View in EDS HTML Full Text PDF Full Text

Factor Structure of the Conners Continuous Performance Test Third Edition (CCPT-3): Exploratory Factor Analysis in a Mixed Clinical Sample

Saved in:
Bibliographic Details
Title: Factor Structure of the Conners Continuous Performance Test Third Edition (CCPT-3): Exploratory Factor Analysis in a Mixed Clinical Sample
Language: English
Authors: Olaf Lund (ORCID 0000-0003-0305-0559), Rune Raudeberg (ORCID 0000-0003-0919-6479), Hans Johansen, Mette-Line Myhre, Espen Walderhaug (ORCID 0000-0002-0115-9596), Amir Poreh, Jens Egeland (ORCID 0000-0002-7322-9266)
Source: Journal of Attention Disorders. 2025 29(13):1163-1176.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 14
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Factor Structure, Factor Analysis, Attention Span, Measures (Individuals), Performance Tests, Attention Deficit Hyperactivity Disorder, Computer Assisted Testing, Conceptual Tempo, Reaction Time, Brain
Assessment and Survey Identifiers: Continuous Performance Test
DOI: 10.1177/10870547251341928
ISSN: 1087-0547
1557-1246
Abstract: Objective: The Conners Continuous Performance Test-3 (CCPT-3) is a computerized test of attention frequently used in clinical neuropsychology. In the present factor analysis, we seek to assess the factor structure of the CCPT-3 and evaluate the suggested dimensions in the CCPT-3 Manual. Method: Data from a mixed clinical sample of 931 adults referred for neuropsychological assessment across four centers were analyzed. Nine standard and eight experimental measures were subjected to an exploratory factor analysis to evaluate factor models ranging from one to six factors. Results: The analysis supported a four-factor model with one overall attention factor and three factors of distinct mechanisms underlying inattention: impulsivity, vigilance, and sustained attention. This closely aligns with the four dimensions outlined in the CCPT-3 Technical Manual and the factor analyses from the CCPT-II. There were some differences between the four-factor model and the interpretations recommended in the Technical Manual. Perseverations were associated with the inattention factor rather than the impulsivity factor, and reaction time was exclusively linked to impulsivity. Incorporating error measures into the vigilance factor suggests that decreases in responsivity, rather than decreases in correct responses, underpin vigilance decrements. Including response bias by inter-stimulus interval (ISI) and by blocks in the analysis indicates that a decrease in arousal may also explain impairments in sustained attention. Conclusion: This study supports the notion in the Technical Manual that CCPT-3 measures both overall attention and three different mechanisms that mediate inattention: impulsivity, vigilance and sustained attention.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1485122
Database: ERIC
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
    Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwHO7hzABmX06P6qPHhBa5O6AAAA4jCB3wYJKoZIhvcNAQcGoIHRMIHOAgEAMIHIBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDNWNhV3SwBgnBCWeDwIBEICBmqI46grYVp8OXM6yPL4LJ4V3krLK5k20EttR5Lp3ZLcKm2D_gOLgfXVjLEfqUyS9YSest3ysklIyWy6AQbOya_7P3QcbQfRtTOS8HEq91qESLTbmoH81OsUr51anldtiKNrldxDPVTPwHLOZHaCcZ5F_uH7tIq7KCQYoRi7PHb7LdwYEzLbfGKjKEmTnlzB69UwwzTKmelkU4Qo=
Text:
  Availability: 1
  Value: <anid>AN0188320981;gs001nov.25;2025Oct01.06:51;v2.2.500</anid> <title id="AN0188320981-1">Factor Structure of the Conners Continuous Performance Test Third Edition (CCPT-3): Exploratory Factor Analysis in a Mixed Clinical Sample </title> <p>Objective: The Conners Continuous Performance Test-3 (CCPT-3) is a computerized test of attention frequently used in clinical neuropsychology. In the present factor analysis, we seek to assess the factor structure of the CCPT-3 and evaluate the suggested dimensions in the CCPT-3 Manual. Method: Data from a mixed clinical sample of 931 adults referred for neuropsychological assessment across four centers were analyzed. Nine standard and eight experimental measures were subjected to an exploratory factor analysis to evaluate factor models ranging from one to six factors. Results: The analysis supported a four-factor model with one overall attention factor and three factors of distinct mechanisms underlying inattention: impulsivity, vigilance, and sustained attention. This closely aligns with the four dimensions outlined in the CCPT-3 Technical Manual and the factor analyses from the CCPT-II. There were some differences between the four-factor model and the interpretations recommended in the Technical Manual. Perseverations were associated with the inattention factor rather than the impulsivity factor, and reaction time was exclusively linked to impulsivity. Incorporating error measures into the vigilance factor suggests that decreases in responsivity, rather than decreases in correct responses, underpin vigilance decrements. Including response bias by inter-stimulus interval (ISI) and by blocks in the analysis indicates that a decrease in arousal may also explain impairments in sustained attention. Conclusion: This study supports the notion in the Technical Manual that CCPT-3 measures both overall attention and three different mechanisms that mediate inattention: impulsivity, vigilance and sustained attention.</p> <p>Keywords: CPT-3; continuous performance test; factor analysis; attention; ADHD</p> <hd id="AN0188320981-2">Introduction</hd> <p>Conners Continuous Performance Test—version 3 (CCPT-3: [<reflink idref="bib13" id="ref1">13</reflink>]) is a neuropsychological test of attention that is frequently used in clinical neuropsychology and in research. The previous version (Conners Continuous Performance Test—version II; CCPT-II) was one of the most used neuropsychological tests of attention in the United States ([<reflink idref="bib50" id="ref2">50</reflink>]) and in the Nordic countries ([<reflink idref="bib21" id="ref3">21</reflink>]). The CCPT-3 claims to give specific information about different types of attention deficits ([<reflink idref="bib13" id="ref4">13</reflink>]). However, the generation of multiple scores in the standard output can make an overall interpretation difficult. Therefore, there is a need to determine whether it is possible to reduce the number of scores to a limited set of meaningful dimensions that can be reliably assessed. With other tests that produce numerous scores it is common to use dimension reductions techniques to assess construct validity supporting the clinical interpretation (e.g., intelligence tests). One approach to improve the interpretations of a test and understand the latent structures that underlie test performance is to use factor analysis to evaluate the structural validity ([<reflink idref="bib26" id="ref5">26</reflink>]). The Technical Manual ([<reflink idref="bib13" id="ref6">13</reflink>]) states that CCPT-3 measures four dimensions of attention: inattentiveness, impulsivity, sustained attention, and vigilance. These dimensions were theoretically derived but have a basis in earlier investigations of the structural validity of the CCPT-II ([<reflink idref="bib19" id="ref7">19</reflink>], [<reflink idref="bib20" id="ref8">20</reflink>]). Others that have used dimension reduction techniques on CCPT-II are [<reflink idref="bib64" id="ref9">64</reflink>], [<reflink idref="bib1" id="ref10">1</reflink>], [<reflink idref="bib11" id="ref11">11</reflink>], and [<reflink idref="bib31" id="ref12">31</reflink>], but to our knowledge, there is currently only one study that analyzes the structural validity of CCPT-3 ([<reflink idref="bib24" id="ref13">24</reflink>]).</p> <p>[<reflink idref="bib19" id="ref14">19</reflink>], utilized a Principal Component Analysis (PCA), and identified five distinct CPT-II factors: focus, hyperactivity/impulsivity, sustained attention, vigilance, and change in control. These factors accounted for 74.4% of the variance observed within the CCPT-II results. [<reflink idref="bib31" id="ref15">31</reflink>] also performed a PCA on the CCPT-II measures. They found some similarities to [<reflink idref="bib19" id="ref16">19</reflink>], but also remarked that the factor structure of CCPT-II can be specific to diagnostic groups. [<reflink idref="bib1" id="ref17">1</reflink>] used PCA on a large sample of 3,226 adults and found a factor structure that overall was consistent with the four-factor structure used in the CCPT-3 manual. [<reflink idref="bib64" id="ref18">64</reflink>] showed that an exploratory factor analysis (EFA) with PCA in CPT-II across children with traumatic brain injury, mixed diagnoses, and normal controls gave a three-factor structure, despite initial poor fits from confirmatory factor analysis (CFA). In a sample of adolescents [<reflink idref="bib11" id="ref19">11</reflink>] demonstrated a component-pattern like [<reflink idref="bib19" id="ref20">19</reflink>]. In an attempt to link white matter microstructure to behavioral findings, [<reflink idref="bib24" id="ref21">24</reflink>] performed a factor analysis of CCPT-3 data in a smaller mixed sample of 171 children without a diagnosis, and children with ADHD or Autism Spectrum Disorder. They extracted only a general attentional skills factor and impulsive behavior, similar to the first two factors in the aforementioned PCA ([<reflink idref="bib19" id="ref22">19</reflink>]). In summary, current research presents conflicting evidence about the number of factors in the CCPT, with findings ranging from three to five distinct factors based on the test version and the demographics of the population studied. These discrepancies are probably enhanced by the variation in the number of indicators used across different studies.</p> <p>Prior to the CCPT-3, vigilance and sustained attention were viewed as synonymous ([<reflink idref="bib42" id="ref23">42</reflink>]). Vigilance was considered to be reduced over time. The systematic variation of stimulus intensity in the Conners' CPTs, as well as computing measures of change in performance as a function of time on task, discerned that these were two different sources of impaired performance. [<reflink idref="bib19" id="ref24">19</reflink>] found that reaction time by interstimulus-interval (ISI) and variation in reaction time by ISI loaded on a factor they called vigilance, whereas change in reaction time over time, change in variation of reaction time over time and change in the number of omissions over time, loaded on a sustained attention factor. This distinction is integrated in the revised third version of CCPT. However, as the new version also allows for comparing numbers of omission errors by ISI, no study has validated a vigilance factor where impaired performance with increasing ISI reaction time is associated with a similar change in omission errors. In this study, we take a rigorous approach by explicitly replicating the factor analysis from the previous CCPT-II conducted in 2010 ([<reflink idref="bib19" id="ref25">19</reflink>]), but using a common factor model. Given that there have been somewhat different results from studies using PCA in CCPT-II, an exploratory factor analysis was preferred over a confirmatory factor analysis ([<reflink idref="bib67" id="ref26">67</reflink>]). Our objective is to both validate the previous findings and factor structure, and also to explore potential improvements in test interpretation. In addition, we examine whether the differentiation between the dimensions of sustained attention and vigilance is reflected in the latent factors, and if both reaction time measures and quality of performance indicators converge on these factors.