&LD particularly utilize inductive learning strategies to solve problems. G&LD students, in spite of the learning disability classification, have additionally demonstrated advanced, well-developed vocabularies (Nielsen, 2002; Wood & Estrada-Hernandez, 2009). Reis and McCoach (2002) and Winner (2000) also noted that G&LD students possessed superior spatial skills.
G&LD students exhibit a weakness in basic processing skills (Assouline et al., 2010; Nielsen, 2002; Wood & Estrada-Hernandez, 2009), leading to struggles with decoding and spelling (Mather & Jaffe, 2002). Several other potential deficits have been noted in such areas as receptive and expressive communication (Wood & Estrada-Hernandez, 2009), written language (Bireley, Languis, & Williamson, 1992), long- and short-term memory (Nielsen, 2002), and short-term auditory memory (Waldron & Saphire, 1990, 1992). Consequently, skills such as memorizing math facts may be affected although the student’s conceptual understanding progresses as expected (Mather & Jaffe, 2002).
Identification Criteria for Students With G&LD
Due to the novel profile of G&LD students, identification procedures have been highly variable with little empirical evidence to support the methodologies utilized (Lovett & Sparks, 2013). McCoach et al. (2001) emphasized that many factors contributed to the underachievement of gifted students; therefore, a discrepancy between cognitive ability and achievement should be accompanied by the evidence of a processing deficit along with a thorough examination of student performance in the classroom through the use of permanent products and curriculum-based measures. Nielsen (2002) recommended a thorough record evaluation along with (a) FSIQ ≥ 120, (b) auditory and/or visual processing deficits, and (c) other giftedness indicators (e.g., Scales for Rating the Behavioral Characteristics of Superior Students, Third Edition [Renzulli et al., 2010], Torrance Test of Creative Thinking [Torrance, 2008], etc.). Determinations of giftedness in Lovett and Sparks’s (2013) meta-analysis included the sole use of FSIQ (Al-Hroub & Whitebread, 2008), the use of FSIQ, or VIQ, or PIQ (Baum & Owen, 1988; Hannah & Shore, 2008), or the use of the general ability index (GAI; Assouline et al., 2010). A cognitive/achievement discrepancy model was noted as a selection criterion in nearly half of the studies, with 1 to 2 standard deviations below cognitive ability being the norm. Lovett and Sparks concluded the analysis with the recommendation of the following identification criteria: FSIQ greater than or equal to a standard score of 120 along with achievement scores at a maximum of 85 to 90 demonstrating functioning in the lower quartile of scores. Although no absolute standard has been established, at minimum, a battery of assessments, including an individual intelligence test, multiple measures of cognitive processing, and a full achievement battery, should be given. The possibility of the learning disability affecting a student’s performance on tests necessitates comprehensive, sophisticated measures (Bell, Taylor, McCallum, Coles, & Hayes, 2015; Brody & Mills, 1997) and methods of determining the discrepancy between student potential and their actual performance (Assouline & Whiteman, 2011; Reis, Baum, & Burke, 2014) through comprehensive, individualized approaches toward identification (Nicpon et al., 2011).
Theoretical Framework: Cattell–Horn–Carroll (CHC) Theory of Intelligence
The integrated model of the CHC theory of intelligence includes nine broad abilities: (a) fluid reasoning (Gf), (b) quantitative knowledge (Gq), (c) crystallized knowledge (Gc), (d) short-term retrieval (Gsm), (e) visual processing (Gv), (f) auditory processing (Ga), (g) long-term storage and retrieval (Glr), (h) processing speed (Gs), and (i) reading and writing Ability (Grw; Ortiz, 2015). Two of these abilities instinctively relate to academic achievement: reading and writing (Grw) and quantitative knowledge (Gq; S. B. Kaufman, Reynolds, Liu, Kaufman, & McGrew, 2012). Many of the other broad abilities can play an indirect role in academic performance. For example, poor short-term memory (Gwm) may affect a student’s ability to learn math facts, poor auditory processing (Ga) may inhibit decoding skills, and crystallized intelligence (Gc) may demonstrate acquired knowledge for vocabulary and facts (Mather & Jaffe, 2002). In addition, higher order thinking skills (Sternberg, 1986) like inductive and deductive reasoning, divergent thinking, and metacognition have been associated with some Glr and Grw abilities (Avitia & Kaufman, 2014; J. Kaufman, Kaufman, & Lichtenberger, 2011).
