550480
research-article2014
JADXXX10.1177/1087054714550480Journal of Attention DisordersHurford et al.
Article
Examination of the Effects of Intelligence on the Test of Variables of Attention for Elementary Students
Journal of Attention Disorders 1–9 © 2014 SAGE Publications Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1087054714550480 jad.sagepub.com
David P. Hurford1, Alex C. Fender1, Jordan L. Boux1, Courtney C. Swigart1, Paige S. Boydston1, Shanise R. Butts1, Christy L. Cox1, Trent A. Becker1, and Mary E. Pike1
Abstract Objective: To examine the performance differences on the Test of Variables of Attention (TOVA) among different IQ level groups. Method: The present study examined the results of the TOVA with 138 elementary students aged 6 to 10 years who were assigned to one of four different groups based on their scores from the Wechsler Nonverbal Scale of Ability (WNV; low average: IQ < 90, average: IQ between 90 and 109, high average: IQ between 110 and 119, superior: IQ between 120 and 129, and very superior: IQ > 129). The latter two groups were combined. Results: On all TOVA measures (response time, response time variability, errors of omission and commission, and ADHD scores), intellectual functioning significantly influenced performance. Conclusion: The results of the present study indicate that performance on the TOVA was affected by intellectual functioning. (J. of Att. Dis. XXXX; XX(X) XX-XX) Keywords ADHD, children, assessment, psychometric properties, continuous performance tests Between 3% and 9% of youth and 2.5% to 4% of adults are estimated to experience symptoms of ADHD (American Psychiatric Association [APA], 2013; Barbaresi et al., 2013; Monastra, 2008; Pastor & Reuben, 2008). ADHD is a neurodevelopmental disorder, with symptoms presenting during childhood and potentially continuing through adulthood (APA, 2013; Dennis et al., 2009; Ozonoff & Jensen, 1999). Individuals experiencing inattention may be easily distracted, struggle to focus on instructions or tasks, and have difficulty completing and/or turning in assignments. Symptoms of hyperactivity can include difficulty sitting still and/or racing thoughts. Individuals experiencing impulsivity may inappropriately blurt out comments, often interrupt others, and have difficulty waiting their turn. These symptoms may be particularly detrimental for the education of roughly 4.5 million children experiencing symptoms of ADHD (Bloom, Cohen, & Freeman, 2009), as they can negatively impact children’s performance at school. In addition, attentional difficulties can also impact an individual’s functioning occupationally, socially, and at home. Proper diagnosis requires symptom presentation across multiple settings (APA, 2013). Identification of ADHD is important in providing appropriate treatment, but accurate diagnosis remains challenging. Differential diagnosis of ADHD is warranted given that comorbidity is very common with ADHD and accurate
diagnosis is critical for the development of treatment for individuals seeking assistance managing their symptoms. Oppositional Defiant Disorder, Conduct Disorder, Intermittent Explosive Disorder, other neurodevelopmental disorders, neurocognitive disorders, anxiety disorders, mood disorders, learning disorders, substance use disorders, as well as hearing or reading difficulties can present with symptoms and behaviors that closely mirror or overlap symptoms and behaviors that are consistent with ADHD (APA, 2013; Deans, O’Laughlin, Brubaker, Gay, & Krug, 2010; Monastra, 2008). When placed in unchallenging or under-stimulating academic environments, highly intelligent youth can behave in ways consistent with those with ADHD (Baum, Olenchak, & Owen, 1998; Reis & McCoach, 2000). In addition, it can sometimes be difficult to differentiate attentional difficulties from age-appropriate behaviors, such as running or frequent talking (Nelson, Rinn, & Hartnett, 2006; Webb et al., 2005; Wood, 2012).
1
Pittsburg State University, KS, USA
Corresponding Author: David P. Hurford, Center for Research, Evaluation and Awareness of Dyslexia, Pittsburg State University, 1701 S. Broadway, Pittsburg, KS 66762, USA. Email: [email protected]
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Children and adolescents with undiagnosed ADHD will likely continue experiencing difficulty in a variety of settings. For example, social acceptance has been traditionally thought to help protect children with ADHD from a variety of additional symptoms, such as depression. However, this may not be consistently accurate for individuals with ADHD (McQuade et al., 2014). College students with ADHD may have difficulty accessing appropriate, consistent treatment due to provider discomfort evaluating, diagnosing, and treating ADHD (Thomas, Rostain, Corso, Babcock, & Madhoo, 2014). Barbaresi and colleagues (2013) found that nearly 30% of their participants who were diagnosed with ADHD in childhood continued to experience symptoms as adults. Adults who experienced ADHD during childhood may also experience an increased risk of suicide, higher likelihood of having at least one other psychiatric disorder as an adult (Barbaresi et al., 2013), quality of life difficulties (Yang, Tai, Yang, & Gau, 2013), and an increased risk for traffic violations (Vaa, 2014). Utilization of empirically based assessment can help facilitate accurate diagnosis of ADHD. Many measures of attentional functioning rely on parent-, teacher-, and/or selfreport. Such rating scales can be helpful, as the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM5; APA, 2013) highlights the importance of symptom presentation in multiple settings and gathering information from multiple sources (APA, 2013), but there are a number of criticisms associated with them. For example, a rater’s relationship with the individual can influence his or her ratings, as can his or her understanding of the often vague and abstract terms that many self- and other-report measures include (Gupta & Kar, 2010). Results are based on the subjective perceptions of raters and, as such, may not always provide the most accurate depiction of an individual’s actual behavior (DuPaul, 2003). Research and best practices indicate that multiple informational sources are the best avenue for accurate diagnosis of ADHD (Forbes, 1998; Schatz, Ballantyne, & Trauner, 2001). Performance-based tests, though not without their own limitations, can serve as an additional source of information as they more objectively examine attentional abilities and help to reduce the impact of constructs such as rater bias. In addition, multiple sources of convergent information gained from subjective measures, such as self-reported, parent and teacher ratings, and objective measures, such as Continuous Performance Tests (CPTs), increase diagnostic accuracy. The Test of Variables of Attention (TOVA) is one such CPT that examines attentional functioning and is frequently used in the diagnosis of ADHD (Gualtieri & Johnson, 2005). It offers both visual and auditory measures of attention using targets, to which individuals are instructed to respond, and non-targets, to which individuals are instructed to withhold their responses. Research supports the use of the TOVA as a diagnostic tool, along with other measures,
in the diagnosis of ADHD (Forbes, 1998; Schatz et al., 2001). During the administration of the TOVA, the individual responds to the presence of the target and refrains from responding to the non-target. The first half of the TOVA consists of a relatively low ratio of target to non-target presentations (1 target to 3.5 non-targets), which proves challenging for individuals with inattention; the second half consists of a relatively high ratio of target to non-target presentations (3.5 targets to 1 non-target), which proves challenging for those with impulsive and hyperactive behaviors (Leark, Greenberg, Kindschi, Dupuy, & Hughes, 2007). Previous research has examined the effects of administering the TOVA and other CPTs in different settings, including varying locations (Bart, Raz, & Dan, 2014) and times of day (Hurford, Lasater, Erickson, & Kiesling, 2013), with no significant differences; thus, CPTs may be, with clinical consideration, administered in various settings and times (afternoon as well as morning administrations). Because of its use of geometric shapes rather than letters of the alphabet, the TOVA may be an especially attractive diagnostic supplement when assessing children, individuals with learning disabilities, and individuals whose first language is not English (Schatz et al., 2001). Administration of the TOVA results in five scores: 1— errors of omission, 2—errors of commission, 3—response time, 4—response time variability, and 5—ADHD score. An omission error occurs when an individual does not respond to a target and is considered a measure of inattention. A commission error occurs when the examinee responds to a non-target and is considered a measure of impulsivity. Response time measures response latency, whereas response time variability measures consistency and is also the variable that accounts for 80% of the variability between individuals with and without ADHD (Leark et al., 2007). An ADHD score is also provided and offers a comparison of the examinee’s performance to those in the standardization sample who were diagnosed with ADHD. The ADHD score is comprised of three of the measures produced by the TOVA tests expressed as z scores: first half response time, total response time variability, and second half d′ score (zResponse Time Half 1 + zd′ Half 2 × −1 + zResponse Time Variability Total). The d′ measure is derived from Signal Detection Theory and indicates response sensitivity (Green & Swets, 1966). The TOVA provides summary data by quarter for each of the variables listed above, which is quite useful for tracking potential changes in performance. Children with ADHD typically demonstrate more omission and commission errors than their peers without ADHD (Sartory, Heine, Muller, & Elvermann-Hallner, 2002). Response time variability accounts for more than 80% of the variability between children with and without ADHD (Greenberg, Kindschi, Dupuy, & Hughes, 2007). The ADHD score helps to discriminate between individuals
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Hurford et al. Table 1. Demographic Characteristics of the Participants by Group. Group Variable n Age (range) Female/male
LA
A
HA
S/VS
14 7.51 (5.72-9.65) 5/9
78 7.98 (5.87-9.91) 38/40
27 7.84 (5.87-10.40) 16/11
19 7.63 (5.95-9.35) 12/7
Note. LA = low average; A = average; HA = high average; S = superior; VS = very superior.