</p> <hd id="AN0188320981-3">Previous Research on Conners CPT and CPT</hd> <p>CCPT-3 is frequently used in the context of diagnosing ADHD and is considered a crucial part in assessing cognitive function in ADHD (e.g., [<reflink idref="bib23" id="ref27">23</reflink>]). [<reflink idref="bib45" id="ref28">45</reflink>], p. 2) examined the discriminant validity in ADHD diagnostics and found that "[a]ccording to five meta-analyses, Conners' CPT shows one of the largest test effect sizes in comparisons of adults with ADHD and normal adults...". However, a recent literature review of the use of CCPTs in an adult population with ADHD ([<reflink idref="bib44" id="ref29">44</reflink>]) criticizes the use of the test for diagnostic and treatment assessment and claim that it has moderate reliability, subpar discriminant and ecological validity, and mixed sensitivity and specificity. [<reflink idref="bib3" id="ref30">3</reflink>] have been more positive to the use of CPTs in general for identifying ADHD, but still have concerns, considering that CPTs only have a modest to moderate ability to differentiate ADHD from non-ADHD clinical samples. They recommend that CPTs should only be used as part of a full diagnostic process. [<reflink idref="bib12" id="ref31">12</reflink>] specifically reviewed the literature for the diagnostic use of CCPT-3 in persons with ADHD, reaching the same conclusion as [<reflink idref="bib3" id="ref32">3</reflink>]. Based on a limited number of studies, CCPT-3 could differentiate between ADHD and controls, but not ADHD with comorbid disorders. [<reflink idref="bib57" id="ref33">57</reflink>] reviewed the incremental diagnostic value of different measurements, and found that CPTs add further validity beyond what is obtained from self-reports and clinical interviews alone. [<reflink idref="bib54" id="ref34">54</reflink>] found that CPTs could differentiate patients with ADHD from other psychiatric patients when used in conjunction with clinical interview instruments. [<reflink idref="bib61" id="ref35">61</reflink>] pointed out that the predictive values of different CPT measures varies, and found that a machine learning model provided better classification accuracy than a standard analysis.</p> <p>The validity of CPT diagnostics of ADHD in children has also been examined. Both reviews by [<reflink idref="bib7" id="ref36">7</reflink>] and [<reflink idref="bib27" id="ref37">27</reflink>] concluded that CPT improves diagnostics of ADHD, and the latter group emphasized the utility of such an "objective" test for ADHD in children. However, different studies on CPTs show mixed sensitivity and specificity. For instance, a study by [<reflink idref="bib33" id="ref38">33</reflink>] indicates that relying solely on a TOVA cut-off score for inclusion might exclude some children who nonetheless meet clinical criteria for ADHD.</p> <p>One reason for the mixed evidence in the sensitivity and specificity of CCPT is that it provides specific data on cognitive dysfunction but does not fully capture the real-world behavioral attention difficulties that individuals experience. This lack of ecological validity is often interpreted as a need for integrating neurocognitive and behavioral rating measures in assessment ([<reflink idref="bib33" id="ref39">33</reflink>]; [<reflink idref="bib62" id="ref40">62</reflink>]).</p> <p>The problem with measuring attention to help the diagnostic process is that such impairments occur not only with ADHD, but with a host of different diagnoses such as schizophrenia and bipolar disorder ([<reflink idref="bib9" id="ref41">9</reflink>]) as well as in anxiety ([<reflink idref="bib53" id="ref42">53</reflink>]) and personality disorders ([<reflink idref="bib40" id="ref43">40</reflink>]). There is also a connection between eye conditions and attention problems. For example different problems with focusing or eye alignment can resemble symptoms of ADHD ([<reflink idref="bib8" id="ref44">8</reflink>]; [<reflink idref="bib48" id="ref45">48</reflink>], [<reflink idref="bib47" id="ref46">47</reflink>]). For methodological reasons, reviews and meta-analyses must analyze attention as an overall ability, instead of using profile analyses to discern prototypical ADHD impulsivity driven inattention from, for example, psychosis-related inattention ([<reflink idref="bib18" id="ref47">18</reflink>]). Thus, using attention as an overall ability would be analogous to researchers applying the Fullscale IQ measure from the Wechsler IQ tests to differentiate between subjects with nonverbal disorders and language disorders, instead of using the indexes measuring verbal and perceptual skills. We take the latter approach to CCPT-3, which also claims to measure four dimensions of attention. The CCPT-3 profile is the target of interest in this study, and this will be illustrated through findings from existing research in this area. [<reflink idref="bib17" id="ref48">17</reflink>] compared patients with ADHD-combined (ADHD-C), ADHD-inattentive (ADHD-I), and schizophrenia. All three groups were similarly impaired on overall signal detection measures, but ADHD-C was characterized by hyperactivity-driven inattention, ADHD-I by difficulties with sustained attention, and schizophrenia by a lack of initial attention and improvement as the task became automated. In fact, a study validating the factor structure of CCPT-II, [<reflink idref="bib20" id="ref49">20</reflink>] found that all clinical groups (i.e., patients with ADHD-C and ADHD-I, brain injury, affective disorders, schizophrenia) were similarly impaired on focused attention. The study evaluated all five factors, but found that four of them were sufficient to create unique impairment profiles. Therefore, it is not surprising that meta-studies or reviews focusing solely on the overall inattention factor, or in combination with hyperactivity and impulsivity, show inconsistent results in differentiating between clinical groups. This highlights the importance of considering the complete profile of impairments for accurate differentiation. A methodological approach favoring multidimensional analyses of CCPT performance relies on a correct understanding of which potential dimensions are actually being measured, which confirms the necessity of factor analytic research.</p> <p>CCPT-3 differs from most other CPTs by varying event-rates, that is, stimulation intensity. A fall in reaction time with longer event-rate is considered specific for ADHD ([<reflink idref="bib2" id="ref50">2</reflink>]; [<reflink idref="bib6" id="ref51">6</reflink>]). CCPT-3 also varies from other CPTs by offering measures of change in reaction time as a function of time on task. Thus, regardless of initial or overall reaction time, the test gives measures of change that are central to the notion of a particular reduction in sustaining attention, instead of only measuring overall attention on the test.</p> <p>Several researchers have found that variations in performance are more sensitive measures of attention than number of response errors, which historically have been the most reported measures for CPTs in meta-studies and reviews ([<reflink idref="bib32" id="ref52">32</reflink>]; [<reflink idref="bib44" id="ref53">44</reflink>]; [<reflink idref="bib56" id="ref54">56</reflink>]). The CCPT-3 offers two measures of intra-test variation in response time, namely the standard deviation of the mean reaction time and variability which measures variation between the 18 sub-blocks composing the test. For now, the standard interpretation of the test offers no measure of variability in error-rate, which preferably should converge with measures of response time variability. In the present factor analysis, we compute measures of change in response style by blocks and ISIs. The centrality of event-rate differences, process measures and variability measures in CCPT may yield information to differentiate between clinical conditions involving attention deficit not given by other CPTs ([<reflink idref="bib2" id="ref55">2</reflink>]; [<reflink idref="bib10" id="ref56">10</reflink>]). However, error rates are robust measures and whether the more subtle process measures of CCPT-3 actually improve measures of theoretical constructs such as vigilance, sustained attention and hyperactivity/impulsivity must be demonstrated. While both CCPT-II and CCPT-3 provide measures of change in reaction time over time, only the CCPT-II had a measure of change in the standard deviation of reaction time over time. The test then measured not only whether a person became slower over time, but also whether they became more variable over time, which gave further validity to the test's ability to measure sustained attention. However, on an individual level, this measure was found to be unstable and unreliable and was omitted in CCPT-3. In a factor analysis of a large sample, it is nonetheless important to the notion of sustained attention to analyze whether a general slowing is related to increased stability or increased variability.</p> <p>To summarize, the CCPT-3 intends to measure four aspects of attention, but this notion has not been investigated. This study will assess the underlying dimensions in CCPT-3 by replicating the factor analysis conducted on the previous version CCPT-II ([<reflink idref="bib19" id="ref57">19</reflink>]). The objective is to evaluate the theoretical separation into the four dimensions presented in the Technical manual, and explore improvements in test interpretation. Therefore, the proposed change in the interpretation of the test's structure from CCPT-II to CCPT-3, along with the addition of experimental scores, necessitate an assessment of the CCPT-3's structural validity.</p> <hd id="AN0188320981-4">Method</hd> <p></p> <hd id="AN0188320981-5">Participants</hd> <p>The dataset consists of test records from 941 patients sequentially referred for neuropsychological assessment over a period from 2019 to 2021. These patients were from four different health institutions: a hospital trust's Division of Physical Medicine and Rehabilitation and Habilitation Center, a university neuropsychological clinic, and a health service-financed private practice. The national health authority has granted the project an exception from the duty of confidentiality, and the project was approved by the local institutional review board.</p> <p>From the entire sample, 10 participants were excluded from the analysis due to omission errors in entire subblocks of the test. There were no other concerns about the validity of the CCPT-3 administration. Performance validity tests were used at the time of the initial neuropsychological assessment if the patient was involved in litigation or suspected for malingering. Patients scoring below established thresholds for insufficient validity were not included in the project. The final sample of 931 patients consisted of 468 males and 463 females. Mean age was 34.9 years (range = 16–82 years, <emph>SD</emph> = 14.6). The average General Ability Index (GAI; equivalent to IQ but without processing speed and working memory measures) from the Wechsler Adult Intelligence Scale-version IV ([<reflink idref="bib68" id="ref58">68</reflink>]) was 90.5 (range = 42–141, <emph>SD</emph> = 18.4). GAI was missing for 177 participants. See Table 1 for the diagnostic composition of the sample.</p> <p>Table 1. Diagnostic Composition of the Sample.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Diagnosis</th><th align="center"><italic>n</italic></th><th align="center">Percentage</th></tr></thead><tbody><tr><td>Normal function</td><td>115</td><td>12.35</td></tr><tr><td>ADHD</td><td>95</td><td>10.20</td></tr><tr><td>Intellectual disability</td><td>92</td><td>9.88</td></tr><tr><td>General learning difficulties</td><td>88</td><td>9.45</td></tr><tr><td>Anxiety disorder</td><td>79</td><td>8.49</td></tr><tr><td>Language disorders</td><td>56</td><td>6.02</td></tr><tr><td>Somatic conditions</td><td>51</td><td>5.48</td></tr><tr><td>Cerebrovascular disease</td><td>42</td><td>4.51</td></tr><tr><td>Traumatic Brain Injury</td><td>37</td><td>3.97</td></tr><tr><td>CNS disorder</td><td>36</td><td>3.87</td></tr><tr><td>Depression disorder</td><td>33</td><td>3.54</td></tr><tr><td>Fatigue</td><td>28</td><td>3.01</td></tr><tr><td>Encephalitis, meningitis</td><td>24</td><td>2.58</td></tr><tr><td>Nonverbal learning disorder</td><td>24</td><td>2.58</td></tr><tr><td>Cerebral Palsy</td><td>20</td><td>2.15</td></tr><tr><td>Substance abuse</td><td>18</td><td>1.93</td></tr><tr><td>Bipolar disorder</td><td>17</td><td>1.83</td></tr><tr><td>Visual dysfunction</td><td>17</td><td>1.83</td></tr><tr><td>Epilepsy</td><td>16</td><td>1.72</td></tr><tr><td>Autism</td><td>12</td><td>1.29</td></tr><tr><td>Multiple Sclerosis</td><td>11</td><td>1.18</td></tr><tr><td>ADHD remission</td><td>10</td><td>1.07</td></tr><tr><td>Psychotic Disorder</td><td>10</td><td>1.07</td></tr><tr><td>Total</td><td>931</td><td>100.00</td></tr></tbody></table> </ephtml> </p> <p>The diagnoses were primarily categorized based on the principal diagnosis provided by the referring authority prior to the neuropsychological examination. However, the categories intellectual disability and learning disorders (general learning difficulties, language disorders, and nonverbal learning disorder) could be changed based on the current assessment.