Rationale
As Assouline and Whiteman (2011) noted, researchers have determined that G&LD students do indeed exist, even though they appear average in the typical classroom. However, to meet their needs, practitioners need a profile of the students’ strengths and weaknesses as revealed through a comprehensive evaluation to help these students develop their particular strengths (Gilman et al., 2013). It is important for students to learn to engage in the struggle of learning before they get to a point in their learning when masking can no longer help them maintain average achievement. This crisis of realism may occur at a higher grade, potentially secondary or postsecondary education, when they will not have opportunities for support and strategies (Baum, 1990). Ultimately, G&LD learners benefit from understanding their strengths and deficits (Nicpon, Assouline, & Fosenburg, 2015; Willard-Holt, Weber, Morrison, & Horgan, 2013) that will empower them to be effective, engaged learners.
Because a comprehensive assessment should include an achievement measure, this study examines the subtests and error factors in the Kaufman Test of Educational Achievement–Third Edition (KTEA-3; A. S. Kaufman & Kaufman, 2014) for strength and weakness patterns that G&LD students exhibit in terms of higher and lower level thinking skills. Based on the above stated reasoning, the following research questions were addressed by this study:
Research Question 1: Do the G&LD student subgroups’ mean scores differ on KTEA-3 subtests and factors?
Research Question 2: Do G&LD students perform similarly to students with giftedness on KTEA-3 subtests that require higher level processing demands?
Research Question 3: Do G&LD students perform similarly to students with a learning disability on KTEA-3 subtests that require lower level processing demands?
Method
Participants
Participants in this study were students from the normative sample of the KTEA-3 (A. S. Kaufman & Kaufman, 2014) who appeared to exhibit characteristics of intellectual giftedness alongside a specific learning disability (SLD; G&LD). This categorization was determined by the coexistence of high crystallized intelligence using the Oral Language Index of the KTEA-3 and a significant discrepancy in reading, writing, and/or math. Control groups were selected from the SLD (SLD-Math and SLD-Reading/Writing) clinical validity sample and the academically gifted (GT) clinical validity sample. Demographic data for these samples are provided in the KTEA-3 Technical and Interpretive Manual (A. S. Kaufman, Kaufman, & Breaux, 2014). As stated in the manual, SLD is defined such that it
is generally consistent with DSM–5 classification, but these groups are also referred to as specific learning disabilities by the IDEIA and various professional organizations, and in the research literature. For the purpose of evaluating the results of the KTEA–3 special group studies, the terms specific learning disorder and specific learning disability may be considered synonymous. (A. S. Kaufman et al., 2014, p. 75)
Academically gifted may be defined as students who exhibit cognitive and academic abilities in the top 2% of the population. About half of each normative and clinical sample were tested on KTEA-3 Form A and half on KTEA-3 Form B.
Sample with G&LD
The G&LD sample (n = 196) included 91 males and 105 females in Grades K-12 (M grade = 6.5; SD = 3.4) who ranged in age from 5 to 18 (M age = 11.2; SD = 3.4). Table 1 displays demographic data on ethnicity, parent education level, and geographical region of participants. The G&LD sample consisted of four subgroups: (a) 32.1% (n = 63) Reading Only disability, (b) 21.9% (n = 43) Writing (with or without a reading) disability, (c) 20.4% (n = 40) Math (with or without a reading and/or writing) disability, and (d) 25.5% (n = 50) Reading/Writing/Math disability.
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Table 1. Demographics for Experimental and Control Samples.
Academically gifted control group
The academically gifted group (n = 69) included 37 males and 32 females in Grades K-12 (M grade = 5.2; SD = 3.3) who ranged in age from 5 to 17 (M age = 10.3; SD = 3.4). Table 1 displays demographic data on ethnicity, parent education level, and geographic region of participants; the academically gifted group was predominately White and had a disproportionate number of Asian participants with fewer Black, Hispanic, and Other participants. Criteria for participation in the academically gifted sample required that two of the following three criteria be met:
(1) obtained standard scores of 130 or above on an individually administered test of cognitive ability, (2) were taking advanced placement classes or were enrolled in a school program for academically gifted students, and/or (3) obtained standard scores of 130 or above, or the equivalent percentile, on a standardized achievement test. (A. S. Kaufman et al., 2014, p. 87)
SLD control group
The SLD group (n = 90) included 45 males and 45 females in Grades 1-12 (M grade = 6.5; SD = 3.1) who ranged in age from 6 to 18 (M age = 12.1; SD = 3.2). Table 1 displays demographic data on ethnicity, parent education level, and geographic region of participants. Inclusion in the SLD group required the following: (a) 15 point discrepancy between reading, written expression or math standard score, and cognitive ability and (b) intra-achievement discrepancy.