90
NUMBER OF PARTIICPANTS
identified by the other scores as having difficulty maintaining attention (e.g., due to anxiety, depression, and/or sleep difficulties) from individuals with clinical difficulties that warrant a diagnosis of ADHD. It has been suggested that individuals with ADHD experience difficulty with various cognitive processes (see Gupta & Kar’s, 2010, discussion). Pharmacotherapy treatment for attentional difficulties has a questionable impact on cognitive processes (Hellwig-Brida, Daseking, Keller, Petermann, & Goldbeck, 2011). Previous research has indicated that youth with ADHD obtain Full Scale IQ (FSIQ) scores an average of 9 points lower than their peers (Frazier, Demaree, & Youngstrom, 2004). Level of intelligence has been shown to impact performance on Conners’ CPT; in fact, researchers found intelligence to be a better predictor of performance on the Conners’ CPT than the presence or absence of an ADHD diagnosis (Munkvold, Manger, & Lundervold, 2014). Understanding the relationship between intelligence and CPT performance is crucial for clinicians to better interpret CPT results, such as those delivered by the TOVA. The effect of intelligence on the TOVA has not previously been empirically examined and is the focus of the present article. When the normative sample was created, the authors of the TOVA did not evaluate the participants’ intellectual functioning. As a result, the relationship between performance on the TOVA and intellectual functioning is not known. Information included in the TOVA Clinical Manual concerning interpretation of the TOVA has the following caveat: “assuming average intelligence” (Greenberg et al., 2007, p. 23). This is based on an assumption that intelligence will have an impact on attentional functioning, and therefore on their performance on the TOVA. The present study examined this assumption of the importance of intellectual functioning and performance on the TOVA. It has been assumed that individuals with higher levels of intelligence will perform significantly better on the TOVA than individuals with lower levels of intelligence. There have been no studies that have empirically tested this assumption. As a result, it is not known whether attentional abilities, as assessed by the TOVA, vary as a function of intellectual abilities. The present study addressed this question with elementary-grade students, as they are most likely to be assessed with the TOVA as a component of the diagnostic process.
80 70 60 50 40 30 20 10 0 Low Average
Average
High Average
Superior
Very Superior
GROUP
Figure 1. The distribution of intelligence scores (WNV) by group. Note. WNV = Wechsler Nonverbal Scale of Ability.
Method Participants One hundred thirty-eight students enrolled in Midwest elementary schools in the United States participated in the present study (see Table 1). Participants were assigned to four groups based on their performance on the Wechsler Nonverbal Scale of Ability (WNV) and classified based on Wechsler’s scale, which included the probable error (low average: less than 90, average: 90-109, high average: 110119, superior: 120-129, and very superior: greater than 129). The primary purpose of the study was to examine the potential effect of intelligence on performance on the TOVA. As a result, four groups were formed based on the participants’ intelligence: low average (WNV scores less than 90; n = 14), average (WNV scores between 90 and 109; n = 78), high average (WNV scores between 110 and 119; n = 27), and superior (WNV scores between 120 and 129; n = 19). As can be seen in Figure 1, there were also four individuals who were in the very superior range (WNV scores 130 and above), but due to the small number of individuals in this group, they were combined with the superior group [(nvery superior = 4) + (nsuperior = 15) = 19]. A 4 (Group) × 2 (Gender) ANOVAs were performed on age, which
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Journal of Attention Disorders and controlled attention assessed during the second half. In addition, the sensitivity and specificity measures are adequate (Lowe, 2001). Data from the TOVA will be analyzed by quarter. The TOVA was presented on Dell Latitude D810 laptop computers. These laptops have 39.1 cm screens. Students sat approximately 51 cm away from the laptop and responded by depressing a handheld thumb switch with their dominant hand that is included within the TOVA kit.
Figure 2. The target (left) and the non-target (right) for the TOVA. Note. TOVA = Test of Variables of Attention.
indicated that there were no main effects of group, F(3, 137) = 0.75, p > .53, gender, F(1, 137) = 0.01, p > .53, p > .92, or interaction of group and gender, F(3, 137) = 1.85, p > .14. A chi-square analysis was performed, which indicated that there were no significant differences in the number of males and females between groups, χ2(3) = 3.32, p > .34.
Tasks and Materials TOVA. The present study used Version 7.3 of the TOVA. The TOVA is a CPT used to assess attention which is 22.6 min in length. Administration begins with verbal instructions regarding the nature of the task. After the verbal instructions are given, a 3-min practice session occurs. Participants are provided with feedback regarding their ability to comprehend the nature of the task during the 3-min practice session, but not thereafter. The TOVA has acceptable psychometric properties (Leark, Wallace, & Fitzgerald, 2004; Llorente et al., 2001). The target is comprised of a white square with a small black square contained within it centered near the top of the white square (see Figure 2). The non-target is also a white square, but the black square contained within it is centered near the bottom of the white square. The location of the small black square within the larger white square differentiates the target from the non-target. The participant is instructed to fixate on a small dot in the middle of the screen and the target or non-target is presented for 100 ms centered above the fixation dot. The inter-target interval is 2 s. The 22.6-min duration of the TOVA is comprised of 648 trials. The target density of the first half of the TOVA is 1 target for 3.5 presentations of the non-target such that the participant responds infrequently during first half. The target density of the second half of the TOVA is 3.5 targets for each non-target such that the participants respond quite frequently during the second half. The first half of the TOVA taps inattention whereas the second half taps impulsivity. Validity and reliability have been well established for the TOVA not only with regard to the overall test but also considering sustained attention as assessed during the first half
WNV. The WNV is a nonverbal measure of ability that measures intellectual abilities exclusively through nonverbal items. It is comprised of six subtests (Matrices, Coding, Object Assembly, Recognition, Spatial Span, and Picture Arrangement). The full battery for individuals aged 8 years 0 months to 21 years 11 months consists of Matrices, Coding, Spatial Span, and Picture Arrangement. Those were the subtests used to determine the intellectual functioning of the participants in the present study. There is a relatively high level of comorbidity of ADHD and dyslexia. Approximately, 30% to 50% of children with dyslexia also have ADHD. As the WNV evaluates intellectual ability nonverbally, it is perceived as a more accurate measure for children who have verbal and language deficiencies, which is often the case with children who have dyslexia or reading difficulties. As a result, the WNV was selected as the measure for intelligence due to its ability to measure intelligence nonverbally. The WNV has adequate psychometric properties (Naglieri & Otero, 2012).
Procedure The participants were assessed with the TOVA in groups of 10 in a quiet, dimly-lit vacant facility in each building. Students wore MSA Safety Works PVC Foldable Ear Muffs (Model 10033236), which had a noise reduction level of 20 dB. The ear muffs were worn to reduce the potential for distraction by ambient noise during the assessment. As the classroom was already quiet, wearing the sound-reducing headphones decreased the likelihood that students would be distracted by random sounds (e.g., movement by other students, sneezing, or coughing, etc.). The students were arranged in the classroom such that they faced away from each other and were not able to see other students’ computer screens. The WNV was individually administered by research assistants who had coursework in advanced tests and measures, individual intelligence testing, standardization techniques and were specifically trained to administer the WNV.