</p> <p>Some patients in the ADHD group were first diagnosed with ADHD after neuropsychological assessment, but they are listed as having ADHD only if the diagnosis has been corroborated after diagnostic evaluation from multidisciplinary teams in the specialist health services. The small group of ADHD in remission, refers to patients who were diagnosed with ADHD in childhood, but without indications to uphold the diagnosis. In these cases, the present neuropsychological assessment was part of the decision-making process. The classification of somatic condition, cardiovascular disease, known medical disorder to Central Nervous System (CNS), Traumatic Brain Injury (TBI), fatigue, encephalitis, meningitis, tic born encephalitis (TBE), visual dysfunction, epilepsy, and multiple sclerosis was based on information from medical records or information in the referral.</p> <p>Depressive disorder, anxiety disorder, substance abuse, bipolar disorder, autism, and psychotic disorder were mostly diagnosed by certified clinical psychologists or psychiatrists prior to neuropsychological assessment. For depressive disorder or anxiety disorder the diagnoses were kept unless the assessment resulted in a learning disorder diagnosis considered more relevant in understanding patients' cognitive performance.</p> <p>A subgroup had normal cognitive performance despite being referred for subjective symptoms of cognitive impairment. This classification was only given in cases where there was no known medical or psychiatric prior diagnosis, although some of these could probably qualify for mild anxiety. Some patients with a medical or psychiatric disorder performed within normative range and were classified according to diagnosis prior to assessment.</p> <hd id="AN0188320981-6">Measures</hd> <p>CCPT-3 is a computerized test where the participants respond to any letters on the screen by pressing the space bar, except for the letter X. The time between the presentation of the letters is either 1, 2, or 4 s, which is called the inter-stimulus interval (ISI). Each letter is presented for 250 ms, and the total number of letters (trials) is 360. The test is divided into six blocks, each with three sub-blocks of 20 trials. Within each block, the sub-blocks have different order of the ISIs conditions. The total time of the test is 14 min and the standard output gives 10 normed-referenced measures (see Table 2).</p> <p>Table 2. CCPT-3 Measures.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /></colgroup><thead><tr><th align="left">Measures</th><th align="center">Description</th></tr></thead><tbody><tr><td>C</td><td>Response style</td></tr><tr><td><italic>d</italic>′</td><td>How well the respondent discriminates non-targets from targets. Computed from omissions and commissions.</td></tr><tr><td>Omissions</td><td>Missed targets</td></tr><tr><td>Commissions</td><td>Incorrect responses to non-targets</td></tr><tr><td>Perseverations</td><td>Responses made less than 100 ms after presentation of a stimulus</td></tr><tr><td>Hit reaction time</td><td>Mean response time</td></tr><tr><td>Hit reaction time standard deviation</td><td>Consistency of response time for the entire test</td></tr><tr><td>Variability</td><td>Consistency of response time between the 18 sub-blocks</td></tr><tr><td>HRT block change</td><td>Change in response time across six blocks</td></tr><tr><td>HRT inter-stimulus interval (ISI) change</td><td>Change in response time across ISI</td></tr></tbody></table> </ephtml> </p> <hd id="AN0188320981-7">Procedure and Analyses</hd> <p></p> <hd id="AN0188320981-8">Variable Selection</hd> <p>To select the variables for inclusion in the analysis, the correlation matrix of all the age and gender corrected scores from the standard output (<emph>T</emph>-score) were inspected. In addition to the measures reported in Table 2, we computed eight new measures from the raw scores to include information from the omissions, commissions, standard deviations, and response style (C) across blocks and ISI. The eight measures were calculated by fitting a linear regression line, and the slope (beta coefficient) was used as a comprehensive measure of the development of omissions, commissions, standard deviations, or response style across the six blocks or three ISI.</p> <p>The two slope measures of the reaction time standard deviations will complement the standard measures of HRT Block change and HRT ISI change. Subjects that perform slower later in the test or during the longest ISI, may do so either because they become consistently slower, or because they become more variable. The increased variation due to some slow responses increases the slope of the standard deviations in reaction time.</p> <p>The <emph>d</emph>′ variable was omitted from the analysis because the information is included in the omissions and the commission variables, and we would not anticipate whether these two measures actually load on the same factor. The <emph>d</emph>′ measure can also be problematic for measures that have unequal base rates, as in CCPT-3 with a target to non-target ratio of 4:1 ([<reflink idref="bib4" id="ref59">4</reflink>]). The <emph>d</emph>′ measure does not take into account the probability of the stimulus, and it is not known if a different probability of target to non-target affects the <emph>d</emph>′ ([<reflink idref="bib52" id="ref60">52</reflink>]).</p> <hd id="AN0188320981-9">Exploratory Factor Analysis</hd> <p>The data analysis was performed with R ([<reflink idref="bib49" id="ref61">49</reflink>]). Appropriateness of the data for factor analysis was assessed with The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy, and if Bartlett's test of sphericity indicated a significant departure from the identity matrix. The variables were also inspected for sufficient pair-wise correlation, outliers (univariate and multivariate), and multicollinearity.</p> <p>Multiple criteria were examined to determine numbers of factors to retain, using the n_factors function from the package parameters ([<reflink idref="bib36" id="ref62">36</reflink>]), which has a function that runs many existing procedures for determining how many factors to retain from factor analysis. It returns the number of factors based on the maximum consensus between methods. To analyze the sample correlation matrix, we used regularized factor analysis with regularized least squares estimation and oblimin rotation, implemented in the function "fareg" from the R-package fungible ([<reflink idref="bib65" id="ref63">65</reflink>]). This method was chosen to address Heywood cases that appeared in some of the factor solutions, as recommended by [<reflink idref="bib14" id="ref64">14</reflink>]. A threshold for significant factor loading was set for coefficients greater than.32 (explaining approximately 10% of the variance in a factor). The different factor solutions were assessed for their statistical robustness, as well as theoretical meaning and practical relevance in a clinical context. We required that the factors should have two or more salient loadings, but they were also allowed to have salient cross loadings.</p> <hd id="AN0188320981-10">Results</hd> <p>Descriptive statistics for the CCPT-3 measures used in the factor analysis are shown in Table 3. The standard output measures are reported as age- and gender-corrected <emph>T</emph>-scores. The computed measures are unstandardized beta coefficients and are not age- or gender-corrected. Raw scores are shown in Table 4.</p> <p>Table 3. Descriptive Statistics for the CCPT-3 Measures.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Measures</th><th align="center">Mean</th><th align="center"><italic>SD</italic></th><th align="center">Min</th><th align="center">Max</th><th align="center">Skewness</th><th align="center">Kurtosis</th></tr></thead><tbody><tr><td>C</td><td>47.74</td><td>11.15</td><td>16.00</td><td>86.00</td><td>0.21</td><td>0.18</td></tr><tr><td>Omissions</td><td>52.85</td><td>11.61</td><td>42.00</td><td>90.00</td><td>1.77</td><td>2.43</td></tr><tr><td>Commissions</td><td>55.70</td><td>11.61</td><td>35.00</td><td>88.00</td><td>0.40</td><td>−0.51</td></tr><tr><td>Perseverations</td><td>54.23</td><td>13.28</td><td>44.00</td><td>90.00</td><td>1.66</td><td>1.65</td></tr><tr><td>Hit reaction time</td><td>50.79</td><td>10.51</td><td>28.00</td><td>90.00</td><td>0.88</td><td>1.29</td></tr><tr><td>Hit reaction time standard deviation</td><td>53.06</td><td>11.50</td><td>30.00</td><td>90.00</td><td>1.08</td><td>1.22</td></tr><tr><td>Variability</td><td>52.71</td><td>11.52</td><td>35.00</td><td>90.00</td><td>1.34</td><td>1.56</td></tr><tr><td>HRT block change</td><td>49.94</td><td>10.31</td><td>0.00</td><td>90.00</td><td>0.09</td><td>1.70</td></tr><tr><td>HRT inter-stimulus interval (ISI) CHANGE</td><td>48.72</td><td>9.73</td><td>0.00</td><td>90.00</td><td>0.03</td><td>1.43</td></tr><tr><td>Omissions block change</td><td>0.19</td><td>1.05</td><td>−3.57</td><td>9.35</td><td>3.22</td><td>19.90</td></tr><tr><td>Commissions block change</td><td>−0.57</td><td>3.67</td><td>−14.29</td><td>20.24</td><td>0.26</td><td>1.52</td></tr><tr><td>Standard deviation block change</td><td>2.93</td><td>15.18</td><td>−70.54</td><td>130.64</td><td>2.49</td><td>17.51</td></tr><tr><td>Omissions ISI change</td><td>−0.82</td><td>1.72</td><td>−10.57</td><td>6.99</td><td>−2.29</td><td>9.61</td></tr><tr><td>Commissions ISI change</td><td>0.74</td><td>5.51</td><td>−17.56</td><td>22.32</td><td>0.11</td><td>0.19</td></tr><tr><td>Standard deviation ISI change</td><td>6.17</td><td>27.13</td><td>−42.03</td><td>246.60</td><td>3.79</td><td>20.19</td></tr><tr><td>C ISI change</td><td>−0.07</td><td>0.12</td><td>−0.64</td><td>0.35</td><td>−0.24</td><td>0.87</td></tr><tr><td>C block change</td><td>0.02</td><td>0.06</td><td>−0.29</td><td>0.35</td><td>0.14</td><td>1.72</td></tr></tbody></table> </ephtml> </p> <p>Table 4. CCPT-3 Raw Scores.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Measures</th><th align="center">Mean</th><th align="center"><italic>SD</italic></th><th align="center">Min</th><th align="center">Max</th><th align="center">Skewness</th><th align="center">Kurtosis</th></tr></thead><tbody><tr><td>C</td><td>−0.90</td><td>0.32</td><td>−1.75</td><td>0.18</td><td>0.23</td><td>0.12</td></tr><tr><td>Omissions (%)</td><td>2.58</td><td>3.82</td><td>0.00</td><td>34.38</td><td>2.64</td><td>9.58</td></tr><tr><td>Commissions (%)</td><td>38.31</td><td>20.61</td><td>0.00</td><td>95.83</td><td>0.37</td><td>−0.63</td></tr><tr><td>Perseverations (%)</td><td>0.44</td><td>1.03</td><td>0.00</td><td>12.50</td><td>5.27</td><td>39.26</td></tr><tr><td>Hit reaction time (ms)</td><td>420.94</td><td>75.25</td><td>272.35</td><td>867.36</td><td>1.19</td><td>2.95</td></tr><tr><td>Hit reaction time standard deviation (log)</td><td>0.24</td><td>0.07</td><td>0.11</td><td>0.72</td><td>1.71</td><td>5.10</td></tr><tr><td>Variability (log)</td><td>0.06</td><td>0.03</td><td>0.02</td><td>0.26</td><td>1.90</td><td>4.91</td></tr><tr><td>HRT block change (log)</td><td>0.00</td><td>0.02</td><td>−0.10</td><td>0.16</td><td>0.47</td><td>3.94</td></tr><tr><td>HRT inter-stimulus interval (ISI) change (log)</td><td>0.04</td><td>0.03</td><td>−0.20</td><td>0.18</td><td>−0.28</td><td>3.62</td></tr></tbody></table> </ephtml> </p> <hd id="AN0188320981-11">Testing Suitability for Factor Analysis</hd> <p>To identify outliers, we used the Performance package ([<reflink idref="bib37" id="ref65">37</reflink>]) with the Robust <emph>z</emph>-score method (with a threshold of 3). There were several univariate outliers of the 931 cases: in Omissions ISI Change (20.1%), Perseverations (17.7%), Omissions (17.6%), Omissions Block Change (13.1%), Standard Deviation ISI Change (9.9%), Standard Deviation Block Change (7.4%), and Variability (5.6%). Additionally, there were some outliers in Hit Reaction Time (3.0%), Hit Reaction Time Standard Deviation (2.8%), HRT Block Change (2.1%), HRT ISI Change (1.6%), C Block Change (1.6%), C ISI Change (1.0%), Commissions Block Change (0.8%), C (0.6%), and Commissions ISI Change (0.6%). There were no outliers with regard to Commissions.