Just over half the students in this group were identified as having a specific learning disorder in both reading and written expression, about 40% were identified in the area of reading only, and about 5% (3 students) were identified in the area of written expression only. (A. S. Kaufman et al., 2014, p. 77)
In addition, “[a]pproximately 15% of the Math Disorder sample (7 students) was identified as having a specific learning disorder in both math and reading” (A. S. Kaufman et al., 2014, p. 79).
Measure
The KTEA-3, an individually administered achievement test for use with students between the ages of 4 and 25, assesses reading, mathematics, written language, and oral language skills, utilizing 19 total subtests (A. S. Kaufman & Kaufman, 2014). Standard scores, with a mean of 100 and standard deviation of 15, are derived from subtest raw scores. The split-half reliability and alternate form reliability of the primary composites, Reading, Math, Written Language, Oral Language, and Academic Skills Battery, range from good (.80) to excellent (.90). The KTEA-3 is also well correlated with other measures of achievement (.70-.86). Furthermore, the intercorrelation between the subtests and composites are in the .70s and .80s, except for the Oral Language and Oral Fluency composites that are in the .40s and .50s. The technical manual reported that “lower correlations are expected because oral language and language processes establish a foundation for building academic skills, but they are not areas of academic achievement in and of themselves” (A. S. Kaufman & Kaufman, 2014, p. 50).
The KTEA-3 defines the processing demands for the reading, math, writing, and Language Process domains. Within the Reading domain, Reading Comprehension, Reading Vocabulary, and Silent Reading Fluency require a higher level processing demand, whereas Letter & Word Recognition, Nonsense Word Decoding, Word Recognition Fluency, and Decoding Fluency necessitate a lower level processing demand. In the Math domain, Match Concepts and Applications required higher level processing demand, whereas Math Computation and Math Fluency required a lower level processing demand. Within the writing domain, Spelling requires a lower level processing demand than Written Expression and Writing Fluency. Finally, Phonological Processing necessitates a higher level processing demand (A. S. Kaufman & Kaufman, 2014).
Procedure
The G&LD sample was selected from the KTEA-3 normative sample. Criteria for participation in the G&LD sample included either an Oral Language Index (oli) standard score ≥ 120 or a mean Listening Comprehension/Oral Expression (lcoe) standard score ≥ 120 plus a minimum 1.5 standard deviation (SD; 22.5 points) discrepancy on a reading, math, or writing index or two math, two writing, or three or more reading subtests. Once the sample was chosen, demographic characteristics were examined for gender, ethnicity, parent education level, and geographic region. Both control groups (GT and SLD) were chosen from the KTEA-3 clinical validity groups; participants were matched on demographic characteristics.
Analysis
A multi-step process was used to investigate the differences between students who are identified as G&LD and matched controlled samples of students identified as gifted and students identified having a speech/language disorder and their corresponding KTEA-3 error scores. The first analytical step in this process was the derivation of factor scores.
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 category of error on a given subtest, students received a grade-level, normative performance label of weakness, average, or strength based on a comparison of a student’s total errors with the average number of errors made by individuals in the KTEA-3 normative sample (A. S. Kaufman et al., 2014). This performance label is called the skill status. Based on this error analysis system, students received multiple skill status error scores within each subtest. To facilitate the use of these skill status error scores in further analyses, exploratory factor analysis and principal components analysis were used to create a reduced error score variable set (see Choi et al., 2017; Hatcher et al., 2017; O’Brien et al., 2017).