Results and Discussion The present study was conducted to examine the assumption that performance on the TOVA could be affected by
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Table 2. Mean Response Time Variability, Mean Response Time, Mean Commission Errors, Mean Omission Errors, and d′ Scores by Quarter and Group. Quarter Group Response time variability Superior High average Average Low average Response time Superior High average Average Low average Commission errors Superior High average Average Low average Omission errors Superior High average Average Low average d′ Superior High average Average Low average
Q1
Q2
Q3
Q4
158.0 (41.6) 137.6 (40.8) 181.4 (88.0) 205.9 (100.5)
179.5 (56.3) 171.9 (61.6) 204.0 (89.7) 248.0 (123.1)
171.0 (44.1) 194.1 (58.8) 232.0 (85.4) 258.9 (83.0)
206.2 (55.5) 231.8 (73.6) 260.0 (92.6) 285.3 (96.6)
528.0 (57.5) 547.4 (89.1) 567.0 (116.7) 686.0 (185.9)
606.4 (77.9) 602.8 (102.1) 631.0 (142.4) 719.5 (151.4)
452.8 (69.3) 483.5 (110.4) 507.0 (119.1) 607.0 (106.1)
465.9 (75.7) 519.3 (107.3) 515.0 (115.7) 604.8 (116.4)
5.0 (8.6) 4.4 (4.9) 10.5 (17.9) 11.4 (16.0)
2.6 (1.9) 6.3 (8.8) 7.8 (15.8) 10.5 (14.0)
11.1 (6.6) 10.1 (5.4) 12.8 (6.1) 10.2 (6.3)
15.1 (6.9) 11.5 (5.17) 13.9 (6.5) 10.2 (5.4)
0.9 (1.1) 2.3 (4.2) 4.2 (5.98) 8.0 (9.3)
1.9 (2.0) 4.4 (6.4) 6.0 (7.13) 9.4 (8.4)
6.7 (10.8) 9.1 (9.4) 15.4 (18.6) 27.2 (21.8)
7.1 (8.8) 16.3 (13.4) 21.6 (23.5) 36.8 (22.4)
5.2 (2.0) 5.4 (2.1) 4.0 (2.19) 3.5 (2.4)
4.6 (1.7) 4.4 (2.3) 3.9 (2.12) 3.2 (2.0)
3.1 (1.8) 2.6 (1.3) 2.0 (1.42) 1.8 (1.5)
2.5 (1.3) 2.0 (1.1) 1.51 (1.28) 1.1 (0.7)
Note. Values in parentheses are standard deviations.
intellectual ability. The TOVA produces a plethora of results including means for response time variability, response time, commission errors, omission errors, and d′. Response time variability is the metric that most strongly differentiates individuals who have attentional difficulties from those who do not. The primary concern of the present study was to determine whether intelligence played a role in TOVA performance, and if so, what variables were affected by intelligence. As a result, the raw scores for response time variability, response time, commission errors, omission errors, and d′ (see Table 2) were included in a 4 (Intelligence group) × 5 (TOVA measure) × 4 (Quarter) repeated-measures ANOVA with repeated measures on type of TOVA measure (e.g., response time variability, response time, commission errors, omission errors, and d′) and quarter (Quarter 1 vs. Quarter 2 vs. Quarter 3 vs. Quarter 4). The analysis reported utilized Greenhouse–Geisser Epsilon to correct for potential sphericity issues. The results indicated that there were a main effects of intelligence group, F(3, 123) = 6.56, p < .0004, ω2 = .12; TOVA measure,
F(4, 492) = 2,021.24, p < .0001, ω2 = .94; and quarter, F(3, 369) = 16.35, p < .0001, ω2 = .11; and two interactions, Group × TOVA measure, F(12, 492) = 4.66, p < .0001, ω2 = .01, and TOVA measure × Quarter, F(12, 1476) = 71.39, p < .0001, ω2 = .001. The main effect involving TOVA measure was due to the different metrics of those measures. Response time and response time variability have similar values given that that their unit of measure concerns milliseconds with mean values ranging from 171 to 719 across groups and quarters. Mean commission and omission errors also have similar metrics, which range from .88 to 16, and are different from response time variability, response time, and d′ (ranging from 1.3 to 5.4). As a result, the main effect due to TOVA measure is not considerably informing; however, their interactions are quite informative. The main effects of group and quarter will be described by TOVA measure. As can be seen in Figure 3 and substantiated with Student–Newman–Keuls post hoc analyses, the superior/ very superior and high average intelligence groups had smaller response time variability means than did
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Journal of Attention Disorders
800
MEAN RESPONSE TIME (ms)
MEAN RESPONSE TIME VARIABILITY (ms)
300 250 200 150 S/VS
100
HA A
50 0
LA
1
2
3
700 600 500 400 S/VS
300
HA
200
A LA
100 0
4
1
2
Note. S = superior; VS = very superior; HA = high average; A = average; LA = low average.
the average and low average intelligence groups across quarters. Response time variability also increased each quarter. The interaction stems from the superior/very superior group’s small response time variability means for the third and fourth quarters relative to their first and second quarters and all other groups. The most important result of the response time variability data concerns the finding that intelligence plays a significant role in performance. The low average group performed the least well and the superior/very superior and high average groups performed the most efficiently. Student–Newman–Keuls post hoc analyses also indicated that the low average intelligence group had the longest response time means across all quarters compared with the other groups. The average, high average, and superior/ very superior groups were not significantly different across quarters (see Figure 4). Interestingly, the Group × Quarter interaction for mean commission errors was a result of the high average and superior/very superior group having very low commission errors for the first two quarters followed by relatively high rates of commission errors for the last two quarters. The low average and average groups had relatively more commission errors for the first quarter, which lowered for these groups. The last two quarters for the average group were relatively high and relatively low for the low average group, respectively. Interestingly, the first half of the TOVA is fairly target scarce in which there is 1 target for every 3.5 non-targets. The second half of the TOVA is target dense in which there are 3.5 targets to every 1 non-target. As a result, commission errors are more likely to occur during the second half, which is what was seen in all groups except for the low average group. In fact, the relative ratio of commission
4
Figure 4. Mean response time by quarter and intelligence group.
Note. S = superior; VS = very superior; HA = high average; A = average; LA = low average.
16
MEAN COMMISSION ERROR
Figure 3. Mean response time variability by quarter and intelligence group.
3
QUARTER
QUARTER
14 12 10 8 S/VS
6
HA 4
A LA
2 0 1
2
3
4
QUARTER
Figure 5. Mean commission errors by quarter and intelligence group.
Note. S = superior; VS = very superior; HA = high average; A = average; LA = low average.
errors rose across quarters with the level of intelligence. The ratio of increase in commission errors from the first, target scarce, to the second half, target dense (Half 2 Commission Errors/Half 1 Commission Errors) was 0.93 for the low average group, 1.5 for the average group, 1.9 for the high average group, and 3.4 for the superior/very superior group (see Figure 5). The opposite result occurred for the errors of omission. Although the low average group had the most errors of commission at any quarter, the four groups performed relatively similarly for the first target-scarce half. As the groups
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Hurford et al.
6 S/VS
35
S/VS
5
HA
HA
30
MEAN D' SCORE
MEAN OMISSION ERROR
40
A 25
LA
20 15 10
A LA
4 3 2 1
5 0
0 1
2
3
4
1
QUARTER
Figure 6. Mean omission errors by quarter and intelligence group.
Note. S = superior; VS = very superior; HA = high average; A = average; LA = low average.
progressed further into the second half, the number of errors of omission increased. The increase was related to level of intelligence in that the superior/very superior group had the fewest omission errors followed by the high average and average groups and then the most errors were generated by the low average group (see Figure 6). Errors of omission occur when the participant fails to respond to a target. The opportunity to make errors of omission is much less likely in the first half of the TOVA, which is the target-scarce condition. Fewer targets are presented and the task involves identifying the target when it is present. Low levels of errors of omission would seem much more likely in a condition in which targets are encountered infrequently, and the data support that notion. The opportunity to make omission errors is much more likely in the target-dense condition of the second half. The results indicate that level of intelligence was an important variable in accurately indicating that the target was present in a high target-dense condition. The final TOVA measure examined was d′, which is a measure of response sensitivity. TOVA uses d′ as a measure of performance decrement and is calculated by determining the ratio of hits to false alarms. The higher the d′ score, the more accurately the participant identified the target. As can be seen in Figure 7, this measure was also influenced by level of intelligence. The superior/very superior and high average groups outperformed the average and low average groups across quarters. Target sensitivity decreased across quarter for all groups, but most notably from the first to the second half in which there is a change in task. The first half is the target-scarce condition in which there are fewer targets to identify, and the second half is the target-dense condition in which there are many targets to identify. Under these conditions, it is expected that d′ values would decrease
2
3
4
QUARTER
Figure 7. Mean d′ score by quarter and intelligence group.
