Abstract

This study investigated the performance of adolescents and young adults with Attention Deficit Hyperactivity Disorder (ADHD), Reading Disorder (RD), and ADHD/RD on measures of alphanumeric and nonalphanumeric naming speed and the relationship between naming speed and academic achievement. The sample (N = 203) included students aged 17–28 years diagnosed with ADHD (n = 83), RD (n = 71), or ADHD/RD (n = 49). Individuals with ADHD performed significantly faster on measures of alphanumeric naming compared with RD and comorbid groups and, within group, demonstrated significantly quicker naming of letters/digits compared with colors/objects. Both alphanumeric rapid naming scores and processing speed scores variably predicted academic achievement scores across groups, whereas nonalphanumeric rapid naming only predicted reading comprehension scores within the ADHD group. Results support findings that older individuals with ADHD show relative weakness in rapid naming of objects and colors. Implications of these findings in regard to assessment of older individuals for ADHD are discussed.

Introduction

Rapid naming abilities have been increasingly identified as weaker among children with Attention Deficit Hyperactivity Disorder (ADHD) relative to children without ADHD (e.g., Bidwell, Willcutt, DeFries, & Pennington, 2007; Tannock, Martinussen, & Frijters, 2000). Historically, deficits in speeded naming of stimuli have been primarily associated with Reading Disorders (RDs) (Hoskyn & Swanson, 2000; Moll, Loff, & Snowling, 2013); however, considering the high frequency with which ADHD and RD co-occur (DuPaul, Gormley, & Laracy, 2013), researchers have examined rapid naming difficulties as a potential shared underlying cognitive weakness that may present across those individuals with ADHD, RD, or a comorbid presentation of the two disorders (e.g., Rucklidge & Tannock, 2002; Weiler, Bernstein, Bellinger, & Waber, 2000).

Deficits in rapid naming have been predominately explored in the context of reading-related difficulties, as rapid naming skills have been frequently associated with reading performance (Denckla & Cutting, 1999; Georgiou, Parrila, Cui, & Papadopoulos, 2013; Kibby, Lee, & Dyer, 2014). Traditionally, two models explaining the role of rapid naming in reading development have been outlined. One model subsumes rapid naming under the general category of phonological processing, suggesting that rapid naming abilities indicate phonological retrieval efficiency (Torgesen & Davis, 1996). In contrast, a secondary argument delineates that rapid naming characterizes processes that are largely independent of phonological processing and that these processes contribute distinctively to the development of reading (Denckla & Cutting, 1999; Wolf, Bowers, & Biddle, 2000). Supporters of this model posit that phonological processing and rapid naming abilities can be impaired either singly or in combination with one another. Furthermore, individuals who exhibit significant difficulties in both basic phonological awareness and rapid naming are thought to demonstrate the most severe deficits in reading, a concept known as the “double-deficit hypothesis” (Wolf & Bowers, 1999).

Supporters of the position that rapid naming is distinctive from other aspects of phonological processing discuss that rapid naming requires unique cognitive demands and is associated with numerous cognitive subprocesses beyond linguistic processing. Wolf and Bowers (1999) detailed a model that described the integrated attentional, visual, lexical, and mental representation demands involved in naming visually presented stimuli. Notably, Wolf and Bowers (1999) pointed out that, in particular, continuous rapid naming tasks, as opposed to discrete-trial naming tasks, place high cognitive demand on the examinee, requiring retrieval of phonological information, selective attention, and visual scanning and filtering. The complex nature of continuous rapid naming tasks highlights why individuals with attention and executive functioning difficulties, such as those with ADHD (Barkley, 1997), might exhibit difficulties on these tasks.

Empirical work has shown that children with ADHD do, in fact, demonstrate certain weaknesses in rapid naming abilities (Ghelani, Sidhu, Jain, & Tannock, 2004). Compared with children without ADHD or learning disabilities, children with ADHD have been shown to demonstrate slower average rapid naming overall (i.e., across naming letters, digits, objects, colors) (Bidwell et al., 2007). However, discrepant findings have occurred when researchers have investigated how children with ADHD perform on specific rapid naming tasks. Although studies have demonstrated that children with ADHD perform significantly worse than controls on rapid naming of colors and objects (Ghelani et al., 2004; Tannock et al., 2000), even after accounting for differences in intellectual ability (Carte, Nigg, & Hinshaw, 1996; Nigg, Hinshaw, Carte, & Treuting, 1998), scores from this group on alphanumeric naming tasks are often similar to controls and higher than children with RD (Ackerman & Dykman, 1993; Carte et al., 1996; Tannock et al., 2000). In contrast, on nonalphanumeric naming tasks, ADHD groups exhibit weaknesses comparable with children with RD, with both groups performing significantly slower than control groups (Semrud-Clikeman, Guy, Griffin, & Hynd, 2000).

Importantly, findings that children with ADHD exhibit relative difficulties in their naming of colors and objects compared with letter and digit naming tasks likely speaks to the specific cognitive requirements of nonalphanumeric naming tasks that might be particularly problematic for children with attention-related difficulties. Naming of colors and objects is said to require more effort and decision making due to the need to process more semantically complex information (Tannock, Banaschewski, & Gold, 2006). Specifically, whereas letters and digits only have one name and are characterized by distinct categories, colors and objects may have more than one possible name and may be associated with overlapping categories (Tannock et al., 2000). Furthermore, Tannock and colleagues (2000) showed that provision of psychostimulant medication, effective in improving effortful processes, to children with ADHD significantly improved their naming of colors but did not have a significant effect on their naming of letters or digits.

Further investigations of rapid naming abilities among children with ADHD have demonstrated the association between speeded naming skills and academic performance. Among both children and young adolescents with ADHD, naming speed scores and pause times have been found to be significantly correlated with reading fluency and, in some cases, with reading comprehension (Li et al., 2009). Additionally, Ackerman and Dykman (1993) demonstrated that within child groups of individuals with ADHD, RD, or both disorders, rapid naming scores were significantly predictive of reading of both real and nonsense words. Notably, however, though individuals with ADHD often exhibit slower processing speed compared with typically developing children (e.g., Weiler et al., 2000), researchers have not parceled out whether reading difficulties within this group can be attributed to rapid naming weaknesses beyond slower processing speed abilities. This is particularly relevant to investigate as processing and rapid naming speed abilities of children are significantly positively correlated with each other as well as with reading skills and reading comprehension (Kail & Hall, 1994).