</p> <p>Assessing multivariate outliers, we used Minimum Covariance Determinant, a robust version of the Mahalanobis distance ([<reflink idref="bib35" id="ref66">35</reflink>]) and detected 226 outliers. This corresponds to 24.3% of the cases. We examined the outlier cases through the analysis of minimum and maximum values to identify any errors and found that all the values were within the "allowed" range. Consequently, we decided against removing participants' results solely because they were outliers ([<reflink idref="bib34" id="ref67">34</reflink>]). Nevertheless, we acknowledge the importance of a cautious interpretation of the results. Given that numerous measures showed outliers or skewness, we conducted a supplementary analysis using Spearman's rank correlation matrix for factor analysis. This approach is notably more resilient to non-normality and outliers, as it relies on rank ordering ([<reflink idref="bib16" id="ref68">16</reflink>]; [<reflink idref="bib66" id="ref69">66</reflink>]). The results from this analysis are reported in the Supplemental Material section: Factor Analysis with Spearman Correlation Matrix. To check if a bifactor solution would be appropriate, we estimated bifactor solutions with SL-procedure, in EFA-tools (from the same regularized factor-analysis). These various bifactor solutions (with two to six group factors) demonstrate that forcing the data from the CPT-3 into a bifactor structure does not produce a theoretically meaningful model. Although some solutions show a stronger general factor (<emph>g</emph>) component than others, none consistently provide compelling evidence that the CPT-3 indicators form a well-defined general dimension of performance alongside interpretable, distinct subfactors. These results suggest that a bifactor model may not be the best representation of the underlying structure of the CCPT-3 (see Supplemental Material).</p> <p>The Kaiser-Meyer-Olkin measure of sampling adequacy yielded an overall Measure of Sampling Adequacy of 0.58, indicating a subpar level of suitability for factor analysis ([<reflink idref="bib29" id="ref70">29</reflink>]). Among the individual measures, Perseverations demonstrated the highest suitability with an MSA value of 0.87. In order from highest to lowest, C (0.47), C ISI Change (0.47), Commissions (0.46), C Block Change (0.44), Omissions Block Change (0.42), Commissions ISI Change (0.37), and Commissions Block Change (0.35) showed the least suitability.</p> <p>The correlations varied between the 0 and.81 in the 136 unique correlation pairs. There were 27 pairs (19.85%) with a correlation above.3, indicating several significant associations. There were no correlations over.90 which would suggest multicollinearity ([<reflink idref="bib66" id="ref71">66</reflink>]). Bartlett's test of sphericity indicated a significant departure from the identity matrix (χ² = 10,761.90, <emph>df</emph> = 136, <emph>p</emph> <.001), suggesting the appropriateness of the data for factor analysis ([<reflink idref="bib5" id="ref72">5</reflink>]).</p> <p>As seen in Table 3 univariate skew and kurtosis was assessed. Skew >2.0 or kurtosis >7.0 would indicate severe univariate non-normality ([<reflink idref="bib15" id="ref73">15</reflink>]). Some of the measures were univariate non-normal, and hence Mardias multivariate test for skewness and kurtosis ([<reflink idref="bib38" id="ref74">38</reflink>]) also both rejected the null hypothesis (<emph>p</emph> <.0001).</p> <hd id="AN0188320981-12">The Factor Analysis</hd> <p>The <emph>n</emph>_factors function suggested five dimensions which were supported by 4 (21.05%) methods out of 19 (optimal coordinates, Parallel analysis, Kaiser criterion, and VSS complexity 1). See Supplemental Figure S1 for Parallel Analysis Scree plot. The CCPT-3 Technical manual suggests four dimensions. Given that the results were inconclusive and that it is better to over-extract ([<reflink idref="bib66" id="ref75">66</reflink>]) we started by extracting six factors, and then examined factor solutions with five, four, three, two, and one factor(s).</p> <hd id="AN0188320981-13">Six-Factor and Five-Factor Model</hd> <p>The six-factor and five-factor model both gave a solution where the fifth and sixth factor had only one strong loading, which could suggest over-extraction. The six-factor and five-factor solution explained 64% and 61% variance respectively (see Supplemental Tables S1 and S2).</p> <hd id="AN0188320981-14">Four Factor Model</hd> <p>The four-factor model is shown in Table 5. The four factors explain a total of 57% variance: Factor 1 explains 22%, Factor 2 explains 12%, Factor 3 explains 12%, and Factor 4 explains 11%. The first factor has strong loadings (over.60) from Variability, Hit Reaction Time SD, Omissions, and Perseverations. It captures aspects related to consistency and response variability, and successful results in the task. Factor 2 has a strong negative loading from Commissions and strong loadings from C and Reaction time. This factor might represent a tendency for fewer commission errors at slower reaction time and a conservative response style, or the opposite a fast and impulsive response style with many commissions. The third factor is characterized by strong loading on C ISI Change, representing changes in response style across different ISI-levels, and a strong negative loading on Commissions ISI Change. The fourth factor has high loading from C Block Change, suggesting this factor captures changes in response style across blocks. There were cross-loadings from several of the variables, suggesting a complex structure. Factor correlations were small, the largest being −.24 between the first and the third factor.</p> <p>Table 5. Four Factor Model.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Measures</th><th align="center">F1</th><th align="center">F2</th><th align="center">F3</th><th align="center">F4</th><th align="center">Communality</th></tr></thead><tbody><tr><td>HRT SD</td><td><bold>0.88</bold></td><td>0.06</td><td>0.03</td><td>−0.02</td><td>0.77</td></tr><tr><td>Variability</td><td><bold>0.84</bold></td><td>−0.05</td><td>−0.05</td><td>−0.02</td><td>0.73</td></tr><tr><td>Omissions</td><td><bold>0.72</bold></td><td>0.14</td><td>−0.22</td><td>0.08</td><td>0.69</td></tr><tr><td>Perseverations</td><td><bold>0.67</bold></td><td>−0.14</td><td>−0.10</td><td>−0.06</td><td>0.50</td></tr><tr><td>SD ISI change</td><td><bold>0.58</bold></td><td>0.08</td><td>0.17</td><td>0.07</td><td>0.34</td></tr><tr><td>SD block change</td><td><bold>0.44</bold></td><td>−0.05</td><td>0.13</td><td>0.24</td><td>0.25</td></tr><tr><td>Omissions block change</td><td><bold>0.42</bold></td><td>−0.01</td><td>0.15</td><td><bold>0.42</bold></td><td>0.37</td></tr><tr><td>C</td><td>0.25</td><td><bold>0.83</bold></td><td>−0.10</td><td>0.04</td><td>0.81</td></tr><tr><td>Commissions</td><td><bold>0.45</bold></td><td><bold>−0.82</bold></td><td>−0.11</td><td>−0.01</td><td>0.86</td></tr><tr><td>HRT</td><td>0.20</td><td><bold>0.71</bold></td><td>0.05</td><td>0.00</td><td>0.56</td></tr><tr><td>C ISI change</td><td>−0.10</td><td>−0.04</td><td><bold>0.88</bold></td><td>0.02</td><td>0.83</td></tr><tr><td>Commissions ISI change</td><td>−0.09</td><td>−0.12</td><td><bold>−0.77</bold></td><td>0.09</td><td>0.58</td></tr><tr><td>HRT ISI change</td><td><bold>0.37</bold></td><td>0.04</td><td><bold>0.52</bold></td><td>−0.01</td><td>0.32</td></tr><tr><td>Omissions ISI change</td><td><bold>−0.33</bold></td><td><bold>−0.34</bold></td><td><bold>0.45</bold></td><td>0.11</td><td>0.51</td></tr><tr><td>C block change</td><td>0.07</td><td>−0.01</td><td>0.02</td><td><bold>0.92</bold></td><td>0.85</td></tr><tr><td>Commissions block change</td><td>0.19</td><td>−0.04</td><td>0.10</td><td><bold>−0.78</bold></td><td>0.63</td></tr><tr><td>HRT block change</td><td>0.17</td><td>0.02</td><td>−0.01</td><td><bold>0.45</bold></td><td>0.25</td></tr><tr><td>Proportion variance</td><td>0.22</td><td>0.12</td><td>0.12</td><td>0.11</td><td /></tr></tbody></table> </ephtml> </p> <p>1 Factor loadings greater than.32 are presented in bold.</p> <hd id="AN0188320981-15">Three-Factor Model</hd> <p>Running the three-factor model gave a solution that explained a total of 47% variance (see Supplemental Table S3): Factor 1 explains 24%, Factor 2 explains 12%, Factor 3 explains 11%. The first factor has strong loadings from Hit Reaction Time SD, Variability, Omissions, and Perseverations. The second factor has the strongest loadings with a negative loading from Commissions and positive loading from C and reaction time. The third factor has one strong loading from C Block Change. There were cross-loadings from four of the variables.</p> <hd id="AN0188320981-16">Two-Factor Model</hd> <p>The two-factor model (presented in the Supplemental Table S4) gave a solution that explained a total 37% variance: The first factor explains 24% and the second factor explains 13%. The first factor has strong loadings from Hit Reaction Time SD, Variability, Omissions, and Perseverations. The second factor has the strongest loadings with a negative loading from Commissions and positive loading from C and reaction time (just below.60). There were four variables with no salient loadings on any factors, and cross loading on Commissions and C.</p> <hd id="AN0188320981-17">Unidimensionality</hd> <p>Fitting a one-dimensional solution did not give a good fit to the data and explained 25% variance. There were seven measures that had a loading under 0.32 (available in the Supplemental Table S5).</p> <hd id="AN0188320981-18">Discussion</hd> <p>The present study sought to assess the underlying dimensions in CCPT-3, replicating earlier factor analytic research on CCPT-II, and investigate the theoretical separation in four dimensions described in the CCPT-3 Technical manual. The results of the factor analysis indicated that the four-factor structure is the best fit for interpreting CCPT-3 results. The four factors resemble those from the Technical manual, generally supporting this division. However, the concepts of sustained attention and vigilance require reconsideration.</p> <p>None of the factor models had a clear simple structure, and each had cross-loadings on some of the measures. The six- and five-factor models had only one strong loading on the last factor(s). The four-factor solution showed moderate explained variance and approached a "simple structure," with no measures lacking significant loadings on any factors, and each factor having four or more substantial loadings, including at least two strong ones. However, the three-factor structure also showed comparable fit, but with less explained variance and a less interpretable factor structure. In the three-factor solution the separate sustained attention- and activation related vigilance factors became inseparable from the overall inattentiveness factor. Both the two-factor and the one-factor solution showed symptoms of under-extraction and had low explained variance. The unidimensional model in particular had little support. Consequently, the CCPT-3 should be interpreted as measuring different underlying dimensions, and the measures from the CCPT-3 cannot be summarized into one overall measure.</p> <p>Table 6 gives an overview of the differences between the four-factor structure of the Technical manual and the present findings. The Technical manual recommends Detectability, Omissions, Commissions, HRT, HRT SD, and Variability as indicators of inattentiveness. The measures HRT, Commissions, and Perseverations are considered indicators of impulsivity. HRT Block Change is an indicator of sustained attention, supported by a qualitative interpretation of changes in standard deviations, omissions, and commissions per block. And lastly, the Technical manual considers HRT ISI Change as an indicator of vigilance, supported by interpretations of changes in standard deviation, omissions, and commissions per ISI Condition. The C-measure is recommended as an overall evaluation of the person's response style.</p> <p>Table 6. Comparison of Technical Manual Dimensions and Simplified Four-Factor Model.