Note. S = superior; VS = very superior; HA = high average; A = average; LA = low average.
during the second half. However, intelligence level played a role in performance with this TOVA measure as well. The TOVA also produces an ADHD score that compares the participant’s performance to a group from the standardization sample comprised of individuals with ADHD. The ADHD score is computed as a combination of z scores associated with response time for the first half, d′ for the second half, and response time variability for the entire assessment (zResponse Time Half 1 + zd′ Half 2 × −1 + zResponse Time Variability Total). A one-way ANOVA was performed on the ADHD scores by group, which indicated that there were significant differences between groups, F(3, 126) = 7.66, p < .0001. The means and standard deviations for the ADHD scores were −0.17 (2.16), −1.1 (2.37), −2.7 (2.86), and −4.1 (3.12) for the superior/very superior, high average, average, and low average groups, respectively. An ADHD score less than −1.81 is considered to be “Not within the Normal Range” and indicates a likelihood of ADHD. As a result, nearly twice as many individuals in the average and low average groups had ADHD scores in the “Not within the Normal Range” as did the high average and superior/very superior groups. As can be seen in Table 3 and substantiated with a chi-square analysis, as intelligence level decreases, the more likely that individuals will have ADHD scores in the Not Within Normal Limits range; χ2(3) = 8.17, p < .04. Thus, individuals in the average and low average intelligence levels would be more likely to be considered as potentially having ADHD than individuals in the high average and superior intelligence levels based on the results of the TOVA. Thus, the level of intelligence plays a significant role in ADHD scores. Based on the results of the present study, it appears that the assumption that intelligence level is an important mediator with regard to the TOVA performance was supported. It is
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Journal of Attention Disorders
Table 3. Participants Whose ADHD Scores Classified as With or Not Within Normal Limits by Group. Group Superior/very superior High average Average Low average
Within normal limits
Not within normal limits
12 15 30 3
7 12 48 11
recommended that clinicians assessing children between the ages of 6 and 10 years consider the participant’s intellectual functioning during the interpretation of the TOVA results. Future research will need to address specifically the manner in which the results of the TOVA should be interpreted with regard to intellectual functioning. Acknowledgment The authors would like to thank the students, teachers, and administrator from Unified School District (USD) 250 in Pittsburg, Kansas, for their willingness to support this study, and Katie Hoover, Ryan Johnson, Evan Adams, Hannah Straw, Autumn Howe, Bekah Orendak, Erin Rivero, and Mila Infanti for their assistance with data collection.
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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Deans, P., O’Laughlin, L., Brubaker, B., Gay, N., & Krug, D. (2010). Use of eye movement tracking in the differential diagnoses of attention deficit hyperactivity disorder (ADHD) and reading disability. Psychology, 1, 238-246. Dennis, M., Francis, D. J., Cirino, P. T., Schachar, R., Barnes, M. A., & Fletcher, J. M. (2009). Why IQ is not a covariate in cognitive studies of neurodevelopmental disorders. Journal of the International Neuropsychological Society, 15, 331-343. doi:10.1017/S1355617709090481 DuPaul, G. (2003). Assessment of ADHD symptoms: Comment on Gomez et al. (2003). Psychological Assessment, 15, 115-117. Forbes, G. B. (1998). Clinical utility of the Test of Variables of Attention (TOVA) in the diagnosis of attention-deficit/ hyperactivity disorder. Journal of Clinical Psychology, 54, 461-476. Frazier, T. W., Demaree, H. A., & Youngstrom, E. A. (2004). Meta-analysis of intellectual and neuropsychological test performance in attention-deficit/hyperactivity disorder. Neuropsychology, 18, 543-555. doi:10.1037/0894-4105. 18.3.543 Green, D. M., & Swets, J. A. (1966). Signal detection theory and psychophysics. New York, NY: John Wiley. Greenberg, L. M., Kindschi, C. L., Dupuy, T. R., & Hughes, S. J. (2007). T.O.V.A. clinical manual: Test of Variables of Attention continuous performance test. Los Alamitos, CA: The TOVA Company. Gualtieri, C. T., & Johnson, L. G. (2005). ADHD: Is objective diagnosis possible? Psychiatry, 2, 44-53. Gupta, R., & Kar, B. R. (2010). Specific cognitive deficits in ADHD: A diagnostic concern in differential diagnosis. Journal of Child and Family Studies, 19, 778-786. doi:10.1007/s10826-010-9369-4 Hellwig-Brida, S., Daseking, M., Keller, F., Petermann, F., & Goldbeck, L. (2011). Effects of methylphenidate on intelligence and attention components in boys with attention-deficit/hyperactivity disorder. Journal of Child and Adolescent Psychopharmacology, 21, 245-253. doi:10.1089/ cap.2010.0041 Hurford, D. P., Lasater, K. A., Erickson, S. E., & Kiesling, N. E. (2013). Examination of the diurnal assumptions of the Test of Variables of Attention for elementary students. Journal of Attention Disorders, 17, 208-214. Leark, R. A., Greenberg, L., Kindschi, C., Dupuy, T., & Hughes, S. J. (2007). Test of Variables of Attention: Professional manual. Los Alamitos, CA: Universal Attention Disorders. Leark, R. A., Wallace, D. R., & Fitzgerald, R. (2004). Testretest reliability and standard error of measurement for the Test of Variables of Attention (T.O.V.A.) with healthy school-age children. Assessment, 11, 285-289. doi:10.1177/1073191104269186 Llorente, A. M., Amado, A. J., Voigt, R. G., Berretta, M. C., Fraley, J. K., Jensen, C. L., & Heird, W. C. (2001). Internal consistency, temporal stability, and reproducibility of individual index scores of the Test of Variables of Attention in children with attention-deficit/hyperactivity disorder. Archives of Clinical Neuropsychology, 16, 535-546. Lowe, S. (2001). Review of the Test of Variables of Attention. In B. S. Plake & J. C. Impara (Eds.), The fourteenth mental
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Hurford et al. measurements yearbook (pp. 2262-2266). Lincoln: The Buros Institute of Mental Measurements, The University of Nebraska Press. McQuade, J. D., Vaughn, A. J., Hoza, B., Murray-Close, D., Molina, B. S. G., Arnold, L. E., & Hechtman, L. (2014). Perceived social acceptance and peer status differentially predict adjustment in youth with and without ADHD. Journal of Attention Disorders, 18, 31-43. doi:10.1177/1087054712437582 Monastra, V. J. (2008). Unlocking the potential of patients with ADHD: A model for clinical practice. Washington, DC: American Psychological Association. Munkvold, L. H., Manger, T., & Lundervold, A. J. (2014). Conners’ continuous performance test (CCPT-II) in children with ADHD, ODD, or a combined ADHD/ODD diagnosis. Child Neuropsychology: A Journal on Normal and Abnormal Development in Childhood and Adolescence, 20, 106-126. doi: 10.1080/09297049.2012.753997 Naglieri, J. A., & Otero, T. M. (2012). The Wechsler Nonverbal Scale of Ability. In D. P. Flanagan & P. L. Harrison (Eds.), Contemporary intellectual assessment: Theories, tests and issues (3rd ed., pp. 436-455). New York, NY: The Guilford Press. Nelson, J. M., Rinn, A. N., & Hartnett, D. N. (2006). The possibility of misdiagnosis of giftedness and ADHD still exists: A response to Mika. Roeper Review, 28, 243-248. Ozonoff, S., & Jensen, J. (1999). Brief report: Specific executive function profiles in three neurodevelopmental disorders. Journal of Autism and Developmental Disorders, 29, 171177. doi:10.1023/A:1023052913110 Pastor, P. N., & Reuben, C. A. (2008). Diagnosed attention deficit hyperactivity disorder and learning disability: United States, 2004-2006 (Series 10, No. 237). Hyattsville, MA. Retrieved from http://www.cdc.gov/nchs/data/series/sr_10/Sr10_237. pdf Reis, S. M., & McCoach, D. B. (2000). The underachievement of gifted students: What do we know and where do we go? Gifted Child Quarterly, 44, 152-170. doi:10.1177/001698620004400302 Sartory, G., Heine, A., Muller, B. W., & Elvermann-Hallner, A. (2002). Event- and motor-related potentials during the continuous performance task in attention deficit/hyperactivity disorder. Journal of Psychophysiology, 16, 97-106. Schatz, A. M., Ballantyne, A. O., & Trauner, D. A. (2001). Sensitivity and specificity of a computerized test of attention in the diagnosis of attention-deficit/hyperactivity disorder. Assessment, 8, 357-365. Thomas, M., Rostain, A., Corso, R., Babcock, T., & Madhoo, M. (2014). ADHD in the college setting: Current perceptions and future vision. Journal of Attention Disorders. Advance online publication. doi:10.1177/1087054714527789 Vaa, T. (2014). ADHD and relative risk of accidents in road traffic: A meta-analysis. Accident Analysis & Prevention, 62, 415-425. Webb, J. T., Amend, E. R., Webb, N. E., Goerss, J., Beljan, P., & Olenchak, F. R. (2005). Misdiagnosis and dual diagnoses
of gifted children and adults. Scottsdale, AZ: Great Potential Press. Wood, S. C. (2012). Examining parent and teacher perceptions of behaviors exhibited by gifted students referred for ADHD diagnosis using the Conners 3 (an exploratory study). Roeper Review, 34, 194-204. Yang, H., Tai, Y., Yang, L., & Gau, S. S. (2013). Prediction of childhood ADHD symptoms to quality of life in young adults: Adult ADHD and anxiety/depression as mediators. Journal of Developmental Disabilities, 34, 3168-3181.