Despite the growing body of literature on rapid naming in children with ADHD, this work has remained focused on child populations. Cognitive weaknesses of young adults with ADHD have been minimally explored, despite increasing evidence that neuropsychological difficulties often endure into adulthood (Barkley, Murphy, & Fischer, 2010). Research has suggested that in those cases where childhood ADHD does persist, continuance of the disorder is likely indicative of greater severity, suggesting that adults with ADHD may show varied cognitive profiles compared with their child counterparts (Kessler et al., 2005). Additionally, research suggests that the gap in cognitive functioning between individuals with ADHD and age-matched controls varies at different levels of maturation and development (Marx et al., 2010). Therefore, research on the cognitive deficits of children with ADHD cannot necessarily be generalized to the adult population. The investigation of cognitive indicators of adult ADHD is particularly relevant as there is an increasing number of students with ADHD pursuing postsecondary education (Government Accountability Office, 2009), many of whom will pursue academic accommodations based on disability eligibility and seek initial psychological evaluations or reevaluations for ADHD diagnoses to do so.

With the increasing prevalence of older students pursuing disability eligibility based on difficulties associated with ADHD, it would be prudent for clinicians evaluating these individuals to be familiar with diverse objective indicators of the disorder. Though psychologists' assessment practices vary significantly, one recent study examining psychological reports submitted as documentation for postsecondary students pursuing ADHD disability eligibility showed that evaluators relied heavily on interviews, rating scales, and behavioral observations during testing to inform their diagnoses (Nelson, Whipple, Lindstrom, & Foels, 2014). These evaluation procedures can be subject to malingering, or feigning symptoms, a concern that is present when evaluating postsecondary students who may try to gain advantages through academic accommodations (Musso & Gouvier, 2014). Therefore, it is important to investigate objective measures that may serve to elucidate underlying weaknesses in functioning that are known to be associated with ADHD. Though objective psychological testing (e.g., intelligence tests) was used by the majority of psychologists within the study by Nelson and colleagues (2014), cognitive-linguistic processing was assessed in only approximately half of cases and primarily focused on visual-motor functioning. Considering no studies to date have investigated rapid naming among postsecondary students with ADHD, it is likely that psychologists do not currently recognize that certain rapid naming weaknesses might be common among this population and serve as a potential indicator of ADHD.

The primary purpose of our study was to examine rapid naming among students with ADHD, RD, and comorbid ADHD and RD who were attending or preparing to attend a postsecondary institution. Our first research question asked whether the three clinical groups (ADHD, RD, and ADHD + RD) being examined demonstrated significant differences in performance across measures of rapid naming. Based on previous empirical work (e.g., Bental & Tirosh, 2007), we hypothesized that the ADHD group would perform significantly faster on alphanumeric rapid naming tasks than the RD group, and both the ADHD and RD groups would perform significantly faster than the comorbid group on this task. In contrast, on measures of color and object naming, we hypothesized that the three groups would demonstrate similar performance. Secondly, considering findings that children with ADHD display varied performance on measures of alphanumeric versus nonalphanumeric rapid naming, we next investigated whether scores on composite measures of these two types of rapid naming tasks differed significantly within each clinical group. Though previous research has not specifically examined differences across varied rapid naming tasks within adult ADHD groups, we believed that the ADHD group would earn significantly higher scores on letter and digit naming tasks compared with object and color naming tasks, as this pattern has been repeatedly demonstrated among children with ADHD. Our final research question asked whether, within each clinical group, measures of alphanumeric rapid naming and color/object rapid naming would significantly predict performance on reading measures as well as academic fluency measures above and beyond general intellectual and processing speed abilities. As both rapid naming composites have been previously associated with reading fluency (Pham, Fine, & Semrud-Clikeman, 2011), we believed that both alphanumeric rapid naming and object/color naming would significantly predict reading performance, including reading fluency, basic reading skills, and reading comprehension. Within previous empirical investigations of the association between rapid naming and academic achievement, nonreading domains have been largely ignored. However, considering that the processes influencing rapid naming also likely would affect fluency within other academic areas, we hypothesized that rapid naming scores across the two composites would also significantly predict math and writing fluency scores.

Method

Participants

This study used data from 203 adolescents and young adults (52% male) who had received comprehensive psychological evaluations at a university-based clinic over a 4-year period. Individuals had primarily sought evaluations through this clinic for the purpose of postsecondary disability eligibility determination. All evaluations were conducted by either a licensed doctoral-level psychologist or a master's-level clinician under the supervision of a doctoral-level licensed psychologist. Evaluations, on average, lasted between 8 and 10 hr and consisted of the administration of general intellectual ability, academic achievement, and language measures as well as measures of social–emotional and behavioral functioning, clinical interviews, and behavioral observations. Based on suggested best practice guidelines (Tobin, Schneider, Reck, & Landau, 2008), a multimethod, multi-informant approach was used in the assessment of ADHD. Of note, clinicians developed assessment batteries based on the presenting concerns of each individual, contributing to some variation in the specific instruments utilized within assessment of each domain.

For inclusion in the study, participants must have received diagnoses of ADHD, RD, or both ADHD and RD based on clinical judgment of the evaluating clinician considering criteria outlined by the DSM-IV, RD criteria outlined by the University System of Georgia (see http://www.usg.edu/academic_affairs_handbook for a detailed description), and qualifying criteria set by the Americans with Disabilities Act and its amendments (ADA, 2010). Additionally, participants must have been administered both the Rapid Naming and Alternate Rapid Naming composites of the Comprehensive Test of Phonological Processing (CTOPP) as the composite scores from this assessment were our primary variables of interest. Participants were excluded if there was a comorbid severe psychopathology (e.g., autism spectrum disorder) or if an intellectual disability was present as the cognitive deficits associated with these difficulties would likely significantly influence performance on rapid naming and achievement measures. However, participants with less severe, more commonly occurring comorbid disorders (e.g., mood disorders) were permitted to participate. In order to examine those students who were within a typical age range for postsecondary education rather than nontraditional students, an age range of 17–28 was designated. The final sample sizes for the three clinical groups were as follows: n = 83 for the ADHD group, n = 71 for the RD group, and n = 49 for the ADHD/RD group.

Demographic Information

Average age was 21.4 years (SD = 2.91). The majority of participants were enrolled in postsecondary education at the time of the evaluation (83.7%); others were either enrolled in high school or had recently graduated high school and were preparing to attend postsecondary institutions (8.9%), were college graduates (3.9%), or did not indicate their level of education (2.5%). The composition of participants by ethnicity was as follows: 86.7% Caucasian, 7.4% African American, 2.5% Hispanic, 2.5% Asian, and 1% multiracial/other.