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left">Dimension</th><th align="center">Technical manual</th><th align="center">Current factor analysis</th></tr></thead><tbody><tr><td rowspan="6">Inattentiveness</td><td>HRT SD</td><td>HRT SD</td></tr><tr><td>Variability</td><td>Variability</td></tr><tr><td>Omissions</td><td>Omissions</td></tr><tr><td>Detectability (<italic>d</italic>′)</td><td>Perseverations</td></tr><tr><td>Commissions</td><td>SD ISI change</td></tr><tr><td>HRT</td><td>SD block change</td></tr><tr><td rowspan="3">Impulsivity</td><td>Perseverations</td><td>C</td></tr><tr><td>Commissions</td><td>Commissions</td></tr><tr><td>HRT</td><td>HRT</td></tr><tr><td rowspan="4">Sustained attention</td><td align="center">—</td><td>C block change</td></tr><tr><td>Commissions by block</td><td>Commissions block change</td></tr><tr><td>HRT block change</td><td>HRT block change</td></tr><tr><td>Omissions by block</td><td>Omissions block change</td></tr><tr><td rowspan="4">Vigilance</td><td align="center">—</td><td>C ISI change</td></tr><tr><td>Commissions by ISI</td><td>Commissions ISI change</td></tr><tr><td>HRT ISI change</td><td>HRT ISI change</td></tr><tr><td>Omissions by ISI</td><td>Omissions ISI change</td></tr></tbody></table> </ephtml> </p> <p>There was a general correspondence, but also differences between the first factor in the present four-factor model and the inattentiveness dimension in the Technical Manual. There were high loadings from Omissions, which are typically used as measures of inattention in CPT and reflect gross "lapses of attention" ([<reflink idref="bib25" id="ref76">25</reflink>]). The reaction time variability measures (Variability and HRT SD) also had high loadings on the inattention factor and have consistently been shown to be an important measure of individual differences in attentional abilities, particularly in ADHD ([<reflink idref="bib32" id="ref77">32</reflink>]). Significant variability in reaction times, especially with longer tails in the reaction time distribution, suggests loss of attention, similar to omission errors but is probably more sensitive to minor lapses not resulting in omissions ([<reflink idref="bib25" id="ref78">25</reflink>]). Reaction time variability was likewise found to have high loadings on the first factor in CPT-II ([<reflink idref="bib11" id="ref79">11</reflink>]; [<reflink idref="bib19" id="ref80">19</reflink>]). [<reflink idref="bib31" id="ref81">31</reflink>] and [<reflink idref="bib1" id="ref82">1</reflink>] found that the variability measures loaded on a similar factor, but as a second component. Perseverations loaded on the first factor and not the second in our sample. In the Technical manual perseverations are considered indicative of impulsivity. [<reflink idref="bib1" id="ref83">1</reflink>] and [<reflink idref="bib11" id="ref84">11</reflink>] also found that perseverations had a high loading on the inattentiveness dimension, where [<reflink idref="bib19" id="ref85">19</reflink>] and [<reflink idref="bib31" id="ref86">31</reflink>] found that they had a cross-loading between second component "inattention" and the first component "impulsivity." However, perseverations are probably a measure that depend on the specific sample characteristics, and it is rare for non-clinical populations to have perseveration errors. [<reflink idref="bib19" id="ref87">19</reflink>] argued for labeling it as "repetitions" because it was not specifically associated with executive dysfunction, and had to be differentiated from the traditional meaning of the concept in clinical neuropsychology. In our sample, given its associations with increased variability, the more likely causes of perseveration errors are slow responses to a preceding stimulus or random responses that reflect low engagement in the task. In children, perseveration errors have been associated with both symptoms of inattention and hyperactivity in ADHD ([<reflink idref="bib30" id="ref88">30</reflink>]). [<reflink idref="bib41" id="ref89">41</reflink>] found a pattern where children with either ADHD or dyslexia had more perseveration errors than a control group, with greatest impairment in the ADHD group.</p> <p>Furthermore, the first factor had a moderate positive loading from Standard Deviation ISI Change, and a small loading from Standard Deviation Block Change. It also has small loadings from measures with cross-loadings on other factors: omissions block change, hit reaction time, ISI Change, and negative loading from omissions ISI Change. Given that the first factor represents a general focused attention factor, the small loadings from the block and ISI change measures could reflect that in order to be attentive, it is necessary to maintain attention also under low-intensity conditions and over time.</p> <p>Commission errors had a cross loading between the first inattentive factor and the second impulsivity factor. Commissions had a positive moderate loading on the first factor and a higher negative loading on the second factor. The splitting of Commissions into two factors supports the recommended interpretation from the manual, which includes Commissions both as a measure of inattentiveness and impulsivity. Our skepticism about including the detectability measure was only partly supported. It was not a clear splitting of omission and commission errors on different factors. Both [<reflink idref="bib19" id="ref90">19</reflink>] and [<reflink idref="bib1" id="ref91">1</reflink>] found that commission errors loaded solely on the impulsivity dimension.</p> <p>Interestingly, the reaction time measure did not load on the first inattentiveness factor in our sample or in [<reflink idref="bib19" id="ref92">19</reflink>] and [<reflink idref="bib1" id="ref93">1</reflink>]. This adds to the evidence that it is possible to have impairments in attention (more variable reaction time and less accurate performance) at different "baseline levels" of speed of reaction time. Whether there is a general slowing of reaction time in ADHD is not settled, but research using drift diffusion modeling ([<reflink idref="bib51" id="ref94">51</reflink>]) has found slow drift rate (an index of reduced processing efficiency) in ADHD in adults ([<reflink idref="bib43" id="ref95">43</reflink>]) and children ([<reflink idref="bib22" id="ref96">22</reflink>]). This reflects an error-prone responding and less efficient speed accuracy trade-off, and not necessarily slow speed as measured in reaction time. Thus, a divergence between speed and accuracy vs variability measures is not rare, and one should avoid interpreting a normal reaction time as a dependable indicator of a "normal" attention capability per se. There is evidence for the relative less importance of reaction time as a neurocognitive marker for ADHD ([<reflink idref="bib46" id="ref97">46</reflink>]).</p> <p>The second factor showed a high negative loading on commission errors, and a high positive loading from response style (more conservative style) and slower reaction time. The converse of this style, that is, many commission errors, fast reaction, and consequently a liberal speed-accuracy trade-off reflected in the response style measure is considered indicators of impulsivity in the manual. Impulsivity is a suboptimal adjustment and balance of speed and accuracy, where fast responding leads to a great number of commission errors. This pattern with excessive commission errors is found to be an important measure of treatment and experimental effects in persons with ADHD ([<reflink idref="bib63" id="ref98">63</reflink>]), but shows mixed findings as an indicator of different ADHD presentations distinguishing hyperactive/impulsive from inattentive presentation ([<reflink idref="bib17" id="ref99">17</reflink>]; [<reflink idref="bib63" id="ref100">63</reflink>]). However high numbers of commission errors are consistently used as valid measures of difficulties with response inhibition and are reliable found to be a salient neurocognitive factor in ADHD ([<reflink idref="bib46" id="ref101">46</reflink>]).</p> <p>Overall, the present study provides robust support for a distinction between inattention and impulsivity in that these two factors are found across the examined factor solutions in our sample. It is parallel to the finding from [<reflink idref="bib19" id="ref102">19</reflink>] and [<reflink idref="bib1" id="ref103">1</reflink>], supporting the importance of CPT as a multifaceted measure.</p> <hd id="AN0188320981-19">Reconsidering Vigilance and Sustained Attention</hd> <p>The third and fourth factors include event-rate measures (vigilance) and measures of time on task (sustained attention) respectively. The concepts of sustained attention and vigilance are important parts of an assessment of attention. When interpreting the test scores, it is crucial to assess whether HRT Block Change and HRT ISI Change are valid and sensitive measures of fatigue (e.g., general slowing) and arousal, and if they are associated with more variable responding. Both of these factors measure the subject's activation, whether this is due to loss of activation because of time on task or because of a less activating stimulus presentation.</p> <p>The third and fourth factors are related to energetic brain states ([<reflink idref="bib39" id="ref104">39</reflink>]) that is, subjects with impaired arousal regulation are particularly prone to have arousal that is too low for optimal performance later on in the test or under less activating stimulus conditions. Both the Moderate Brain Arousal Model ([<reflink idref="bib60" id="ref105">60</reflink>]) and the Cognitive Energetic Model ([<reflink idref="bib58" id="ref106">58</reflink>]) claim that suboptimal brain arousal mediates the attention deficit in ADHD. The confirmation of separate vigilance and sustained attention factors in CCPT-3 in this factor analysis indicates a potential for identifying these processes and differentiates them from other processes underlying attention deficit in other disorders.</p> <p>Finding that the key measure of vigilance in the Technical manual (i.e., HRT ISI Change) loads on the third factor, and that this factor also receives strong loadings from other measures of vigilance such as Response style ISI Change and a strong negative loading from Commissions ISI Change, lends support for a true vigilance dimension in attention, but may demand some rethinking of the concept. In the CCPT-II, [<reflink idref="bib19" id="ref107">19</reflink>] found that longer and more variable response times during the low-activating 4 second ISI indicated vigilance decrement. Unfortunately, they had no opportunity to study changes in response accuracy for CCPT-II. As CCPT-3 offers data on omissions and commissions by ISI, there was an expectation that a true vigilance decrement, evident from reaction time measures, would also be paralleled by more errors in responding: more omissions and more commissions. This expectation, however, must be revised: As the overall number of omission errors averaged only 2.5 in the sample, further dividing them by ISI created a ceiling effect, reducing the potential to measure vigilance changes. Still, this measure loaded moderately high on the vigilance factor. The numbers of commissions were in fact reduced on the longest ISI. Potentially, longer reaction time by ISI could reflect a strategy change: Slow reaction when it was possible (i.e., when ISI was longest) could reflect giving priority to correct responding. This seems improbable, as we then would expect to see not only fewer commissions but also fewer omissions. The high loading on the experimental variable of response style by ISI that measures the tendency to respond (regardless of false or true positive responses) bridges the seemingly opposite direction of omission and commission errors in this factor. The response pattern associated with vigilance does not reflect whether the subject manages to uphold correct responding, but that they manage to uphold responding. Thus, vigilance seems to reflect activation or arousal, as predicted by the arousal models of ADHD ([<reflink idref="bib39" id="ref108">39</reflink>]).