To create the factor scores, polychoric correlation matrices were generated for each subtest. The exceptions were Reading and Listening Comprehension and Oral and Written Expression subtests. Because each of these subtests have a smaller number of error scores that, in general, are the same across the subtest type (comprehension or expression), one polychoric correlation matrix was generated for each subtest type (comprehension or expression). An exploratory factor analysis using unweighted least square extraction was conducted for each of the subtests excluding comprehension, expression, and phonological processing. Because the Comprehension, Expression, and Phonological Processing subtests include a small number of error scores, principal components analysis was used to extract the factors for these subtests.
Regardless of the factor extraction technique, a combination of parallel analysis (PA; Horn, 1965), a visual inspection of the scree plot (Cattell, 1966), and content review of the factor structure were used to determine the number of factors to extract. For the subtests related to the current study, four factors were extracted for the comprehension and expression subtests, three factors for Letter & Word Recognition, and two factors for Nonsense Word Decoding, Spelling and Phonological Processing subtest. R version 3.2.3 was used to generate Bartlett factor scores (DiStefano, Zhu, & Mîndrilă, 2009) for each of the extracted factors.
The next analytic step involved is the identification of a subset of 196 students who were identified as G&LD. Students were classified as G&LD based on an Oral Language Index or the average of Listening Comprehension + Oral Expression ≥ 120 plus a 1½ SD, or greater discrepancy on a reading, math, or writing index or two math, two writing, or three or more reading subtests. In addition, matched control samples of students identified as gifted (GT) and students identified having a SLD. The matching process considered the student’s age, grade, sex, ethnicity, and parent education level.
The final analytic step was to investigate whether the errors made on the KTEA-3 tests varied between the control groups and the G&LD group. To test this hypothesis, several one-way MANCOVAs were conducted with subtest error factor scores as dependent variables and grouping variable (G&LD, GT, or SLD) as the independent variable. To correct for the potential impact of student development, age was used as a covariate. Prior to conducting the analyses, to verify that age should be considered a covariate, the relationship between age and the dependent variables was studied. Age did not have a statistically significant relationship with the error factor scores for reading comprehension and written expression. Looking at the assumption of homogeneity of within group regression slopes, three subtests (Listening Comprehension, Oral Expression, and Math Computation) had statistically significant age and group interaction effects. Therefore, for these subtests, age was not utilized as a covariate, and one-way MANOVAs were conducted instead. 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 test was statistically significant for all subtests. 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 (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 −2.6.
Results
This research compared the G&LD students with the GT and SLD control groups on achievement subtest scores and error factor scores. The gifted group’s achievement scores were significantly higher than that of the SLD’s. In addition, the G&LD scores were lower than the GT group’s scores, but not significantly. The differences between the control groups’ error factor scores were also significant across all error factors.
As seen in Tables 2 and 3, the G&LD group’s subtest and factor scores fell between the GT and SLD groups. In particular, G&LD–Writing (with or without Reading) students scored in the Above Average range with a mean of 116.2 (10.4) on the math composite index. In addition, the G&LD–Reading and G&LD–Math (with or without Reading or Writing) students also performed in the Above Average range with a mean of 117.1 (10.2) and 113.0 (10.0), respectively, on the Written Language composite index. The G&LD–Math (with or without Reading or Writing) students also had Above Average scores in all areas of reading with a mean of 117.8 (12.4). Of the four subgroups, the G&LD–Reading, Writing, and Math subgroup scored lower than the other subgroups across composite indices (M = 103.5) and as seen in Table 4 across all factors. Of interest, although the disparities in the reading subtests were not as dramatically different across the G&LD subgroups, the G&LD–Writing (with or without Reading) students had lower mean score 100.5 (12.9) on the lower processing demand subtest, Decoding Fluency.
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Table 2. Group Means and Standard Deviations on KTEA-3 Error Factors.
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Table 3. MANCOVA Group Means and Standard Deviations on Error Factors for Each Subtest.
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Table 4. MANOVA Group Means and Standard Deviations on Error Factors for Each Subtest.