Author Biographies David P. Hurford, PhD, is the director of the Center for READing at Pittsburg State University, which provides evaluations and interventions for dyslexia, reading difficulties, and ADHD. His research interests include phonological processing, developing assessment devices for dyslexia, evaluation of interventions for dyslexia, reading difficulties and ADHD and curriculum development that incorporates these areas to prevent reading failure. Alex C. Fender, MS, Clinical Psychology, is licensed as a psychological associate in North Carolina. She currently works for the Geneva Foundation as a behavioral outcomes assessor examining the predictive validity of suicide assessments in military personnel. Jordan L. Boux, BS, is working on her masters in clinical psychology at Pittsburg State University and is an intern at Wyandot Center providing outpatient therapy to uninsured adult clients. Courtney C. Swigart, EdS, is a school psychologist currently working as a school psychologist in Rose Hill School District, Butler County Interlocal. Paige S. Boydston, MS, is a psychologist II at Parsons State Hospital and Training Center working with adolescents and adults with developmental and intellectual disabilities; as well as an autism specialist for the southeast Kansas area serving children with autism in home and community based settings. Shanise R. Butts, MS, is a behavioral health professional at the El Dorado Kansas Correctional Facility. She conducts intakes and provides crisis therapy and mental health treatment to offenders located at this facility. Christy L. Cox, EdS in School Psychology, currently works with Topeka Seaman School District as a school psychologist. Trent A. Becker has received a bachelor of arts in psychology and is currently pursuing a bachelor of science in communications with an emphasis in broadcasting. Along with his interests in audio/video technology, he enjoys studying human theories of communication. Mary E. Pike, BS, is working as a mental health technician for Family and Children’s Services providing crisis services to adults with mental health and addiction issues in an inpatient setting.
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research-article2014
JADXXX10.1177/1087054714550480Journal of Attention DisordersHurford et al.
Article
Examination of the Effects of Intelligence on the Test of Variables of Attention for Elementary Students
Journal of Attention Disorders 1–9 © 2014 SAGE Publications Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/1087054714550480 jad.sagepub.com
David P. Hurford1, Alex C. Fender1, Jordan L. Boux1, Courtney C. Swigart1, Paige S. Boydston1, Shanise R. Butts1, Christy L. Cox1, Trent A. Becker1, and Mary E. Pike1
Abstract Objective: To examine the performance differences on the Test of Variables of Attention (TOVA) among different IQ level groups. Method: The present study examined the results of the TOVA with 138 elementary students aged 6 to 10 years who were assigned to one of four different groups based on their scores from the Wechsler Nonverbal Scale of Ability (WNV; low average: IQ < 90, average: IQ between 90 and 109, high average: IQ between 110 and 119, superior: IQ between 120 and 129, and very superior: IQ > 129). The latter two groups were combined. Results: On all TOVA measures (response time, response time variability, errors of omission and commission, and ADHD scores), intellectual functioning significantly influenced performance. Conclusion: The results of the present study indicate that performance on the TOVA was affected by intellectual functioning. (J. of Att. Dis. XXXX; XX(X) XX-XX) Keywords ADHD, children, assessment, psychometric properties, continuous performance tests Between 3% and 9% of youth and 2.5% to 4% of adults are estimated to experience symptoms of ADHD (American Psychiatric Association [APA], 2013; Barbaresi et al., 2013; Monastra, 2008; Pastor & Reuben, 2008). ADHD is a neurodevelopmental disorder, with symptoms presenting during childhood and potentially continuing through adulthood (APA, 2013; Dennis et al., 2009; Ozonoff & Jensen, 1999). Individuals experiencing inattention may be easily distracted, struggle to focus on instructions or tasks, and have difficulty completing and/or turning in assignments. Symptoms of hyperactivity can include difficulty sitting still and/or racing thoughts. Individuals experiencing impulsivity may inappropriately blurt out comments, often interrupt others, and have difficulty waiting their turn. These symptoms may be particularly detrimental for the education of roughly 4.5 million children experiencing symptoms of ADHD (Bloom, Cohen, & Freeman, 2009), as they can negatively impact children’s performance at school. In addition, attentional difficulties can also impact an individual’s functioning occupationally, socially, and at home. Proper diagnosis requires symptom presentation across multiple settings (APA, 2013). Identification of ADHD is important in providing appropriate treatment, but accurate diagnosis remains challenging. Differential diagnosis of ADHD is warranted given that comorbidity is very common with ADHD and accurate
diagnosis is critical for the development of treatment for individuals seeking assistance managing their symptoms. Oppositional Defiant Disorder, Conduct Disorder, Intermittent Explosive Disorder, other neurodevelopmental disorders, neurocognitive disorders, anxiety disorders, mood disorders, learning disorders, substance use disorders, as well as hearing or reading difficulties can present with symptoms and behaviors that closely mirror or overlap symptoms and behaviors that are consistent with ADHD (APA, 2013; Deans, O’Laughlin, Brubaker, Gay, & Krug, 2010; Monastra, 2008). When placed in unchallenging or under-stimulating academic environments, highly intelligent youth can behave in ways consistent with those with ADHD (Baum, Olenchak, & Owen, 1998; Reis & McCoach, 2000). In addition, it can sometimes be difficult to differentiate attentional difficulties from age-appropriate behaviors, such as running or frequent talking (Nelson, Rinn, & Hartnett, 2006; Webb et al., 2005; Wood, 2012).