</p> <p>The sustained attention factor had a similar pattern where later blocks were associated both with fewer commission and omission errors, reflected in the strong loading from change in response style. Again, this indicates that problems with sustaining attention result in less active responding, rather than less correct responding. This is as expected from arousal models of ADHD. In the study by [<reflink idref="bib19" id="ref109">19</reflink>] they used another measure of changes in omissions, the Delta Omission, dividing the omissions into thirds of the test. Exchanging the Omissions Block Change with a Delta Omission in our sample gave approximately the same factor solution, with no salient loadings on any factors. We also estimated changes in tau-parameters from the Ex-gaussian distribution as a function of time on task. In line with the reasoning that the long tail of variable reaction time could be a more sensitive measure of loss of attention. This alternative index of minor attentional lapses not resulting in omission errors produced the same overall factor-structure.</p> <hd id="AN0188320981-20">Limitations</hd> <p>The present analysis further reveals that the factor structure of CCPT-3 is not clearly defined and is sensitive to the factor analytical method. Different estimators and/or oblique rotations lead to different factor patterns or inadmissable solutions, raising concerns about the generalizability of the results to new samples. Two earlier attempts to validate the factor structure in CPT-II ([<reflink idref="bib1" id="ref110">1</reflink>]; [<reflink idref="bib64" id="ref111">64</reflink>]) failed to find a good fit for any factor structure using a more restrictive confirmatory factor analysis (CFA). In [<reflink idref="bib64" id="ref112">64</reflink>] the four factor CFA showed the "best fit" (but still with subpar fit indices). In a later follow-up analysis they report a three-component solution PCA to be the best fit. [<reflink idref="bib1" id="ref113">1</reflink>] found that 1-, 3-, 4-, and 5-factor solutions were misspecified, and therefore reported a PCA-analysis which is more "permissive" and is used for dimension reduction (rather than latent dimensions). They found a four-component solution comparable to our results, and overall to the dimensions in the Technical manual. [<reflink idref="bib64" id="ref114">64</reflink>] speculated that problems with fitting a factor structure to CPT were due to "inadequate sampling of the construct" and an overreliance on reaction time measures in their analysis, and in CPT in general. We included several measures of accuracy in the analysis but agree with the notion that CCPT-3 is essentially based on reaction time and accuracy measures that inherently restrict the sampling of the broader construct "attentional abilities". [<reflink idref="bib64" id="ref115">64</reflink>] also comments on problems that CPT measures reflect different capacities, or that they are complex, as high or low scores can reflect different attentional abilities. For example, HRT can both be a measure of inattention (slow responses) and impulsivity (a too fast response).</p> <p>Results from exploratory factor analysis are highly dependent on the included measures, and a clear pattern of inter-correlations leads to more stable factor solutions. We sought to validate the factor structure from [<reflink idref="bib19" id="ref116">19</reflink>], which is also reflected in the dimensions reported in the Technical Manual. However, the manual uses a different composition of indicators supporting the four dimensions. Having few variables per factor is unsatisfactory, and we also wanted to include potential indicators for the dimensions sustained attention and vigilance. This constrained the minimum number of variables to include. These indicators had small correlations with the standard output measures Block Change and ISI Change, reflecting less shared variance and/or different reliability. Also, the number of computed variables, and the way they are computed, presumably affect the factor structure, but we did not successfully find a way to include other measures that had high psychometric quality and that fully captured the dimensions of sustained attention and vigilance.</p> <p>The factor analysis is also affected by different sample characteristics. We sought to use a heterogeneous clinical sample, reflecting typical patients that are assessed with CCPT-3. Although 67% of the sample had at least one measure that diverged more than one standard deviation from normative mean, there were some measures that had less than ideal variation. Twenty-one percent had a perfect score on omissions (no missed responses on target trials), which leads to little variation and probably a "ceiling effect." This attenuates the correlation of omission with the other measures ([<reflink idref="bib55" id="ref117">55</reflink>]; [<reflink idref="bib59" id="ref118">59</reflink>]). The few omissions underscore the point made by [<reflink idref="bib28" id="ref119">28</reflink>]; omissions reflect gross inattention in adult samples, but reaction time variability is sensitive to minor fluctuations in attention. In a factor analysis this leads to "poor coverage" of the indicators to the underlying concept ("inattention"). Furthermore, if subjects with attention deficits exhibit a distinct factor structure compared to subjects without such deficits, then the factor structure may be non-invariant to these clinical differences or diagnostic status. This could lead to varying interpretations regarding which indicators accurately reflect the different dimensions. It is possible that the factor structure will vary according to diagnosis, and it should be assessed how these characteristics affect the inherent dimensions of the test.</p> <hd id="AN0188320981-21">Summary and Conclusions</hd> <p>This study supports the idea that CCPT-3 measures both overall attention, as well as three different mechanisms that mediate inattention, namely impulsivity, vigilance, and sustained attention. This closely aligns with the four dimensions outlined in the CCPT-3 Technical Manual, although there were some differences. Adding error measures to the vigilance factor based only on reaction time analyses in CCPT-II indicates that a reduction in responsivity rather than a reduction in correct responses underlies vigilance decrements. Adding response-bias by ISI and by blocks in the analyses indicates that a fall in arousal may explain impairments in sustained attention.</p> <hd id="AN0188320981-22">Supplemental Material</hd> <p>Graph: Supplemental material, sj-docx-1-jad-10.1177_10870547251341928 for Factor Structure of the Conners Continuous Performance Test Third Edition (CCPT-3): Exploratory Factor Analysis in a Mixed Clinical Sample by Olaf Lund, Rune Raudeberg, Hans Johansen, Mette-Line Myhre, Espen Walderhaug, Amir Poreh and Jens Egeland in Journal of Attention Disorders</p> <ref id="AN0188320981-23"> <title> References </title> <blist> <bibl id="bib1" idref="ref10" type="bt">1</bibl> <bibtext> Aduen P. A., Kofler M. J., Bradshaw C. P., Sarver D. E., Cox D. J. (2020). The role of top-down attentional control and attention-deficit/hyperactivity disorder symptoms in predicting future motor vehicle crash risk. Neuropsychology, 34(8), 894–905. https://doi.org/10.1037/neu0000707</bibtext> </blist> <blist> <bibl id="bib2" idref="ref50" type="bt">2</bibl> <bibtext> Albrecht B., Uebel-von Sandersleben H., Wiedmann K., Rothenberger A. (2015). ADHD history of the concept: The case of the continuous performance test. Current Developmental Disorders Reports, 2(1), 10–22. https://doi.org/10.1007/s40474-014-0035-1</bibtext> </blist> <blist> <bibl id="bib3" idref="ref30" type="bt">3</bibl> <bibtext> Arrondo G., Mulraney M., Iturmendi-Sabater I., Musullulu H., Gambra L., Niculcea T., Banaschewski T., Simonoff E., Döpfner M., Hinshaw S. P., Coghill D., Cortese S. (2024). Systematic review and meta-analysis: Clinical utility of continuous performance tests for the identification of attention-deficit/hyperactivity disorder. Journal of the American Academy of Child & Adolescent Psychiatry, 63(2), 154–171. https://doi.org/10.1016/j.jaac.2023.03.011</bibtext> </blist> <blist> <bibl id="bib4" idref="ref59" type="bt">4</bibl> <bibtext> Aujla H. (2023). D′<subs>o</subs>: Sensitivity at the optimal criterion location. Behavior Research Methods, 55(5), 2532–2558. https://doi.org/10.3758/s13428-022-01913-5</bibtext> </blist> <blist> <bibl id="bib5" idref="ref72" type="bt">5</bibl> <bibtext> Bartlett M. S. (1951). The effect of standardization on a χ 2 approximation in factor analysis. Biometrika, 38(3/4), 337–344. https://<ulink href="http://www.jstor.org/stable/2332580">www.jstor.org/stable/2332580</ulink></bibtext> </blist> <blist> <bibl id="bib6" idref="ref51" type="bt">6</bibl> <bibtext> Bella-Fernández M., Martin-Moratinos M., Li C., Wang P., Blasco-Fontecilla H. (2024). Differences in ex-Gaussian parameters from response time distributions between individuals with and without attention deficit/hyperactivity disorder: A meta-analysis. Neuropsychology Review, 34(1), 320–337. https://doi.org/10.1007/s11065-023-09587-2</bibtext> </blist> <blist> <bibl id="bib7" idref="ref36" type="bt">7</bibl> <bibtext> Berger I., Slobodin O., Cassuto H. (2017). Usefulness and validity of continuous performance tests in the diagnosis of attention-deficit hyperactivity disorder children. Archives of Clinical Neuropsychology, 32(1), 81–93. https://doi.org/10.1093/arclin/acw101</bibtext> </blist> <blist> <bibl id="bib8" idref="ref44" type="bt">8</bibl> <bibtext> Borsting E., Rouse M., Chu R. (2005). Measuring ADHD behaviors in children with symptomatic accommodative dysfunction or convergence insufficiency: A preliminary study. Optometry - Journal of the American Optometric Association, 76(10), 588–592. https://doi.org/10.1016/j.optm.2005.07.007</bibtext> </blist> <blist> <bibl id="bib9" idref="ref41" type="bt">9</bibl> <bibtext> Bortolato B., Miskowiak K. W., Köhler C. A., Vieta E., Carvalho A. F. (2015). Cognitive dysfunction in bipolar disorder and schizophrenia: A systematic review of meta-analyses. Neuropsychiatric Disease and Treatment, 11, 3111–3125. https://doi.org/10.2147/NDT.S76700</bibtext> </blist> <blist> <bibtext> Brooks J. M., Guilfoyle J., Alfonso D., Oh A., Nili A. N., Frick L. A., Resch Z. J., Soble J. R., Ulrich D. M. (2024). Differentiating performance on the connors continuous performance test (CPT-3) as a function of comorbid internalizing psychopathology. Journal of Attention Disorders, 28(6), 1024–1031. https://doi.org/10.1177/10870547231219000</bibtext> </blist> <blist> <bibtext> Bytoft B., Knorr S., Vlachova Z., Jensen R. B., Mathiesen E. R., Beck-Nielsen H., Gravholt C. H., Jensen D. M., Clausen T. D., Mortensen E. L., Damm P. (2017). Assessment of attention deficits in adolescent offspring exposed to maternal type 1 diabetes. PLoS ONE, 12(1), e0169308. https://doi.org/10.1371/journal.pone.0169308</bibtext> </blist> <blist> <bibtext> Callan P. D., Swanberg S., Weber S. K., Eidnes K., Pope T. M., Shepler D. (2024). Diagnostic utility of conners continuous performance test-3 for attention deficit/hyperactivity disorder: A systematic review. Journal of Attention Disorders, 28(6), 992–1007. https://doi.org/10.1177/10870547231223727</bibtext> </blist> <blist> <bibtext> Conners C. K. (2014). Conners' continuous performance test (3rd ed.). Multi-Health Systems.</bibtext> </blist> <blist> <bibtext> Cooperman A. W., Waller N. G. (2022). Heywood you go away! Examining causes, effects, and treatments for heywood cases in exploratory factor analysis. Psychological Methods, 27(2), 156–176. https://doi.org/10.1037/met0000384</bibtext> </blist> <blist> <bibtext> Curran P. J., West S. G., Finch J. F. (1996). The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychological Methods, 1(1), 16–29. https://doi.org/10.1037/1082-989X.1.1.16</bibtext> </blist> <blist> <bibtext> de Winter J. C. F., Gosling S. D., Potter J. (2016). Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data. Psychological Methods, 21(3), 273–290. https://doi.org/10.1037/met0000079</bibtext> </blist> <blist> <bibtext> Egeland J. (2007). Differentiating attention deficit in adult ADHD and schizophrenia. Archives of Clinical Neuropsychology, 22(6), 763–771. https://doi.org/10.1016/j.acn.2007.06.004</bibtext> </blist> <blist> <bibtext> Egeland J., Johansen S. N., Ueland T. (2009). Differentiating between ADHD sub-types on CCPT measures of sustained attention and vigilance. Scandinavian Journal of Psychology, 50(4), 347–354. https://doi.org/10.1111/j.1467-9450.2009.00717.x</bibtext> </blist> <blist> <bibtext> Egeland J., Kovalik-Gran I. (2010a). Measuring several aspects of attention in one test: The factor structure of conners's continuous performance test. Journal of Attention Disorders, 13(4), 339–346. https://doi.org/10.1177/1087054708323019</bibtext> </blist> <blist> <bibtext> Egeland J., Kovalik-Gran I. (2010b). Validity of the factor structure of conners' CPT. Journal of Attention Disorders, 13(4), 347–357. https://doi.org/10.1177/1087054709332477</bibtext> </blist> <blist> <bibtext> Egeland J., Løvstad M., Norup A., Nybo T., Persson B. A., Rivera D. F., Schanke A.