Overall, there were statistically significant differences across the three groups and across all subtest error pattern scores as depicted in Table 5. To analyze these error patterns in terms of our research questions, Table 6 depicted the results of pairwise comparisons comparing the mean of the G&LD sample across errors with the mean error factor scores of the GT group as well as the SLD group. The Bonferroni correction was used to control for family-wise error for each subtests’ error factors. The researchers expected greater patterns of similarities and differences between the G&LD group and the comparison groups. Overall, the G&LD sample outperformed the SLD comparison group, as almost all of the pairwise differences were statistically significant. Silent letters in Nonsense Word Decoding and addition in Math Computation were the two exceptions. Similarly, across most of the error factors, few to no statistically significant differences existed between the G&LD group and the GT group. The exceptions were the silent letter factor of the Nonsense Word Decoding subtest, the basic computation factor of the Math Concepts & Applications subtest and the addition factor of the Math Computation subtest, in which the G&LD sample scored significantly lower than the GT group. Finally, Table 7 depicts statistically significant differences across the four G&LD subgroups: (a) G&LD–Reading; (b) G&LD–Writing (with or without reading); (c) G&LD–Math (with or without reading or writing); and (d) G&LD–All. The results of pairwise comparisons of the mean of the G&LD subgroups across errors with the mean factor scores of the GT and SLD groups are displayed in Table 7. As done previously, Bonferroni pairwise corrections were utilized to control for family-wise errors. The analysis by subgroup revealed that the G&LD–All subgroup solely scored significantly lower on the addition portion of the Math Concepts & Applications subtests.
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Table 5. Results Summary by KTEA-3 Subtests and Error Factors.
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Table 6. Bonferroni Pairwise Comparisons/Difference Between Means.
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Table 7. Bonferroni Pairwise Comparisons of Gifted Learning Disabled Reading, Writing, and Math Subgroups.
Confirming the researchers’ hypothesis for the first research question, the G&LD sample did not score statistically differently than the GT sample on the following higher level processing demand subtests and factors: (a) Phonological Processing, (b) Reading Comprehension, Factor 2–figurative/narrative, (c) Math Concepts, Factor 3–complex problems, and (d) Written Expression. The researchers’ second hypothesis, that the G&LD sample would score similarly to the SLD group on the lower level processing demand subtests and factors, was confirmed on two subtest factors: silent letters in Nonsense Word Decoding and addition in Math Computation. In addition, it was partially confirmed on the basic computation factor of Math Concepts & Applications, because the G&LD group scored statistically differently than the GT group, but was also significantly different than the SLD group.
Discussion
This study examined the strength and weakness patterns of students who exhibit giftedness along with a learning disability in reading, math, writing, or any combination of the three. In response to the first research question, the mean composite and subtest scores on the KTEA-3 for the GT group were in the Above Average to High range, while the SLD group’s mean subtest scores were all in the Below Average range. As hypothesized, the means scores of the G&LD sample fell in the Average range between the two groups. However, it should be noted that variability within the subgroups demonstrated a weakness in the specific area of learning disability with Above Average scores, similar to GT students, in the other academic areas. In particular, the G&LD–Reading only group was Above Average in both writing and math, the G&LD–Writing (with or without a reading disability) were Above Average in Math, and the G&LD–Math (with or without a reading or writing disability) were Above Average in Reading, Decoding, and Reading Fluency. The G&LD–Writing (with or without reading) group’s Decoding Fluency score was lower than all other reading scores, indicating a potential source of their reading difficulties given that a person’s specific fluency skills can combine to form higher level abilities (Mercer, Mercer, & Pullen, 2011). Therefore, unlike students with SLD who displayed average performance across academic subjects, the G&LD students had specific areas of weakness with one exception; the G&LD–All group, as one would expect, exhibited deficits across all three academic domains.
Research Question 2 examined whether or not the G&LD performed similar to GT students on higher level processing demand tasks. When comparing the G&LD with the GT control, the G&LD sample did not score statistically differently on most of the higher level processing demand subtests and factors than the GT sample, thereby confirming the researchers’ hypothesis. In particular, the sample performed similar to the GT group across phonological processing and reading comprehension, both higher level reading tasks. It should be noted that the G&LD–Reading and G&LD–Reading, Writing, and Math groups’ mean scores on literal reading comprehension were lower than their figurative comprehension scores. Although significantly above the SLD group, the score differences may be explained by the G&LD students’ strengths in advanced reasoning skills, problem-solving skills, and divergent/abstract thinking skills, all aspects of fluid reasoning (Ferri et al., 1997; Munro, 2002; Wood & Estrada-Hernandez, 2009). LaRusso et al. (2015) found that students’ complex reasoning predicted their ability to exhibit deep comprehension, demonstrating that high-level figurative processing tasks may be enhanced by a strength in reasoning skills.