Instruments

General intellectual ability

General intellectual ability was measured through administration of the Wechsler Adult Intelligence Scale, Fourth Edition (WAIS-IV; Wechsler, 2008a). For the purpose of this study, the General Ability Index (GAI) score was used to control for general intellectual ability. The GAI was used instead of the WAIS-IV Full Scale IQ because the GAI is less sensitive to the effect of processing speed and working memory difficulties, which are common among individuals with neuropsychological problems such as ADHD (Wechsler, 2008b). The GAI is composed of the six subtests that form the Verbal Comprehension Index (VCI) and Perceptual Reasoning Index (PRI; i.e., Similarities, Vocabulary, Information, Block Design, Matrix Reasoning, Visual Puzzles). For the VCI and PRI, stability coefficients for the standardization sample, as reported by the authors, were 0.96 and 0.87, respectively. Moderate to strong correlations between the VCI and PRI scores and composite scores for reading, mathematics, and written and oral language on the Wechsler Individual Achievement Test, Second Edition (WIAT-II) were also demonstrated among a sample of 16- to 19-year olds (Wechsler, 2008b).

Processing speed

The Processing Speed Index (PSI) from the WAIS-IV was used as a measure of overall processing speed ability. The PSI is composed of the Coding and Symbol Search subtests. These subtests assess how quickly individuals can perform basic, clerical tasks. Specifically, individuals are given 120 s on each task to either draw symbols underneath numbers based on how they correspond in a given key (Coding) or to indicate whether either of two target symbols matches any of a series of five symbols presented immediately to the left (Symbol Search). The reliability coefficient for PSI for 16- to 29-year olds within the standardization sample was 0.87. As an indicator of validity, within a sample of 16- to 89-year olds, PSI scores were strongly correlated with measures of visual-motor speed (i.e., Trail-Making Test from the Delis–Kaplan Executive Function System [DKEFS]) and moderately correlated with verbal fluency measures (i.e., Verbal Fluency subtest of the DKEFS; Wechsler, 2008b).

Naming speed

Naming speed abilities were assessed through administration of the CTOPP (Wagner, Torgeson, & Rashotte, 1999a). The Rapid Naming and Alternate Rapid Naming Composites of the CTOPP include four subtests in which individuals are presented with stimuli and required to name the stimuli as quickly as possible. Each subtest is composed of 72 randomly arranged stimulus items (either digits, letters, colors, or objects), which are presented evenly across two trials, one page each. An individual's score for each subtest is based on the amount of time it takes to name all the items. The Rapid Naming Composite Score (alphanumeric rapid naming) is based on the scores for the Rapid Digit Naming and Rapid Letter Naming subtests, whereas the Alternate Rapid Naming Composite Score (object/color naming) is constructed based on scaled scores from the Rapid Color Naming and Rapid Object Naming subtests. For individuals 18 years and older within the standardization sample, reliability estimates were 0.90 for the Rapid Naming Composite and 0.91 for the Alternate Rapid Naming Composite (Wagner, Torgeson, & Rashotte, 1999b). The test authors also demonstrated moderate to large correlations between scores on measures of letter and digit rapid naming and decoding and word reading scores across other well-validated assessments (e.g., Wide Range Achievement Test, Third Edition).

The CTOPP rapid naming measures were selected as these are continuous-trial rapid naming tasks. The use of continuous rapid naming tasks when examining the relationship between rapid naming and reading skills is preferable as performance on serial naming tasks has been shown to be more highly associated with reading performance compared with discrete-trial naming performance (Chiappe, Stringer, Siegel, & Stanovich, 2002; Georgiou et al., 2013), and serial naming tasks are thought to more closely align with the demands presented in the reading of continuous text (Norton & Wolf, 2012).

Basic reading skills

Basic reading skills were assessed using subtests from the Woodcock–Johnson Tests of Achievement, Third Edition (WJ-III; Woodcock, McGrew, & Mather, 2001). Specifically, the Basic Reading Skills Cluster (BRSC) score was used in the analyses. The BRSC is composed of two individual subtests: Letter-Word Identification and Word Attack. The Letter-Word Identification subtest is scored based on the number of single words of increasing difficulty an individual can read accurately, whereas the Word Attack subtest assesses how well an individual can decode pseudowords. As reported by the authors, across all individuals in the testing sample, the 1-year test–retest reliability coefficient for the BRSC was 0.95 (McGrew & Woodcock, 2001). Notably, validity across measures of the WJ-III was demonstrated through moderate to strong correlations between scores on the WJ-III and scores on similar subtests across academic domains on other well-established achievement measures.

Reading fluency

The Reading Fluency subtest from the WJ-III was administered to assess fluency of text reading. This subtest measures how quickly an individual can read simple sentences. Individuals are given 3 min to read a series of simple sentences about general knowledge concepts and determine whether the sentences are true or false. The reading fluency score is determined based on the number of accurate responses provided within the allotted time frame. Test–retest reliability coefficients for reading fluency among adolescents and adults in the standardization sample were 0.80 and 0.94, respectively (McGrew & Woodcock, 2001).

Single-word reading speed

Form A of the Test of Word Reading Efficiency (TOWRE; Torgesen, Wagner, & Rashotte, 1999a) was administered as a measure of single-word reading efficiency. The TOWRE Total Word Reading Efficiency (TWRE) composite is composed of two subtests: Sight Word Efficiency (SWE) and Phonemic Decoding Efficiency (PDE). The SWE subtest measures the number of real words an individual can read aloud accurately in 45 s, and the PDE subtest measures the number of pseudowords that can be accurately decoded in 45 s. Authors of the TOWRE indicated test–retest reliability scores of 0.93 and 0.94 for the two forms of the TOWRE across all individuals in the standardization sample and moderate to large correlations between subtest scores and scores of reading rate, accuracy, and comprehension on the Gray Oral Reading Test, Third Edition (GORT-3) and Woodcock Reading Mastery Tests, Revised (WRMT-R) within a sample of fifth-grade children (Torgesen, Wagner, & Rashotte, 1999b).

Reading comprehension

Reading comprehension was assessed using the Comprehension section from Form H of the Nelson–Denny Reading Test (NDRT; Brown, Fishco, & Hanna, 1993a). The Comprehension section of the NDRT is composed of 7 reading passages accompanied by 38 related questions. Individuals are given 20 min to read as many passages and answer as many questions as possible. Scores are determined based on the number of comprehension questions correctly answered. Reliability estimates for the Comprehension section ranged from 0.86 to 0.88 (Brown, Fishco, & Hanna, 1993b).