-K., Sigurdardottir S., Arango-Lasprilla J. C. (2016). Following international trends while subject to past traditions: Neuropsychological test use in the nordic countries. Clinical Neuropsychologist, 30, 1479–1500. https://doi.org/10.1080/13854046.2016.1237675</bibtext> </blist> <blist> <bibtext> Feldman J. S., Huang-Pollock C. (2021). Slow drift rate predicts ADHD symptomology over and above executive dysfunction. Child Neuropsychology, 27(6), 834–855. https://doi.org/10.1080/09297049.2021.1902490</bibtext> </blist> <blist> <bibtext> Fuermaier A. B. M., Fricke J. A., Vries S. M., de, Tucha L., Tucha O. (2019). Neuropsychological assessment of adults with ADHD: A delphi consensus study. Applied Neuropsychology: Adult, 26(4), 340–354. https://doi.org/10.1080/23279095.2018.1429441</bibtext> </blist> <blist> <bibtext> Gagnon A., Grenier G., Bocti C., Gillet V., Lepage J.-F., Baccarelli A. A., Posner J., Descoteaux M., Takser L. (2022). White matter microstructural variability linked to differential attentional skills and impulsive behavior in a pediatric population. Cerebral Cortex (New York, NY), 33(5), 1895–1912. https://doi.org/10.1093/cercor/bhac180</bibtext> </blist> <blist> <bibtext> Gmehlin D., Fuermaier A. B. M., Walther S., Tucha L., Koerts J., Lange K. W., Tucha O., Weisbrod M., Aschenbrenner S. (2016). Attentional lapses of adults with attention deficit hyperactivity disorder in tasks of sustained attention. Archives of Clinical Neuropsychology, 31(4), 343–357. https://doi.org/10.1093/arclin/acw016</bibtext> </blist> <blist> <bibtext> Hair J. F. (2019). Multivariate data analysis (8th ed.). Cengage.</bibtext> </blist> <blist> <bibtext> Hall C. L., Valentine A. Z., Groom M. J., Walker G. M., Sayal K., Daley D., Hollis C. (2016). The clinical utility of the continuous performance test and objective measures of activity for diagnosing and monitoring ADHD in children: A systematic review. European Child & Adolescent Psychiatry, 25(7), 677–699. https://doi.org/10.1007/s00787-015-0798-x</bibtext> </blist> <blist> <bibtext> Huang-Pollock C. L., Karalunas S. L., Tam H., Moore A. N. (2012). Evaluating vigilance deficits in ADHD: A meta-analysis of CPT performance. Journal of Abnormal Psychology, 121(2), 360–371. https://doi.org/10.1037/a0027205</bibtext> </blist> <blist> <bibtext> Kaiser H. F., Rice J. (1974). Little Jiffy, Mark Iv. Educational and Psychological Measurement, 34(1), 111–117. https://doi.org/10.1177/001316447403400115</bibtext> </blist> <blist> <bibtext> Kallitsoglou A. (2013). An investigation of the usefulness of the conner's continuous performance test II in the assessment of inattention and hyperactivity in the school. European Journal of Special Needs Education, 28(3), 348–361.</bibtext> </blist> <blist> <bibtext> Keřková B., Knížková K., Siroňová A., Hrubý A., Večeřová M., Šustová P., Jonáš J., Rodriguez M. (2023). Smoking and attention in schizophrenia spectrum disorders: What are we neglecting? Frontiers in Psychology, 14, Article 1114473. https://<ulink href="http://www.frontiersin.org/articles/10.3389/fpsyg.2023.1114473">www.frontiersin.org/articles/10.3389/fpsyg.2023.1114473</ulink></bibtext> </blist> <blist> <bibtext> Kofler M. J., Rapport M. D., Sarver D. E., Raiker J. S., Orban S. A., Friedman L. M., Kolomeyer E. G. (2013). Reaction time variability in ADHD: A meta-analytic review of 319 studies. Clinical Psychology Review, 33(6), 795–811. https://doi.org/10.1016/j.cpr.2013.06.001</bibtext> </blist> <blist> <bibtext> Kollins S. H., DeLoss D. J., Cañadas E., Lutz J., Findling R. L., Keefe R. S. E., Epstein J. N., Cutler A. J., Faraone S. V. (2020). A novel digital intervention for actively reducing severity of paediatric ADHD (STARS-ADHD): A randomised controlled trial. The Lancet. Digital Health, 2(4), e168–e178. https://doi.org/10.1016/S2589-7500(20)30017-0</bibtext> </blist> <blist> <bibtext> Leys C., Delacre M., Mora Y. L., Lakens D., Ley C. (2019). How to classify, detect, and manage univariate and multivariate outliers, with emphasis on pre-registration. International Review of Social Psychology, 32(1), Article 5. https://doi.org/10.5334/irsp.289</bibtext> </blist> <blist> <bibtext> Leys C., Klein O., Dominicy Y., Ley C. (2018). Detecting multivariate outliers: Use a robust variant of the Mahalanobis distance. Journal of Experimental Social Psychology, 74, 150–156. https://doi.org/10.1016/j.jesp.2017.09.011</bibtext> </blist> <blist> <bibtext> Lüdecke D., Ben-Shachar M. S., Patil I., Makowski D. (2020). Extracting, computing and exploring the parameters of statistical models using. R. Journal of Open Source Software, 5, 2445. https://doi.org/10.21105/joss.02445</bibtext> </blist> <blist> <bibtext> Lüdecke D., Ben-Shachar M. S., Patil I., Waggoner P., Makowski D. (2021). Performance: An r package for assessment, comparison and testing of statistical models. Journal of Open Source Software, 6, 3139. https://doi.org/10.21105/joss.03139</bibtext> </blist> <blist> <bibtext> Mardia K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3), 519–530.</bibtext> </blist> <blist> <bibtext> Martella D., Aldunate N., Fuentes L. J., Sánchez-Pérez N. (2020). Arousal and executive alterations in attention deficit hyperactivity disorder (ADHD). Frontiers in Psychology, 11, Article 1991. https://doi.org/10.3389/fpsyg.2020.01991</bibtext> </blist> <blist> <bibtext> McClure G., Hawes D. J., Dadds M. R. (2015). Borderline personality disorder and neuropsychological measures of executive function: A systematic review. Personality and Mental Health, 10(1), 43–57. https://doi.org/10.1002/pmh.1320</bibtext> </blist> <blist> <bibtext> Miranda M. C., Barbosa T., Muszkat M., Rodrigues C. C., Sinnes E. G., Coelho L. F. S., Rizzuti S., Palma S. M. M., Bueno O. F. A. (2012). Performance patterns in Conners' CPT among children with attention deficit hyperactivity disorder and dyslexia. Arquivos de neuro-psiquiatria, 70(2), 91–96. https://doi.org/10.1590/s0004-282x2012000200004</bibtext> </blist> <blist> <bibtext> Mirsky A. F., Anthony B. J., Duncan C. C., Ahearn M. B., Kellam S. G. (1991). Analysis of the elements of attention: A neuropsychological approach. Neuropsychology Review, 2(2), 109–145. https://doi.org/10.1007/BF01109051</bibtext> </blist> <blist> <bibtext> Mowinckel A. M., Pedersen M. L., Eilertsen E., Biele G. (2015). A meta-analysis of decision-making and attention in adults with ADHD. Journal of Attention Disorders, 19(5), 355–367. https://doi.org/10.1177/1087054714558872</bibtext> </blist> <blist> <bibtext> Pagán A. F., Huizar Y. P., Schmidt A. T. (2023). Conner's continuous performance test and adult ADHD: A systematic literature review. Journal of Attention Disorders, 27(3), 231–249. https://doi.org/10.1177/10870547221142455</bibtext> </blist> <blist> <bibtext> Pettersson R., Söderström S., Nilsson K. W. (2018). Diagnosing ADHD in adults: An examination of the discriminative validity of neuropsychological tests and diagnostic assessment instruments. Journal of Attention Disorders, 22(11), 1019–1031. https://doi.org/10.1177/1087054715618788</bibtext> </blist> <blist> <bibtext> Pievsky M. A., McGrath R. E. (2018). The neurocognitive profile of attention-deficit/hyperactivity disorder: A review of meta-analyses. Archives of Clinical Neuropsychology, 33(2), 143–157. https://doi.org/10.1093/arclin/acx055</bibtext> </blist> <blist> <bibtext> Poltavski D. V., Biberdorf D., Mark C. (2016). Association of asthenopia with sustained attention, accommodation and encephalographic activity. Vision Development & Rehabilitation, 2, 155–168. https://doi.org/10.31707/VDR2016.2.3.p155</bibtext> </blist> <blist> <bibtext> Poltavski D. V., Biberdorf D., Petros T. V. (2012). Accommodative response and cortical activity during sustained attention. Vision Research, 63, 1–8. https://doi.org/10.1016/j.visres.2012.04.017</bibtext> </blist> <blist> <bibtext> R Core Team. (2022). R: A language and environment for statistical computing. https://<ulink href="http://www.R-project.org/">www.R-project.org/</ulink></bibtext> </blist> <blist> <bibtext> Rabin L. A., Paolillo E., Barr W. B. (2016). Stability in test-usage practices of clinical neuropsychologists in the united states and canada over a 10-year period: A follow-up survey of INS and NAN members. Archives of Clinical Neuropsychology, 31(3), 206–230. https://doi.org/10.1093/arclin/acw007</bibtext> </blist> <blist> <bibtext> Ratcliff R. (1978). A theory of memory retrieval. Psychological Review, 85(2), 59.</bibtext> </blist> <blist> <bibtext> Reeves A. (2019). Attention and signal detection. Information, 10(88), 254. https://doi.org/10.3390/info10080254</bibtext> </blist> <blist> <bibtext> Robinson O. J., Vytal K., Cornwell B. R., Grillon C. (2013). The impact of anxiety upon cognition: Perspectives from human threat of shock studies. Frontiers in Human Neuroscience, 7, Article 203. https://doi.org/10.3389/fnhum.2013.00203</bibtext> </blist> <blist> <bibtext> Rosso G., Portaluppi C., Teobaldi E., Di Salvo G., Maina G. (2023). Assessing adult ADHD through objective neuropsychological measures: A critical overview. Journal of Attention Disorders, 27(7), 786–794. https://doi.org/10.1177/10870547231167564</bibtext> </blist> <blist> <bibtext> Rouder J. N., Haaf J. M. (2019). A psychometrics of individual differences in experimental tasks. Psychonomic Bulletin & Review, 26(2), 452–467. https://doi.org/10.3758/s13423-018-1558-y</bibtext> </blist> <blist> <bibtext> Salum G. A., Sato J. R., Manfro A. G., Pan P. M., Gadelha A., do Rosário M. C., Polanczyk G. V., Castellanos F. X., Sonuga-Barke E., Rohde L. A. (2019). Reaction time variability and attention-deficit/hyperactivity disorder: Is increased reaction time variability specific to attention-deficit/hyperactivity disorder? Testing predictions from the default-mode interference hypothesis. ADHD Attention Deficit and Hyperactivity Disorders, 11(1), 47–58. https://doi.org/10.1007/s12402-018-0257-x</bibtext> </blist> <blist> <bibtext> Sawaya H., Miller J. C., Raines J. M. (2023). Review of studies on incremental validity of assessment measures used in psychological assessment of attention-deficit hyperactivity disorder. Assessment, 31(2), 518–537. https://doi.org/10.1177/10731911231159933</bibtext> </blist> <blist> <bibtext> Sergeant J. A. (2005). Modeling attention-deficit/hyperactivity disorder: A critical appraisal of the cognitive-energetic model. Biological Psychiatry, 57(11), 1248–1255. https://doi.org/10.1016/j.biopsych.2004.09.010</bibtext> </blist> <blist> <bibtext> Shaked D., Faulkner L. M. D., Tolle K., Wendell C. R., Waldstein S. R., Spencer R. J. (2020). Reliability and validity of the Conners' continuous performance test. Applied neuropsychology. Adult, 27(5), 478–487. https://doi.org/10.1080/23279095.2019.1570199</bibtext> </blist> <blist> <bibtext> Sikström S., Söderlund G. (2007). Stimulus-dependent dopamine release in attention-deficit/hyperactivity disorder. Psychological Review, 114(4), 1047–1075. https://doi.org/10.1037/0033-295X.114.4.1047</bibtext> </blist> <blist> <bibtext> Slobodin O., Yahav I., Berger I. (2020). A machine-based prediction model of ADHD using CPT data. Frontiers in Human Neuroscience, 14, Article 560021. https://doi.org/10.3389/fnhum.2020.560021</bibtext> </blist> <blist> <bibtext> Toplak M. E., West R. F., Stanovich K. E. (2013). Practitioner review: Do performance-based measures and ratings of executive function assess the same construct? Journal of Child Psychology and Psychiatry, 54(2), 131–143. https://doi.org/10.1111/jcpp.12001</bibtext> </blist> <blist> <bibtext> Varela J. L., Magnante A. T., Miskey H. M., Ord A. S., Eldridge A., Shura R. D. (2024). A systematic review of the utility of continuous performance tests among adults with ADHD. The Clinical Neuropsychologist, 38(7), 1524–1585. https://doi.org/10.1080/13854046.2024.2315740</bibtext> </blist> <blist> <bibtext> Vertinski M. (2014). Factor structure of the CPT-II [UNLV Theses, Dissertations, Professional Papers, and Capstones]. UNLV. https://doi.org/10.34917/7048626</bibtext> </blist> <blist> <bibtext> Waller N. G. (2024). Fungible: Psychometric functions from the waller lab. https://CRAN.R-project.org/package=fungible</bibtext> </blist> <blist> <bibtext> Watkins M. (2020). A step-by-step guide to exploratory factor analysis with R and RStudio (1st ed.). Routledge. https://doi.org/10.4324/9781003120001</bibtext> </blist> <blist> <bibtext> Watkins M. W. (2018). Exploratory factor analysis: A guide to best practice. Journal of Black Psychology, 44(3), 219–246. https://doi.org/10.1177/0095798418771807</bibtext> </blist> <blist> <bibtext> Wechsler D. (2008). Wechsler Adult Intelligence Scale – Fourth Edition (WAIS–IV). NCS Pearson.