The G&LD group also demonstrated similar strengths to the GT sample across the higher level processing demand Written Expression tasks and complex Math Concepts & Applications tasks. Once again, the G&LD group’s apparent strength in fluid reasoning (Gf) likely provided them with the ability to draw inferences and transfer and generalize information in writing tasks. Furthermore, Gf allowed the G&LD group to think conceptually and solve abstract mathematical problems (Mather & Jaffe, 2002). In contrast, the G&LD sample—in particular the G&LD–All—demonstrated scores similar to the SLD group on the basic computation portion of the Math Concepts & Applications subtests. Although considered a higher level processing demand subtest, problems that emphasize basic computation skills require a lower level of processing. When G&LD students display a weakness with Gsm (Nielsen, 2002), they often experience difficulties in memorizing mathematics facts (Mather & Jaffe, 2002). As such, one may posit that the G&LD students’ weakness in basic math facts may affect the student’s ability to solve basic conceptual mathematics problems.
Research Question 3 examined whether or not the G&LD sample was statistically similar to the SLD sample on lower level processing demand tasks. The G&LD sample was statistically different from the SLD group on all subtests and factors except for decoding nonsense words with silent letters and computing addition math problems. Both these tasks require lower level processing demands. As mentioned above, G&LD students often struggle with short-term memory, thereby affecting their ability to acquire automaticity with math facts (Mather & Jaffe, 2002; Nielsen, 2002). Furthermore, because nonsense word decoding tasks require students to evaluate letter patterns and is predictive of reading problems (Chard, Simmons, & Kame’enui, 1998), G&LD students with a weakness in reading will likely exhibit a weakness in this basic skill. The remaining lower level processing demand subtests means showed a significant difference between the G&LD sample, thereby contradicting the researchers’ hypothesis that the sample would score similarly.
Limitations
There are several limitations to this study, including violation of MANOVA assumptions, lack of reliability estimates, and restrictions on group membership criteria that were influenced by sample size needs. Although MANOVA results were shared on secondary subgroup analyses, they violated the assumption of homogeneity of covariance. Results should therefore be considered with that violation in mind.
Given the type of research conducted, it was not possible to obtain reliability estimates such as test–retest reliability scores. However, that limitation is a constant in this type of research and not addressable in future work.
Finally, and most importantly for future research, the G&LD sample selection process had limitations. First, giftedness in the G&LD sample was approximated by using a measure of crystallized intelligence as a proxy for intelligence. This choice does not encompass the rich assessment requirements for a gifted diagnosis, but is the best approximation tool that was available in this study. Future research should seek access to additional information that could better delineate true giftedness, such as formal intelligence testing and subjective measures. The second limitation of the sample selection for the G&LD sample was that the original requirement of a 2 standard deviation difference between the proxy intelligence measure and reading, writing, and/or math achievement scores yielded a sample size that was too small for analysis purposes. The requirement was therefore altered to a 1.5 standard deviation difference. This modification allowed for a sufficient sample size, but affected the generalizability of the sample to the true G&LD population. Future researchers should be mindful to use a 2 standard deviation gap between a measure of intelligence and reading, writing, and/or math achievement scores to optimize generalizability of the results.
The results of this study strongly support the use of error and profile analysis to properly diagnose students with a G&LD profile. Using full-scale or composite scores potentially masks the true areas of strength and challenge of these learners. Failure to analyze the unique profile of these learners leaves them prone to academic disengagement. It is vital that diagnosticians complete comprehensive evaluations of these learners to ensure advanced content and accelerated pace in areas of strength, with evidence-based interventions to remediate challenges. Teachers and diagnosticians should also recognize that G&LD students may have challenges that come with low-level processing demands, but this does not negate their gifted strengths. Societally, we tend to expect gifted students to be perfect and use flaws in learning to negate a gifted diagnosis. As such, skilled diagnosticians and teachers should be mindful that G&LD students may exhibit challenges with seemingly simple achievement areas but that these challenges do not quash the gifted aspects of these learners. Rather, these areas should be addressed with strong interventions while the gifted, higher level functioning areas should be cultivated appropriately.
Acknowledgements
The authors thank NCS Pearson for providing the standardization and validation data for the Kaufman Tests of Educational Achievement–Third edition (KTEA-3). Copyrights by NCS Pearson, Inc. used with permission. They also 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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