Math and writing fluency

The Math Fluency and Writing Fluency subtests from the WJ-III were administered to assess individuals' academic fluency abilities outside of the reading domain. The math fluency subtest measures how many simple math calculations (e.g., one- to two-digit addition, subtraction, multiplication, and division) an individual can accurately solve in a 3-min time period. On the writing fluency subtest, individuals are provided with three target words per item which they must incorporate into a simple sentence; the test is scored based on the number of sentences the individual can generate with precise inclusion of the target words given 7 min. For the Math Fluency subtest, the reliability coefficient among adolescents in the standardization sample was 0.89 and among adults it was 0.96. For these groups, reliability coefficients on the Writing Fluency subtest were 0.84 and 0.87, respectively (McGrew & Woodcock, 2001).

Procedures

Institutional Review Board approval was obtained for the study. Data for the study were archival and obtained from the evaluations discussed earlier. All individuals assessed at the clinic were administered the CTOPP Rapid Naming and Alternate Rapid Naming Composites, preventing the possibility of selection bias.

Results

Descriptive Variables

The three groups (ADHD, RD, and ADHD + RD) did not differ significantly in regard to age, F(2, 202) = 1.49, p = .23, or general intellectual ability, as measured by the WAIS-IV GAI, F(2, 202) = 0.72, p = .49. In contrast, the mean WAIS-IV PSI scores were significantly different across the three groups, F(2, 202) = 3.63, p = .03. Specifically, the ADHD group had the highest overall average processing speed scores (M = 96.99; SD = 12.70) followed by the comorbid (ADHD + RD) group (M = 92.63; SD = 14.87) and the RD-only group (M = 92.06; SD = 9.50). Bonferroni post hoc tests revealed that the ADHD group performed more quickly on processing speed tasks compared with both the RD and comorbid groups, but that the processing speed scores across the RD and comorbid groups were not significantly different.

Notably, we also ran analyses to determine the appropriateness of examining the ADHD group as a whole versus conducting separate analyses with individuals grouped by ADHD subtype. Specifically, we ran three one-way analyses of variance (ANOVAs) comparing those individuals with ADHD Predominantly Inattentive Type (ADHD-PI; n = 40) and ADHD Combined Type (ADHD-C; n = 40) subtypes on processing speed, alphanumeric rapid naming, and nonalphanumeric rapid naming. Individuals with ADHD Predominantly Hyperactive/Impulsive Type and ADHD Not Otherwise Specified were not included in these analyses as these groups were minimal in size (n = 1 and 2, respectively). Results revealed that the ADHD-PI and ADHD-C groups did not differ significantly on their processing speed (p = .09), alphanumeric rapid naming (p = .42), or nonalphanumeric rapid naming (p = .31) scores. Therefore, these individuals were combined in further analyses under a general ADHD group.

Comparison of Alphanumeric and Object/Color Naming Across Groups

First, we conducted two one-way ANOVAs to examine whether the ADHD, RD, and comorbid groups varied significantly across alphanumeric rapid naming and object/color naming composite scores. The alpha level was set at 0.02 (0.05/4) for these analyses and follow-up analyses with PSI as a covariate by using the Bonferroni method to correct for increased probability of Type I error due to multiple comparisons. As presented in Table 1, a main effect was observed for the alphanumeric rapid naming composite and approached the Bonferroni-corrected α level for the object/color naming composite (p = .026). Bonferroni post hoc comparisons indicated that the ADHD group earned significantly higher average alphanumeric rapid naming scores compared with both the RD group (d = 0.90) and the comorbid group (d = 1.03); the comorbid and RD-only groups did not significantly differ. Regarding object/color naming composite scores, only differences between the ADHD and comorbid groups approached significance, with the trend in the direction of the ADHD group demonstrating higher scores (d = 0.48). As WAIS-IV PSI scores differed across groups and were shown to be moderately and significantly correlated with alphanumeric rapid naming (r = .36, p < .001) and object/color naming (r = .38, p < .001) scores in preliminary analyses, secondary analyses of covariance were conducted using PSI as a covariate. Table 1 presents the adjusted means and standard deviations (SDs) after accounting for PSI scores. With the inclusion of the covariate, a similar main effect was observed for the mean alphanumeric rapid naming scores across groups and large effects sizes remained for the ADHD group compared with the RD and comorbid groups (d = 0.83 and 0.94, respectively); however, after accounting for PSI scores, a main effect was no longer present across groups when comparing object/color naming scores, suggesting that initial differences in object/color naming scores between the ADHD and comorbid groups were attributable to differences in processing speed.

Table 1.

Analysis of variance and covariance results for alphanumeric and object/color naming scores across groups

Variable Group
 
F(2, 202) 
1. ADHD (n = 83) 2. RD (n = 71) 3. ADHD + RD (n = 49) 
Analysis of variance results 
 RAN 98.63 (15.87) 84.32 (15.93) 82.98 (13.90) 22.79* (1 > 2, 3) 
 AltRAN 90.28 (13.67) 86.73 (14.05) 83.35 (15.78) 3.71** (1 > 3) 
Analysis of covariance results with WAIS PSI as a covariate 
 RAN 97.51 (14.87) 85.19 (14.80) 83.52 (14.74) 18.61* (1 > 2, 3) 
 AltRAN 89.11 (13.57) 87.64 (13.51) 84.05 (13.46) 2.14 (ns)*** 
Variable Group
 
F(2, 202) 
1. ADHD (n = 83) 2. RD (n = 71) 3. ADHD + RD (n = 49) 
Analysis of variance results 
 RAN 98.63 (15.87) 84.32 (15.93) 82.98 (13.90) 22.79* (1 > 2, 3) 
 AltRAN 90.28 (13.67) 86.73 (14.05) 83.35 (15.78) 3.71** (1 > 3) 
Analysis of covariance results with WAIS PSI as a covariate 
 RAN 97.51 (14.87) 85.19 (14.80) 83.52 (14.74) 18.61* (1 > 2, 3) 
 AltRAN 89.11 (13.57) 87.64 (13.51) 84.05 (13.46) 2.14 (ns)*** 

Notes: Values are given as mean (SD). Estimated SD for analyses of covariance = (SE × √n) for each group. ADHD = Attention Deficit Hyperactivity Disorder; RD = Reading Disorder; SE = standard error; RAN = Rapid Naming Composite from the CTOPP; AltRAN = Alternate Rapid Naming Composite from the CTOPP; SD = standard deviation; CTOPP = Comprehensive Test of Phonological Processing.

*p < .001; **p = .026; ***ns = not significant.