1
Pittsburg State University, KS, USA
Corresponding Author: David P. Hurford, Center for Research, Evaluation and Awareness of Dyslexia, Pittsburg State University, 1701 S. Broadway, Pittsburg, KS 66762, USA. Email: [email protected]
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Journal of Attention Disorders
Children and adolescents with undiagnosed ADHD will likely continue experiencing difficulty in a variety of settings. For example, social acceptance has been traditionally thought to help protect children with ADHD from a variety of additional symptoms, such as depression. However, this may not be consistently accurate for individuals with ADHD (McQuade et al., 2014). College students with ADHD may have difficulty accessing appropriate, consistent treatment due to provider discomfort evaluating, diagnosing, and treating ADHD (Thomas, Rostain, Corso, Babcock, & Madhoo, 2014). Barbaresi and colleagues (2013) found that nearly 30% of their participants who were diagnosed with ADHD in childhood continued to experience symptoms as adults. Adults who experienced ADHD during childhood may also experience an increased risk of suicide, higher likelihood of having at least one other psychiatric disorder as an adult (Barbaresi et al., 2013), quality of life difficulties (Yang, Tai, Yang, & Gau, 2013), and an increased risk for traffic violations (Vaa, 2014). Utilization of empirically based assessment can help facilitate accurate diagnosis of ADHD. Many measures of attentional functioning rely on parent-, teacher-, and/or selfreport. Such rating scales can be helpful, as the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM5; APA, 2013) highlights the importance of symptom presentation in multiple settings and gathering information from multiple sources (APA, 2013), but there are a number of criticisms associated with them. For example, a rater’s relationship with the individual can influence his or her ratings, as can his or her understanding of the often vague and abstract terms that many self- and other-report measures include (Gupta & Kar, 2010). Results are based on the subjective perceptions of raters and, as such, may not always provide the most accurate depiction of an individual’s actual behavior (DuPaul, 2003). Research and best practices indicate that multiple informational sources are the best avenue for accurate diagnosis of ADHD (Forbes, 1998; Schatz, Ballantyne, & Trauner, 2001). Performance-based tests, though not without their own limitations, can serve as an additional source of information as they more objectively examine attentional abilities and help to reduce the impact of constructs such as rater bias. In addition, multiple sources of convergent information gained from subjective measures, such as self-reported, parent and teacher ratings, and objective measures, such as Continuous Performance Tests (CPTs), increase diagnostic accuracy. The Test of Variables of Attention (TOVA) is one such CPT that examines attentional functioning and is frequently used in the diagnosis of ADHD (Gualtieri & Johnson, 2005). It offers both visual and auditory measures of attention using targets, to which individuals are instructed to respond, and non-targets, to which individuals are instructed to withhold their responses. Research supports the use of the TOVA as a diagnostic tool, along with other measures,
in the diagnosis of ADHD (Forbes, 1998; Schatz et al., 2001). During the administration of the TOVA, the individual responds to the presence of the target and refrains from responding to the non-target. The first half of the TOVA consists of a relatively low ratio of target to non-target presentations (1 target to 3.5 non-targets), which proves challenging for individuals with inattention; the second half consists of a relatively high ratio of target to non-target presentations (3.5 targets to 1 non-target), which proves challenging for those with impulsive and hyperactive behaviors (Leark, Greenberg, Kindschi, Dupuy, & Hughes, 2007). Previous research has examined the effects of administering the TOVA and other CPTs in different settings, including varying locations (Bart, Raz, & Dan, 2014) and times of day (Hurford, Lasater, Erickson, & Kiesling, 2013), with no significant differences; thus, CPTs may be, with clinical consideration, administered in various settings and times (afternoon as well as morning administrations). Because of its use of geometric shapes rather than letters of the alphabet, the TOVA may be an especially attractive diagnostic supplement when assessing children, individuals with learning disabilities, and individuals whose first language is not English (Schatz et al., 2001). Administration of the TOVA results in five scores: 1— errors of omission, 2—errors of commission, 3—response time, 4—response time variability, and 5—ADHD score. An omission error occurs when an individual does not respond to a target and is considered a measure of inattention. A commission error occurs when the examinee responds to a non-target and is considered a measure of impulsivity. Response time measures response latency, whereas response time variability measures consistency and is also the variable that accounts for 80% of the variability between individuals with and without ADHD (Leark et al., 2007). An ADHD score is also provided and offers a comparison of the examinee’s performance to those in the standardization sample who were diagnosed with ADHD. The ADHD score is comprised of three of the measures produced by the TOVA tests expressed as z scores: first half response time, total response time variability, and second half d′ score (zResponse Time Half 1 + zd′ Half 2 × −1 + zResponse Time Variability Total). The d′ measure is derived from Signal Detection Theory and indicates response sensitivity (Green & Swets, 1966). The TOVA provides summary data by quarter for each of the variables listed above, which is quite useful for tracking potential changes in performance. Children with ADHD typically demonstrate more omission and commission errors than their peers without ADHD (Sartory, Heine, Muller, & Elvermann-Hallner, 2002). Response time variability accounts for more than 80% of the variability between children with and without ADHD (Greenberg, Kindschi, Dupuy, & Hughes, 2007). The ADHD score helps to discriminate between individuals
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Hurford et al. Table 1. Demographic Characteristics of the Participants by Group. Group Variable n Age (range) Female/male
LA
A
HA
S/VS
14 7.51 (5.72-9.65) 5/9
78 7.98 (5.87-9.91) 38/40
27 7.84 (5.87-10.40) 16/11
19 7.63 (5.95-9.35) 12/7
Note. LA = low average; A = average; HA = high average; S = superior; VS = very superior.
90
NUMBER OF PARTIICPANTS
identified by the other scores as having difficulty maintaining attention (e.g., due to anxiety, depression, and/or sleep difficulties) from individuals with clinical difficulties that warrant a diagnosis of ADHD. It has been suggested that individuals with ADHD experience difficulty with various cognitive processes (see Gupta & Kar’s, 2010, discussion). Pharmacotherapy treatment for attentional difficulties has a questionable impact on cognitive processes (Hellwig-Brida, Daseking, Keller, Petermann, & Goldbeck, 2011). Previous research has indicated that youth with ADHD obtain Full Scale IQ (FSIQ) scores an average of 9 points lower than their peers (Frazier, Demaree, & Youngstrom, 2004). Level of intelligence has been shown to impact performance on Conners’ CPT; in fact, researchers found intelligence to be a better predictor of performance on the Conners’ CPT than the presence or absence of an ADHD diagnosis (Munkvold, Manger, & Lundervold, 2014). Understanding the relationship between intelligence and CPT performance is crucial for clinicians to better interpret CPT results, such as those delivered by the TOVA. The effect of intelligence on the TOVA has not previously been empirically examined and is the focus of the present article. When the normative sample was created, the authors of the TOVA did not evaluate the participants’ intellectual functioning. As a result, the relationship between performance on the TOVA and intellectual functioning is not known. Information included in the TOVA Clinical Manual concerning interpretation of the TOVA has the following caveat: “assuming average intelligence” (Greenberg et al., 2007, p. 23). This is based on an assumption that intelligence will have an impact on attentional functioning, and therefore on their performance on the TOVA. The present study examined this assumption of the importance of intellectual functioning and performance on the TOVA. It has been assumed that individuals with higher levels of intelligence will perform significantly better on the TOVA than individuals with lower levels of intelligence. There have been no studies that have empirically tested this assumption. As a result, it is not known whether attentional abilities, as assessed by the TOVA, vary as a function of intellectual abilities. The present study addressed this question with elementary-grade students, as they are most likely to be assessed with the TOVA as a component of the diagnostic process.
80 70 60 50 40 30 20 10 0 Low Average
Average
High Average
Superior
Very Superior
GROUP
Figure 1. The distribution of intelligence scores (WNV) by group. Note. WNV = Wechsler Nonverbal Scale of Ability.
Method Participants One hundred thirty-eight students enrolled in Midwest elementary schools in the United States participated in the present study (see Table 1). Participants were assigned to four groups based on their performance on the Wechsler Nonverbal Scale of Ability (WNV) and classified based on Wechsler’s scale, which included the probable error (low average: less than 90, average: 90-109, high average: 110119, superior: 120-129, and very superior: greater than 129). The primary purpose of the study was to examine the potential effect of intelligence on performance on the TOVA. As a result, four groups were formed based on the participants’ intelligence: low average (WNV scores less than 90; n = 14), average (WNV scores between 90 and 109; n = 78), high average (WNV scores between 110 and 119; n = 27), and superior (WNV scores between 120 and 129; n = 19). As can be seen in Figure 1, there were also four individuals who were in the very superior range (WNV scores 130 and above), but due to the small number of individuals in this group, they were combined with the superior group [(nvery superior = 4) + (nsuperior = 15) = 19]. A 4 (Group) × 2 (Gender) ANOVAs were performed on age, which
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Journal of Attention Disorders and controlled attention assessed during the second half. In addition, the sensitivity and specificity measures are adequate (Lowe, 2001). Data from the TOVA will be analyzed by quarter. The TOVA was presented on Dell Latitude D810 laptop computers. These laptops have 39.1 cm screens. Students sat approximately 51 cm away from the laptop and responded by depressing a handheld thumb switch with their dominant hand that is included within the TOVA kit.