In addition to examining mean score differences between the groups, we also examined potential differences between the groups with regard to the proportion of participants scoring significantly below average on the rapid naming composites. We used a cutoff score of ≤85 to determine a significant weakness, as this is fully 1 SD below the mean and a commonly used cutoff (e.g., Dombrowski, Kamphaus, & Reynolds, 2004). When this cutoff was applied, 19.3% (16 of 83 participants), 56.3% (40 of 71 participants), and 61.2% (30 of 49 participants) of the ADHD, RD, and comorbid groups, respectively, scored below this cutoff for the alphanumeric naming composite. On the object/color naming composite, 38.6% (32 of 83 participants), 52.1% (37 of 71 participants), and 67.3% (33 of 49 participants) of the ADHD, RD, and comorbid groups, respectively, scored below the cutoff. Chi-square analyses indicated significant group differences for both alphanumeric naming, χ2(2, N = 203) = 30.94, p < .001, and object/color naming, χ2(2, N = 203) = 10.37, p = .006. Bonferroni-corrected post hoc z-tests indicated that participants in the RD and comorbid groups were more likely than those in the ADHD group to score below the cutoff on the alphanumeric rapid naming composite. Participants in the comorbid group were more likely than those in the RD and ADHD groups to score below the cutoff on the object/color naming composite.

Alphanumeric and Object/Color Naming Composite and Subtest Scores Within Groups

Table 2 presents the results of a series of paired t-tests comparing the alphanumeric rapid naming and object/color naming scores within the three clinical groups. Alpha was set at 0.02 (0.05/3) to account for increased probability of Type 1 error resulting from multiple comparisons. Across the ADHD, RD, and comorbid groups, only the ADHD group performed significantly differently on measures of alphanumeric rapid naming and object/color naming. Their average object/color naming scores were significantly lower compared with their alphanumeric rapid naming scores, t(82) = 5.70, p < .01, with a moderate effect size (d = 0.56). In contrast, both the RD and comorbid groups earned similar scores on measures of alphanumeric and object/color naming.

Table 2.

Within subjects t-test results for alphanumeric rapid naming and object/color naming scores

Group Variable Mean (SDt(dfEffect size 
ADHD (n = 83) RAN 98.63 (15.87) 5.70 (82)* 0.56 
AltRAN 90.28 (13.67) 
RD (n = 71) RAN 84.32 (15.93) −1.47 (70) −0.16 
AltRAN 86.73 (14.05) 
ADHD + RD (n = 49) RAN 82.98 (13.90) −0.22 (48) −0.02 
AltRAN 83.35 (15.78) 
Group Variable Mean (SDt(dfEffect size 
ADHD (n = 83) RAN 98.63 (15.87) 5.70 (82)* 0.56 
AltRAN 90.28 (13.67) 
RD (n = 71) RAN 84.32 (15.93) −1.47 (70) −0.16 
AltRAN 86.73 (14.05) 
ADHD + RD (n = 49) RAN 82.98 (13.90) −0.22 (48) −0.02 
AltRAN 83.35 (15.78) 

Notes: ADHD = Attention Deficit Hyperactivity Disorder; RD = Reading Disorder; RAN = Rapid Naming Composite from the CTOPP; AltRAN = Alternate Rapid Naming Composite from the CTOPP; SD = standard deviation; df = degrees of freedom; CTOPP = Comprehensive Test of Phonological Processing.

*p < .01.

In order to more closely examine the groups' performances on measures of rapid naming, the four individual naming subtests (letters, digits, colors, and objects) were compared using a block design model for each group individually (see Table 3). For the ADHD-only group, there was a significant main effect comparing the individual subtests. Specifically, this group performed similarly on both alphanumeric rapid naming subtests. Additionally, no significant differences were observed in scores on the letter and color naming subtests. However, scores for digit naming were significantly higher than both color naming and object naming scores, color naming scores were significantly higher than object naming, and letter naming scores were significantly higher than object naming. Overall, the ADHD group was able to name digits more quickly than any other stimuli and scored lowest when required to name objects.

Table 3.

Post hoc comparisons between alphanumeric rapid naming and object/color naming scores within groups

Group Means
 
F 
Digit (RAN) Letter (RAN) Color (AltRAN) Object (AltRAN) 
ADHD 10.06a 9.51ab 8.82b 7.95c 9.98* 
RD 7.97de 6.90f 8.36d 7.35ef 4.12* 
ADHD + RD 7.61gh 6.71i 7.76g 6.74hi 1.98 
Group Means
 
F 
Digit (RAN) Letter (RAN) Color (AltRAN) Object (AltRAN) 
ADHD 10.06a 9.51ab 8.82b 7.95c 9.98* 
RD 7.97de 6.90f 8.36d 7.35ef 4.12* 
ADHD + RD 7.61gh 6.71i 7.76g 6.74hi 1.98 

Notes: Scores with the same subscripts within a row do not differ significantly at the p < .05 level. ADHD = Attention Deficit Hyperactivity Disorder; RD = Reading Disorder; RAN = Rapid Naming Composite from the CTOPP; AltRAN = Alternate Rapid Naming Composite from the CTOPP; CTOPP = Comprehensive Test of Phonological Processing.

*p < .01.

Although a significant main effect was observed when comparing the alphanumeric rapid naming and object/color naming subtests for the RD group as well, a different trend of scores was observed compared with the ADHD-only group. That is, this group performed best on the digit and color naming tasks, and the most significant differences across scores were between the color and letter naming subtests; this group earned significantly higher scores when naming colors compared with letters. Interestingly, letter and object naming scores did not vary significantly nor did the scores on the digit and object subtests. A similar pattern of scores were demonstrated when comparing subtest scores for the comorbid group; however, the overall main effect was not significant, indicating that scores across subtests were the most consistent in general within this group.

In sum, considering scores across alphanumeric rapid naming and object/color naming subtests within each group, results demonstrated that the ADHD group performed better on alphanumeric rapid naming tasks compared with object/color naming tasks. However, for both the RD and comorbid groups, performance was not clearly stronger or weaker within either the alphanumeric or object/color naming domains.

Rapid Naming and Academic Achievement Measures

Several hierarchical multiple regressions were conducted to examine the relationships between alphanumeric rapid naming and object/color naming scores and six academic achievement measures within each group. This statistical approach was chosen as hierarchical regressions are frequently utilized within social science literature and are commonly used to demonstrate the incremental contribution of individual independent variables (Canivez, 2013; McFall, 2005). GAI and PSI scores were included in the first two steps of each regression in order to observe the predictive utility of alphanumeric rapid naming and object/color naming scores above and beyond variance accounted for by general intellectual ability and processing speed abilities. Evaluation of collinearity diagnostics resulted in tolerance values ranging from 0.58 to 0.89 and variable inflation factor values ranging from 1.13 to 1.73, indicating that multicollinearity was not problematic (Keith, 2006). Results of the regression analyses are presented in Table 4. Across the three groups, GAI scores accounted for a significant amount of variance for nearly all academic achievement scores, as would be expected. Additionally, after accounting for general intellectual ability, processing speed scores significantly predicted scores on all academic fluency measures (i.e., WJ-III Reading, Math, and Writing Fluency; TOWRE TWRE) for both the RD and comorbid groups. In contrast, processing speed only accounted for a significant amount of variance in reading and math fluency scores for the ADHD-only group. For all three groups, processing speed also accounted for a significant amount of variance in reading comprehension scores, as measured by the NDRT Comprehension index. Processing speed was not a significant predictor of basic reading skills (i.e., WJ-III ACH BRSC) for any group.

Table 4.

Summary of hierarchical regression analyses for WAIS GAI/PSI, alphanumeric rapid naming, and object/color naming scores predicting academic achievement variables

 ADHD (n = 83)
 
RD (n = 71)
 
ADHD + RD (n = 49)
 
R2 R2 Δ R2 R2 Δ R2 R2 Δ 
Dependent variable = WJ-III Basic Reading Skills Cluster 
 Step 1: WAIS GAI .34 .34* .14 .14* .17 .17* 
 Step 2: WAIS PSI .34 .00 .14 .00 .20 .03 
 Step 3: RAN .37 .02 .31 .16* .20 .00 
 Step 4: AltRAN .37 .00 .31 .00 .20 .00 
Dependent variable = WJ-III Reading Fluency 
 Step 1: WAIS GAI .24 .24* .08 .08* .17 .17* 
 Step 2: WAIS PSI .44 .21* .26 .18* .43 .27* 
 Step 3: RAN .49 .05* .30 .04 .44 .01 
 Step 4: AltRAN .50 .02 .31 .02 .45 .00 
Dependent variable = WJ-III Math Fluency 
 Step 1: WAIS GAI .08 .08* .04 .04 .09 .09* 
 Step 2: WAIS PSI .24 .16* .22 .18* .19 .09* 
 Step 3: RAN .32 .08* .26 .04 .21 .03 
 Step 4: AltRAN .32 .01 .26 .00 .23 .02 
Dependent variable = WJ-III Writing Fluency 
 Step 1: WAIS GAI .16 .16* .08 .08* .14 .14* 
 Step 2: WAIS PSI .19 .02 .24 .15* .37 .23* 
 Step 3: RAN .22 .03 .30 .06* .37 .00 
 Step 4: AltRAN .24 .02 .31 .01 .38 .01 
Dependent variable = TOWRE Total WRE 
 Step 1: WAIS GAI .04 .04 .05 .05 .08 .08* 
 Step 2: WAIS PSI .07 .03 .12 .07* .19 .10* 
 Step 3: RAN .51 .44* .47 .34* .32 .13* 
 Step 4: AltRAN .51 .00 .49 .02 .34 .03 
Dependent variable = NDRT Comprehension 
 Step 1: WAIS GAI .27 .27* .32 .32* .28 .28* 
 Step 2: WAIS PSI .30 .03* .44 .12* .44 .17* 
 Step 3: RAN .32 .02 .45 .00 .46 .02 
 Step 4: AltRAN .35 .03* .47 .02 .48 .02 
 ADHD (n = 83)
 
RD (n = 71)
 
ADHD + RD (n = 49)
 
R2 R2 Δ R2 R2 Δ R2 R2 Δ 
Dependent variable = WJ-III Basic Reading Skills Cluster 
 Step 1: WAIS GAI .34 .34* .14 .14* .17 .17* 
 Step 2: WAIS PSI .34 .00 .14 .00 .20 .03 
 Step 3: RAN .37 .02 .31 .16* .20 .00 
 Step 4: AltRAN .37 .00 .31 .00 .20 .00 
Dependent variable = WJ-III Reading Fluency 
 Step 1: WAIS GAI .24 .24* .08 .08* .17 .17* 
 Step 2: WAIS PSI .44 .21* .26 .18* .43 .27* 
 Step 3: RAN .49 .05* .30 .04 .44 .01 
 Step 4: AltRAN .50 .02 .31 .02 .45 .00 
Dependent variable = WJ-III Math Fluency 
 Step 1: WAIS GAI .08 .08* .04 .04 .09 .09* 
 Step 2: WAIS PSI .24 .16* .22 .18* .19 .09* 
 Step 3: RAN .32 .08* .26 .04 .21 .03 
 Step 4: AltRAN .32 .01 .26 .00 .23 .02 
Dependent variable = WJ-III Writing Fluency 
 Step 1: WAIS GAI .16 .16* .08 .08* .14 .14* 
 Step 2: WAIS PSI .19 .02 .24 .15* .37 .23* 
 Step 3: RAN .22 .03 .30 .06* .37 .00 
 Step 4: AltRAN .24 .02 .31 .01 .38 .01 
Dependent variable = TOWRE Total WRE 
 Step 1: WAIS GAI .04 .04 .05 .05 .08 .08* 
 Step 2: WAIS PSI .07 .03 .12 .07* .19 .10* 
 Step 3: RAN .51 .44* .47 .34* .32 .13* 
 Step 4: AltRAN .51 .00 .49 .02 .34 .03 
Dependent variable = NDRT Comprehension 
 Step 1: WAIS GAI .27 .27* .32 .32* .28 .28* 
 Step 2: WAIS PSI .30 .03* .44 .12* .44 .17* 
 Step 3: RAN .32 .02 .45 .00 .46 .02 
 Step 4: AltRAN .35 .03* .47 .02 .48 .02 

Notes: ADHD = Attention Deficit Hyperactivity Disorder; RD = Reading Disorder; WAIS GAI = Wechsler Adult Intelligence Scale General Ability Index; WAIS PSI = Wechsler Adult Intelligence Scale Processing Speed Index; RAN = Rapid Naming Composite from the CTOPP; AltRAN = Alternate Rapid Naming Composite from the CTOPP; WJ-III = Woodcock-Johnson Tests of Achievement, Third Edition; TOWRE Total WRE = Test of Word Reading Efficiency Total Word Reading Efficiency Score; NDRT = Nelson–Denny Reading Test; CTOPP = Comprehensive Test of Phonological Processing.

*p < .05.