Figure 2. The target (left) and the non-target (right) for the TOVA. Note. TOVA = Test of Variables of Attention.
indicated that there were no main effects of group, F(3, 137) = 0.75, p > .53, gender, F(1, 137) = 0.01, p > .53, p > .92, or interaction of group and gender, F(3, 137) = 1.85, p > .14. A chi-square analysis was performed, which indicated that there were no significant differences in the number of males and females between groups, χ2(3) = 3.32, p > .34.
Tasks and Materials TOVA. The present study used Version 7.3 of the TOVA. The TOVA is a CPT used to assess attention which is 22.6 min in length. Administration begins with verbal instructions regarding the nature of the task. After the verbal instructions are given, a 3-min practice session occurs. Participants are provided with feedback regarding their ability to comprehend the nature of the task during the 3-min practice session, but not thereafter. The TOVA has acceptable psychometric properties (Leark, Wallace, & Fitzgerald, 2004; Llorente et al., 2001). The target is comprised of a white square with a small black square contained within it centered near the top of the white square (see Figure 2). The non-target is also a white square, but the black square contained within it is centered near the bottom of the white square. The location of the small black square within the larger white square differentiates the target from the non-target. The participant is instructed to fixate on a small dot in the middle of the screen and the target or non-target is presented for 100 ms centered above the fixation dot. The inter-target interval is 2 s. The 22.6-min duration of the TOVA is comprised of 648 trials. The target density of the first half of the TOVA is 1 target for 3.5 presentations of the non-target such that the participant responds infrequently during first half. The target density of the second half of the TOVA is 3.5 targets for each non-target such that the participants respond quite frequently during the second half. The first half of the TOVA taps inattention whereas the second half taps impulsivity. Validity and reliability have been well established for the TOVA not only with regard to the overall test but also considering sustained attention as assessed during the first half
WNV. The WNV is a nonverbal measure of ability that measures intellectual abilities exclusively through nonverbal items. It is comprised of six subtests (Matrices, Coding, Object Assembly, Recognition, Spatial Span, and Picture Arrangement). The full battery for individuals aged 8 years 0 months to 21 years 11 months consists of Matrices, Coding, Spatial Span, and Picture Arrangement. Those were the subtests used to determine the intellectual functioning of the participants in the present study. There is a relatively high level of comorbidity of ADHD and dyslexia. Approximately, 30% to 50% of children with dyslexia also have ADHD. As the WNV evaluates intellectual ability nonverbally, it is perceived as a more accurate measure for children who have verbal and language deficiencies, which is often the case with children who have dyslexia or reading difficulties. As a result, the WNV was selected as the measure for intelligence due to its ability to measure intelligence nonverbally. The WNV has adequate psychometric properties (Naglieri & Otero, 2012).
Procedure The participants were assessed with the TOVA in groups of 10 in a quiet, dimly-lit vacant facility in each building. Students wore MSA Safety Works PVC Foldable Ear Muffs (Model 10033236), which had a noise reduction level of 20 dB. The ear muffs were worn to reduce the potential for distraction by ambient noise during the assessment. As the classroom was already quiet, wearing the sound-reducing headphones decreased the likelihood that students would be distracted by random sounds (e.g., movement by other students, sneezing, or coughing, etc.). The students were arranged in the classroom such that they faced away from each other and were not able to see other students’ computer screens. The WNV was individually administered by research assistants who had coursework in advanced tests and measures, individual intelligence testing, standardization techniques and were specifically trained to administer the WNV.
Results and Discussion The present study was conducted to examine the assumption that performance on the TOVA could be affected by
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Hurford et al.
Table 2. Mean Response Time Variability, Mean Response Time, Mean Commission Errors, Mean Omission Errors, and d′ Scores by Quarter and Group. Quarter Group Response time variability Superior High average Average Low average Response time Superior High average Average Low average Commission errors Superior High average Average Low average Omission errors Superior High average Average Low average d′ Superior High average Average Low average
Q1
Q2
Q3
Q4
158.0 (41.6) 137.6 (40.8) 181.4 (88.0) 205.9 (100.5)
179.5 (56.3) 171.9 (61.6) 204.0 (89.7) 248.0 (123.1)
171.0 (44.1) 194.1 (58.8) 232.0 (85.4) 258.9 (83.0)
206.2 (55.5) 231.8 (73.6) 260.0 (92.6) 285.3 (96.6)
528.0 (57.5) 547.4 (89.1) 567.0 (116.7) 686.0 (185.9)
606.4 (77.9) 602.8 (102.1) 631.0 (142.4) 719.5 (151.4)
452.8 (69.3) 483.5 (110.4) 507.0 (119.1) 607.0 (106.1)
465.9 (75.7) 519.3 (107.3) 515.0 (115.7) 604.8 (116.4)
5.0 (8.6) 4.4 (4.9) 10.5 (17.9) 11.4 (16.0)
2.6 (1.9) 6.3 (8.8) 7.8 (15.8) 10.5 (14.0)
11.1 (6.6) 10.1 (5.4) 12.8 (6.1) 10.2 (6.3)
15.1 (6.9) 11.5 (5.17) 13.9 (6.5) 10.2 (5.4)
0.9 (1.1) 2.3 (4.2) 4.2 (5.98) 8.0 (9.3)
1.9 (2.0) 4.4 (6.4) 6.0 (7.13) 9.4 (8.4)
6.7 (10.8) 9.1 (9.4) 15.4 (18.6) 27.2 (21.8)
7.1 (8.8) 16.3 (13.4) 21.6 (23.5) 36.8 (22.4)
5.2 (2.0) 5.4 (2.1) 4.0 (2.19) 3.5 (2.4)
4.6 (1.7) 4.4 (2.3) 3.9 (2.12) 3.2 (2.0)
3.1 (1.8) 2.6 (1.3) 2.0 (1.42) 1.8 (1.5)
2.5 (1.3) 2.0 (1.1) 1.51 (1.28) 1.1 (0.7)
Note. Values in parentheses are standard deviations.
intellectual ability. The TOVA produces a plethora of results including means for response time variability, response time, commission errors, omission errors, and d′. Response time variability is the metric that most strongly differentiates individuals who have attentional difficulties from those who do not. The primary concern of the present study was to determine whether intelligence played a role in TOVA performance, and if so, what variables were affected by intelligence. As a result, the raw scores for response time variability, response time, commission errors, omission errors, and d′ (see Table 2) were included in a 4 (Intelligence group) × 5 (TOVA measure) × 4 (Quarter) repeated-measures ANOVA with repeated measures on type of TOVA measure (e.g., response time variability, response time, commission errors, omission errors, and d′) and quarter (Quarter 1 vs. Quarter 2 vs. Quarter 3 vs. Quarter 4). The analysis reported utilized Greenhouse–Geisser Epsilon to correct for potential sphericity issues. The results indicated that there were a main effects of intelligence group, F(3, 123) = 6.56, p < .0004, ω2 = .12; TOVA measure,
F(4, 492) = 2,021.24, p < .0001, ω2 = .94; and quarter, F(3, 369) = 16.35, p < .0001, ω2 = .11; and two interactions, Group × TOVA measure, F(12, 492) = 4.66, p < .0001, ω2 = .01, and TOVA measure × Quarter, F(12, 1476) = 71.39, p < .0001, ω2 = .001. The main effect involving TOVA measure was due to the different metrics of those measures. Response time and response time variability have similar values given that that their unit of measure concerns milliseconds with mean values ranging from 171 to 719 across groups and quarters. Mean commission and omission errors also have similar metrics, which range from .88 to 16, and are different from response time variability, response time, and d′ (ranging from 1.3 to 5.4). As a result, the main effect due to TOVA measure is not considerably informing; however, their interactions are quite informative. The main effects of group and quarter will be described by TOVA measure. As can be seen in Figure 3 and substantiated with Student–Newman–Keuls post hoc analyses, the superior/ very superior and high average intelligence groups had smaller response time variability means than did
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800
MEAN RESPONSE TIME (ms)
MEAN RESPONSE TIME VARIABILITY (ms)
300 250 200 150 S/VS
100
HA A
50 0
LA
1
2
3
700 600 500 400 S/VS
300
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A LA
100 0
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Note. S = superior; VS = very superior; HA = high average; A = average; LA = low average.