Regarding the primary variables of interest, alphanumeric rapid naming and object/color naming scores were limited in terms of their significance in predicting performance on academic achievement measures across groups. Specifically, alphanumeric rapid naming scores were mainly predictive of TOWRE TWRE scores; alphanumeric rapid naming scores accounted for a significant amount of variance on this composite for all three groups. However, across other measures, alphanumeric rapid naming scores were less significant in their predictive utility. Specifically, alphanumeric rapid naming scores significantly predicted reading and math fluency scores for the ADHD group only and BRSC and writing fluency scores for the RD group. Alphanumeric rapid naming scores did not account for a significant amount of variance for any academic measures for the comorbid group except for TWRE.

Object/color naming scores generally did not account for significant variance in academic achievement scores. The only score for which object/color naming accounted for a significant amount of variance was the NDRT Comprehension index score for the ADHD group.

Discussion

The aim of this study was to investigate potential weaknesses in naming speed abilities of older individuals with ADHD and to compare naming speed abilities across groups of adolescents and adults with ADHD, RD, and comorbid ADHD/RD. As anticipated, the ADHD-only group performed significantly faster overall on measures of alphanumeric rapid naming tasks compared with the RD and ADHD/RD groups. This finding was replicated even after accounting for processing speed differences across the three groups. In contrast, all three groups performed similarly on the overall object/color naming composite when controlling for processing speed differences. Whereas fluency in retrieval of alphanumeric and nonalphanumeric information was slower in general for the RD and comorbid groups, there appeared to be a distinct relative weakness in retrieval of nonalphanumeric information within the ADHD group. Notably, though the mean score on the nonalphanumeric naming composite fell in the lower end of the average range for this group, scores on the alphanumeric naming composite were more centrally within the average range and were statistically higher. These findings are generally consistent with previous investigations of rapid naming weaknesses among similar clinical groups of children and adolescents (Shanahan et al., 2006). Notably, other studies have not examined how rapid naming abilities across ADHD and RD groups differ once accounting for processing speed. Although processing speed deficits have been repeatedly found within groups with ADHD (Shanahan et al., 2006; Willcutt, Pennington, Olson, Chhabildas, & Hulslander, 2005), we found that the comparable object and color naming weaknesses across the ADHD, RD, and comorbid groups were only revealed once processing speed was controlled, indicating that this is a significant variable to include when examining the role of naming speed.

Our study also took a novel approach to studying naming speed by examining the patterns of performance on different rapid naming measures within each group. In a trend unseen within the other two clinical groups, individuals with ADHD-only scored significantly lower on object and color naming measures in comparison with alphanumeric naming tasks and scored significantly lower on object naming compared with the other three naming tasks. Researchers have suggested that children with ADHD struggle with object naming due to the semantic processing and executive functioning demands of this task (Tannock et al., 2000), and previous work has demonstrated that adults with ADHD have difficulty with verbal fluency when required to generate words based on semantic categories (Marchetta, Hurks, Krabbendam, & Jolles, 2008). Additionally, in a study comparing adults with and without ADHD on measures of executive functioning and verbal learning, Holdnack, Moberg, Arnold, Gur, and Gur (1995) found that individuals in the clinical group demonstrated less consistent use of a semantic organizational strategy within a verbal memory task. Our results further support the idea that individuals with ADHD might experience particular difficulty with quickly filtering and organizing semantic information and continue to experience this difficulty throughout adolescence and adulthood.

With regard to the relationship between rapid naming abilities and academic achievement, the significance of rapid naming in predicting performance on academic measures was variable after accounting for processing speed and general intellectual ability. Within ADHD groups, processing speed has been previously shown to affect performance in several academic areas such as reading fluency (Jacobson et al., 2011), reading comprehension (Noggle, Thompson, & Davis, 2014), and writing fluency (DeBono et al., 2012). Therefore, it appears particularly important to parcel out how rapid naming may be associated with academic performance above and beyond processing speed. In line with earlier findings, our results indicated that processing speed was significantly associated with performance on multiple academic measures across groups, including fluency tasks across domains (i.e., reading, math, and writing) and a reading comprehension task. Interestingly, once processing speed was accounted for, alphanumeric rapid naming was found to be variably predictive of academic performance. Specifically, though alphanumeric rapid naming was significantly predictive of single-word reading speed across the three groups, it was only predictive of reading fluency and math fluency within the ADHD group and only predictive of basic reading skills and writing fluency within the RD group.

Multiple explanations may be considered for our findings. First, there have been variable findings previously about the specific contribution of rapid naming abilities in predicting reading achievement throughout development. For example, Beidas, Khateb, and Breznitz (2013) found that among university students with and without dyslexia, letter naming speed was a significant predictor of reading fluency, but was only predictive of decoding abilities in the dyslexic group. Further, a longitudinal investigation of typically developing children showed that the influence of rapid naming abilities on basic reading skills appeared to be time limited; among the sample of elementary school children, naming speed lacked independent significant influence on word reading by as early as fourth grade (Wagner et al., 1997). Secondly, previous research has suggested that processing speed may be an integral component in the relationship between rapid naming and reading achievement. Specifically, within samples of elementary-aged typically developing children, processing speed has been repeatedly shown to be indirectly associated with word recognition through its significant direct effect on rapid naming abilities and the direct relationship between rapid naming and word reading (Cutting & Denckla, 2001; Kail & Hall, 1994). Therefore, although these studies did not show that processing speed directly predicted reading abilities, rapid naming scores may contribute less to the variance in reading scores once accounting for processing speed.

Considering the specific relative weaknesses in object/color naming apparent in the ADHD group compared with their performance on alphanumeric rapid naming measures, it is meaningful that object/color rapid naming was significant in determining the performance on a reading comprehension measure within the ADHD group, although the amount of variance accounted for was small (i.e., 3%). Therefore, although object/color naming difficulties among individuals with ADHD may not necessarily affect their performance on more simple, concrete academic tasks, it may be related to the type of higher-order skills that are utilized when processing and organizing information within text. The reading comprehension task was timed and failure to complete all items resulted in lower scores; therefore, the fluency with which an individual can filter information, make semantic connections, and access previous knowledge is a pivotal component in achieving success on this measure. The relationship between object/color naming and scores on this measure within the ADHD group may be reflective of similar difficulties with fluency in semantic processing. Overall, individuals with ADHD may be able to perform simple, concrete tasks with appropriate speed and accuracy but may exhibit weaknesses on more complex academic tasks.

Importantly, the ecological validity of the academic measures utilized within this study was limited, particularly considering the age and educational status of the participants. Within a postsecondary setting, academic demands generally reach far beyond the demonstration of basic academic skills, requiring students to integrate and organize large quantities of information while thinking critically. Therefore, the significance of the relative weaknesses within the ADHD group in object/color naming, considered to be reflective of more complex, semantic processing, may not have been revealed within scores on tasks that tapped into lower-order cognitive processing. For example, object/color naming scores were not significantly related to scores on the reading fluency task, but this task only asked students to decide the validity of short, rudimentary sentences. In contrast, it is possible problems with speed of semantic processing would negatively affect the speed of a college student with ADHD reading a long, multifaceted passage that requires processing of difficult vocabulary, recalling of previously learned information, and making inferences about the text. Previous research has demonstrated that children with reading comprehension difficulties often exhibit executive function difficulties, such as problems with strategic organizing, and these difficulties account for struggles with comprehension after accounting for phonological processing abilities (Locascio, Mahone, Eason, & Cutting, 2010). Therefore, it is possible that individuals who are slower in their ability to filter and give selective attention to certain information would also exhibit greater difficulty in completing more complex reading tasks. In accordance with this line of reasoning, object/color naming did predict performance for the ADHD group on the Nelson–Denny Reading Comprehension task, which requires students to answer questions related to longer, silently read passages. Though this task still may not be as challenging as postsecondary work considering the content is more elementary, this is a closer approximation of the type of assessments with which postsecondary students are presented. Furthermore, in this same vein, though general processing speed accounted for a substantial amount of variance in academic performance on the measures we administered, fluency in basic cognitive processing may not be as important as an individual's ability to quickly retrieve semantic information when being administered content-heavy examinations in the postsecondary setting.

Limitations

One important consideration in interpreting the results of this study is that data were collected from students who had received psychological evaluations generally for the purpose of postsecondary disability determination. As a recent report estimated that the percentage of students with disabilities who go on to attend postsecondary education within 4 years after high school is estimated to be less than 50% (Newman, Wagner, Cameto, & Knokey, 2009), our sample may have included higher functioning students with ADHD in general, and findings may not generalize to the overall population of adults with ADHD. Furthermore, the same report by Newman and colleagues revealed that only 37% of postsecondary students with disabilities informed their schools of their disabilities. The fact that our sample was generally composed of students seeking accommodations based on disability eligibility may be suggestive that participants were more self-aware and cognitively developed in regard to understanding their needs than the average postsecondary student with a disability.

Another limitation of the study concerns the failure to include a control group in the analyses. Without a control group, we were unable to analyze how individuals with ADHD perform on rapid naming measures compared with a typically developing group and therefore were unable to investigate whether performance on nonalphanumeric rapid naming measures is significantly weaker for the ADHD group compared with nonclinical individuals. However, it is important to note that the rapid naming measures utilized within this study are norm-referenced, and scores are generated based on comparison with a large population of same-age individuals. Therefore, the naming speed scores of the three clinical groups can still be compared generally with the performance of typically developing same-age peers.

We also did not include measures of performance validity in the current study, although inclusion of such measures is recommended when external incentive for suboptimal performance may be present as was the case for the evaluations conducted within the current study (Bush et al., 2005). Extant research indicates that this is potentially a significant problem within adult ADHD evaluations, as approximately 20%–50% of examinees completing such evaluations fail performance validity tests when they are administered (Musso & Gouvier, 2014). Although we did not administer stand-alone performance validity tests, we were able to examine scores on an embedded performance validity indictor within the WAIS-IV: Reliable Digit Span (RDS). Applying an established cutoff of <7 on RDS (Schroeder, Twumasi-Ankrah, Baade, & Marshall, 2012), we found that 8.9% of our sample scored below this cutoff. These data provide some support for the validity of the scores from the measures administered during the current study for most participants; however, it should be highlighted that stand-alone performance validity tests are more sensitive to detecting suboptimal effort and that when these tests are administered to those participating in adult ADHD evaluations, high failure rates have been found (e.g., nearly 48% of examines in Sullivan, May, & Galbally, 2007).

A final limitation concerns the exclusion of control variables that would have permitted clearer inferences from our results. Although we excluded individuals with severe psychopathology, we included participants with commonly occurring disorders such as mood disorders. Because depression has been shown to be associated with slowed information processing speed (Tsourtos, Thompson, & Stough, 2002), this uncontrolled variable may have affected the results of the current study. Furthermore, inferences drawn from the current results would be clearer if we controlled for variables that would have permitted a fine-grained analysis of processing speed. Because the processing speed measures used in the current student require both fine motor skill and cognitive processing speed, inclusion of a measure of basic motor speed would have allowed for disaggregation of the abilities tapped by the processing speed measures as well as their specific influence in relation to naming speed and the prediction of academic fluency.

Implications for Practice

Regardless of certain limitations, the findings of the current study suggest important implications, particularly for evaluators assessing older individuals with possible ADHD. Psychologists evaluating individuals in this population are often heavily reliant on rating scales, symptom checklists, and observational reports (Nelson et al., 2014), all of which are subject to malingering, especially, with college students seeking accommodations (see Musso & Gouvier, 2014) and self-report biases, such as students without ADHD reporting significant symptoms (see Lovett, Nelson, & Lindstrom, 2014 for a review). Therefore, objective measures that can identify common weaknesses among students in this population would be helpful in supporting diagnostic impressions. Perhaps the most interesting results of our study were revealed by comparing performance on individual rapid naming tasks within clinical groups; that is, individuals in the ADHD group, on average, showed a distinctive pattern of performance in which alphanumeric rapid naming was significantly stronger than nonalphanumeric rapid naming. By understanding relative weaknesses that may be exhibited by individuals with ADHD, psychologists can support their diagnoses through both normative and ipsative comparisons. Our study findings suggest that a weaker performance on nonalphanumeric rapid naming tasks compared with alphanumeric naming tasks could be interpreted by evaluators as indicative of semantic processing difficulties often observed in individuals with ADHD. Additionally, as the administration of rapid naming tasks is brief and simple in nature, it would not be particularly burdensome as an addition to a test battery.

It is important to note that our findings were based on group-level analyses, and the absence of the particular pattern of rapid naming performance that was found overall in our ADHD group should not rule out a diagnosis of ADHD. Similarly, a specific relative weakness in object/color naming certainly does not indicate a diagnosis of ADHD. However, as it is always recommended that psychologists utilize multiple methods during evaluations, it is continually beneficial to investigate useful diagnostic tools, and rapid naming assessments may provide useful information to evaluators when assessing older individuals for ADHD.

Conflict of Interest

None declared.

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