the average and low average intelligence groups across quarters. Response time variability also increased each quarter. The interaction stems from the superior/very superior group’s small response time variability means for the third and fourth quarters relative to their first and second quarters and all other groups. The most important result of the response time variability data concerns the finding that intelligence plays a significant role in performance. The low average group performed the least well and the superior/very superior and high average groups performed the most efficiently. Student–Newman–Keuls post hoc analyses also indicated that the low average intelligence group had the longest response time means across all quarters compared with the other groups. The average, high average, and superior/ very superior groups were not significantly different across quarters (see Figure 4). Interestingly, the Group × Quarter interaction for mean commission errors was a result of the high average and superior/very superior group having very low commission errors for the first two quarters followed by relatively high rates of commission errors for the last two quarters. The low average and average groups had relatively more commission errors for the first quarter, which lowered for these groups. The last two quarters for the average group were relatively high and relatively low for the low average group, respectively. Interestingly, the first half of the TOVA is fairly target scarce in which there is 1 target for every 3.5 non-targets. The second half of the TOVA is target dense in which there are 3.5 targets to every 1 non-target. As a result, commission errors are more likely to occur during the second half, which is what was seen in all groups except for the low average group. In fact, the relative ratio of commission
4
Figure 4. Mean response time by quarter and intelligence group.
Note. S = superior; VS = very superior; HA = high average; A = average; LA = low average.
16
MEAN COMMISSION ERROR
Figure 3. Mean response time variability by quarter and intelligence group.
3
QUARTER
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14 12 10 8 S/VS
6
HA 4
A LA
2 0 1
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Figure 5. Mean commission errors by quarter and intelligence group.
Note. S = superior; VS = very superior; HA = high average; A = average; LA = low average.
errors rose across quarters with the level of intelligence. The ratio of increase in commission errors from the first, target scarce, to the second half, target dense (Half 2 Commission Errors/Half 1 Commission Errors) was 0.93 for the low average group, 1.5 for the average group, 1.9 for the high average group, and 3.4 for the superior/very superior group (see Figure 5). The opposite result occurred for the errors of omission. Although the low average group had the most errors of commission at any quarter, the four groups performed relatively similarly for the first target-scarce half. As the groups
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Hurford et al.
6 S/VS
35
S/VS
5
HA
HA
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40
A 25
LA
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Figure 6. Mean omission errors by quarter and intelligence group.
Note. S = superior; VS = very superior; HA = high average; A = average; LA = low average.
progressed further into the second half, the number of errors of omission increased. The increase was related to level of intelligence in that the superior/very superior group had the fewest omission errors followed by the high average and average groups and then the most errors were generated by the low average group (see Figure 6). Errors of omission occur when the participant fails to respond to a target. The opportunity to make errors of omission is much less likely in the first half of the TOVA, which is the target-scarce condition. Fewer targets are presented and the task involves identifying the target when it is present. Low levels of errors of omission would seem much more likely in a condition in which targets are encountered infrequently, and the data support that notion. The opportunity to make omission errors is much more likely in the target-dense condition of the second half. The results indicate that level of intelligence was an important variable in accurately indicating that the target was present in a high target-dense condition. The final TOVA measure examined was d′, which is a measure of response sensitivity. TOVA uses d′ as a measure of performance decrement and is calculated by determining the ratio of hits to false alarms. The higher the d′ score, the more accurately the participant identified the target. As can be seen in Figure 7, this measure was also influenced by level of intelligence. The superior/very superior and high average groups outperformed the average and low average groups across quarters. Target sensitivity decreased across quarter for all groups, but most notably from the first to the second half in which there is a change in task. The first half is the target-scarce condition in which there are fewer targets to identify, and the second half is the target-dense condition in which there are many targets to identify. Under these conditions, it is expected that d′ values would decrease
2
3
4
QUARTER
Figure 7. Mean d′ score by quarter and intelligence group.
Note. S = superior; VS = very superior; HA = high average; A = average; LA = low average.
during the second half. However, intelligence level played a role in performance with this TOVA measure as well. The TOVA also produces an ADHD score that compares the participant’s performance to a group from the standardization sample comprised of individuals with ADHD. The ADHD score is computed as a combination of z scores associated with response time for the first half, d′ for the second half, and response time variability for the entire assessment (zResponse Time Half 1 + zd′ Half 2 × −1 + zResponse Time Variability Total). A one-way ANOVA was performed on the ADHD scores by group, which indicated that there were significant differences between groups, F(3, 126) = 7.66, p < .0001. The means and standard deviations for the ADHD scores were −0.17 (2.16), −1.1 (2.37), −2.7 (2.86), and −4.1 (3.12) for the superior/very superior, high average, average, and low average groups, respectively. An ADHD score less than −1.81 is considered to be “Not within the Normal Range” and indicates a likelihood of ADHD. As a result, nearly twice as many individuals in the average and low average groups had ADHD scores in the “Not within the Normal Range” as did the high average and superior/very superior groups. As can be seen in Table 3 and substantiated with a chi-square analysis, as intelligence level decreases, the more likely that individuals will have ADHD scores in the Not Within Normal Limits range; χ2(3) = 8.17, p < .04. Thus, individuals in the average and low average intelligence levels would be more likely to be considered as potentially having ADHD than individuals in the high average and superior intelligence levels based on the results of the TOVA. Thus, the level of intelligence plays a significant role in ADHD scores. Based on the results of the present study, it appears that the assumption that intelligence level is an important mediator with regard to the TOVA performance was supported. It is
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Table 3. Participants Whose ADHD Scores Classified as With or Not Within Normal Limits by Group. Group Superior/very superior High average Average Low average
Within normal limits
Not within normal limits
12 15 30 3
7 12 48 11
recommended that clinicians assessing children between the ages of 6 and 10 years consider the participant’s intellectual functioning during the interpretation of the TOVA results. Future research will need to address specifically the manner in which the results of the TOVA should be interpreted with regard to intellectual functioning. Acknowledgment The authors would like to thank the students, teachers, and administrator from Unified School District (USD) 250 in Pittsburg, Kansas, for their willingness to support this study, and Katie Hoover, Ryan Johnson, Evan Adams, Hannah Straw, Autumn Howe, Bekah Orendak, Erin Rivero, and Mila Infanti for their assistance with data collection.
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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Author Biographies David P. Hurford, PhD, is the director of the Center for READing at Pittsburg State University, which provides evaluations and interventions for dyslexia, reading difficulties, and ADHD. His research interests include phonological processing, developing assessment devices for dyslexia, evaluation of interventions for dyslexia, reading difficulties and ADHD and curriculum development that incorporates these areas to prevent reading failure. Alex C. Fender, MS, Clinical Psychology, is licensed as a psychological associate in North Carolina. She currently works for the Geneva Foundation as a behavioral outcomes assessor examining the predictive validity of suicide assessments in military personnel. Jordan L. Boux, BS, is working on her masters in clinical psychology at Pittsburg State University and is an intern at Wyandot Center providing outpatient therapy to uninsured adult clients. Courtney C. Swigart, EdS, is a school psychologist currently working as a school psychologist in Rose Hill School District, Butler County Interlocal. Paige S. Boydston, MS, is a psychologist II at Parsons State Hospital and Training Center working with adolescents and adults with developmental and intellectual disabilities; as well as an autism specialist for the southeast Kansas area serving children with autism in home and community based settings. Shanise R. Butts, MS, is a behavioral health professional at the El Dorado Kansas Correctional Facility. She conducts intakes and provides crisis therapy and mental health treatment to offenders located at this facility. Christy L. Cox, EdS in School Psychology, currently works with Topeka Seaman School District as a school psychologist. Trent A. Becker has received a bachelor of arts in psychology and is currently pursuing a bachelor of science in communications with an emphasis in broadcasting. Along with his interests in audio/video technology, he enjoys studying human theories of communication. Mary E. Pike, BS, is working as a mental health technician for Family and Children’s Services providing crisis services to adults with mental health and addiction issues in an inpatient setting.
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