</bibtext> </blist> </ref> <ref id="AN0188320981-24"> <title> Footnotes </title> <blist> <bibtext> The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research was funded by Vestfold Hospital Trust.</bibtext> </blist> <blist> <bibtext> Olaf Lund</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0003-0305-0559 Rune Raudeberg</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0003-0919-6479 Espen Walderhaug</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0002-0115-9596 Jens Egeland</bibtext> </blist> <blist> <bibtext>Graph https://orcid.org/0000-0002-7322-9266</bibtext> </blist> <blist> <bibtext> Supplemental material for this article is available online.</bibtext> </blist> </ref> <aug> <p>By Olaf Lund; Rune Raudeberg; Hans Johansen; Mette-Line Myhre; Espen Walderhaug; Amir Poreh and Jens Egeland</p> <p>Reported by Author; Author; Author; Author; Author; Author; Author</p> <p></p> <p>Olaf Lund is a PhD candidate at the Division of Mental Health and Addiction and a clinical neuropsychologist at the Habilitation Centre, Vestfold Hospital Trust.</p> <p>Rune Raudeberg, PhD, is an associate professor in the Department of Biological and Medical Psychology at the University of Bergen and a clinical neuropsychologist.</p> <p>Hans Johansen is a clinical neuropsychologist at the Division of Physical Medicine and Rehabilitation, Vestfold Hospital Trust.</p> <p>Mette-Line Myhre is a clinical neuropsychologist at the Habilitation Centre, Vestfold Hospital Trust.</p> <p>Espen Walderhaug, PhD, is a clinical neuropsychologist and researcher at the Department of Addiction Treatment, Oslo University Hospital.</p> <p>Amir Poreh, PhD, is a professor at Cleveland State University and a clinical neuropsychologist.</p> <p>Jens Egeland, PhD, is a professor II in the Department of Psychology at the University of Oslo, a researcher at the Division of Mental Health and Addiction, Vestfold Hospital Trust, and a clinical neuropsychologist.</p> </aug> <nolink nlid="nl1" bibid="bib13" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib50" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib21" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib26" firstref="ref5"></nolink> <nolink nlid="nl5" bibid="bib19" firstref="ref7"></nolink> <nolink nlid="nl6" bibid="bib20" firstref="ref8"></nolink> <nolink nlid="nl7" bibid="bib64" firstref="ref9"></nolink> <nolink nlid="nl8" bibid="bib11" firstref="ref11"></nolink> <nolink nlid="nl9" bibid="bib31" firstref="ref12"></nolink> <nolink nlid="nl10" bibid="bib24" firstref="ref13"></nolink> <nolink nlid="nl11" bibid="bib42" firstref="ref23"></nolink> <nolink nlid="nl12" bibid="bib67" firstref="ref26"></nolink> <nolink nlid="nl13" bibid="bib23" firstref="ref27"></nolink> <nolink nlid="nl14" bibid="bib45" firstref="ref28"></nolink> <nolink nlid="nl15" bibid="bib44" firstref="ref29"></nolink> <nolink nlid="nl16" bibid="bib12" firstref="ref31"></nolink> <nolink nlid="nl17" bibid="bib57" firstref="ref33"></nolink> <nolink nlid="nl18" bibid="bib54" firstref="ref34"></nolink> <nolink nlid="nl19" bibid="bib61" firstref="ref35"></nolink> <nolink nlid="nl20" bibid="bib27" firstref="ref37"></nolink> <nolink nlid="nl21" bibid="bib33" firstref="ref38"></nolink> <nolink nlid="nl22" bibid="bib62" firstref="ref40"></nolink> <nolink nlid="nl23" bibid="bib53" firstref="ref42"></nolink> <nolink nlid="nl24" bibid="bib40" firstref="ref43"></nolink> <nolink nlid="nl25" bibid="bib48" firstref="ref45"></nolink> <nolink nlid="nl26" bibid="bib47" firstref="ref46"></nolink> <nolink nlid="nl27" bibid="bib18" firstref="ref47"></nolink> <nolink nlid="nl28" bibid="bib17" firstref="ref48"></nolink> <nolink nlid="nl29" bibid="bib32" firstref="ref52"></nolink> <nolink nlid="nl30" bibid="bib56" firstref="ref54"></nolink> <nolink nlid="nl31" bibid="bib10" firstref="ref56"></nolink> <nolink nlid="nl32" bibid="bib68" firstref="ref58"></nolink> <nolink nlid="nl33" bibid="bib52" firstref="ref60"></nolink> <nolink nlid="nl34" bibid="bib49" firstref="ref61"></nolink> <nolink nlid="nl35" bibid="bib36" firstref="ref62"></nolink> <nolink nlid="nl36" bibid="bib65" firstref="ref63"></nolink> <nolink nlid="nl37" bibid="bib14" firstref="ref64"></nolink> <nolink nlid="nl38" bibid="bib37" firstref="ref65"></nolink> <nolink nlid="nl39" bibid="bib35" firstref="ref66"></nolink> <nolink nlid="nl40" bibid="bib34" firstref="ref67"></nolink> <nolink nlid="nl41" bibid="bib16" firstref="ref68"></nolink> <nolink nlid="nl42" bibid="bib66" firstref="ref69"></nolink> <nolink nlid="nl43" bibid="bib29" firstref="ref70"></nolink> <nolink nlid="nl44" bibid="bib15" firstref="ref73"></nolink> <nolink nlid="nl45" bibid="bib38" firstref="ref74"></nolink> <nolink nlid="nl46" bibid="bib25" firstref="ref76"></nolink> <nolink nlid="nl47" bibid="bib30" firstref="ref88"></nolink> <nolink nlid="nl48" bibid="bib41" firstref="ref89"></nolink> <nolink nlid="nl49" bibid="bib51" firstref="ref94"></nolink> <nolink nlid="nl50" bibid="bib43" firstref="ref95"></nolink> <nolink nlid="nl51" bibid="bib22" firstref="ref96"></nolink> <nolink nlid="nl52" bibid="bib46" firstref="ref97"></nolink> <nolink nlid="nl53" bibid="bib63" firstref="ref98"></nolink> <nolink nlid="nl54" bibid="bib39" firstref="ref104"></nolink> <nolink nlid="nl55" bibid="bib60" firstref="ref105"></nolink> <nolink nlid="nl56" bibid="bib58" firstref="ref106"></nolink> <nolink nlid="nl57" bibid="bib55" firstref="ref117"></nolink> <nolink nlid="nl58" bibid="bib59" firstref="ref118"></nolink> <nolink nlid="nl59" bibid="bib28" firstref="ref119"></nolink>
Header DbId: eric
DbLabel: ERIC
An: EJ1485122
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Factor Structure of the Conners Continuous Performance Test Third Edition (CCPT-3): Exploratory Factor Analysis in a Mixed Clinical Sample
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Olaf+Lund%22">Olaf Lund</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-0305-0559">0000-0003-0305-0559</externalLink>)<br /><searchLink fieldCode="AR" term="%22Rune+Raudeberg%22">Rune Raudeberg</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-0919-6479">0000-0003-0919-6479</externalLink>)<br /><searchLink fieldCode="AR" term="%22Hans+Johansen%22">Hans Johansen</searchLink><br /><searchLink fieldCode="AR" term="%22Mette-Line+Myhre%22">Mette-Line Myhre</searchLink><br /><searchLink fieldCode="AR" term="%22Espen+Walderhaug%22">Espen Walderhaug</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0115-9596">0000-0002-0115-9596</externalLink>)<br /><searchLink fieldCode="AR" term="%22Amir+Poreh%22">Amir Poreh</searchLink><br /><searchLink fieldCode="AR" term="%22Jens+Egeland%22">Jens Egeland</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-7322-9266">0000-0002-7322-9266</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Attention+Disorders%22"><i>Journal of Attention Disorders</i></searchLink>. 2025 29(13):1163-1176.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 14
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Factor+Structure%22">Factor Structure</searchLink><br /><searchLink fieldCode="DE" term="%22Factor+Analysis%22">Factor Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Attention+Span%22">Attention Span</searchLink><br /><searchLink fieldCode="DE" term="%22Measures+%28Individuals%29%22">Measures (Individuals)</searchLink><br /><searchLink fieldCode="DE" term="%22Performance+Tests%22">Performance Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Attention+Deficit+Hyperactivity+Disorder%22">Attention Deficit Hyperactivity Disorder</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+Assisted+Testing%22">Computer Assisted Testing</searchLink><br /><searchLink fieldCode="DE" term="%22Conceptual+Tempo%22">Conceptual Tempo</searchLink><br /><searchLink fieldCode="DE" term="%22Reaction+Time%22">Reaction Time</searchLink><br /><searchLink fieldCode="DE" term="%22Brain%22">Brain</searchLink>
– Name: SubjectThesaurus
  Label: Assessment and Survey Identifiers
  Group: Su
  Data: <searchLink fieldCode="SU" term="%22Continuous+Performance+Test%22">Continuous Performance Test</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/10870547251341928
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1087-0547<br />1557-1246
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Objective: The Conners Continuous Performance Test-3 (CCPT-3) is a computerized test of attention frequently used in clinical neuropsychology. In the present factor analysis, we seek to assess the factor structure of the CCPT-3 and evaluate the suggested dimensions in the CCPT-3 Manual. Method: Data from a mixed clinical sample of 931 adults referred for neuropsychological assessment across four centers were analyzed. Nine standard and eight experimental measures were subjected to an exploratory factor analysis to evaluate factor models ranging from one to six factors. Results: The analysis supported a four-factor model with one overall attention factor and three factors of distinct mechanisms underlying inattention: impulsivity, vigilance, and sustained attention. This closely aligns with the four dimensions outlined in the CCPT-3 Technical Manual and the factor analyses from the CCPT-II. There were some differences between the four-factor model and the interpretations recommended in the Technical Manual. Perseverations were associated with the inattention factor rather than the impulsivity factor, and reaction time was exclusively linked to impulsivity. Incorporating error measures into the vigilance factor suggests that decreases in responsivity, rather than decreases in correct responses, underpin vigilance decrements. Including response bias by inter-stimulus interval (ISI) and by blocks in the analysis indicates that a decrease in arousal may also explain impairments in sustained attention. Conclusion: This study supports the notion in the Technical Manual that CCPT-3 measures both overall attention and three different mechanisms that mediate inattention: impulsivity, vigilance and sustained attention.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1485122
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1485122
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/10870547251341928
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 14
        StartPage: 1163
    Subjects:
      – SubjectFull: Factor Structure
        Type: general
      – SubjectFull: Factor Analysis
        Type: general
      – SubjectFull: Attention Span
        Type: general
      – SubjectFull: Measures (Individuals)
        Type: general
      – SubjectFull: Performance Tests
        Type: general
      – SubjectFull: Attention Deficit Hyperactivity Disorder
        Type: general
      – SubjectFull: Computer Assisted Testing
        Type: general
      – SubjectFull: Conceptual Tempo
        Type: general
      – SubjectFull: Reaction Time
        Type: general
      – SubjectFull: Brain
        Type: general
      – SubjectFull: Continuous Performance Test
        Type: general
    Titles:
      – TitleFull: Factor Structure of the Conners Continuous Performance Test Third Edition (CCPT-3): Exploratory Factor Analysis in a Mixed Clinical Sample
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Olaf Lund
      – PersonEntity:
          Name:
            NameFull: Rune Raudeberg
      – PersonEntity:
          Name:
            NameFull: Hans Johansen
      – PersonEntity:
          Name:
            NameFull: Mette-Line Myhre
      – PersonEntity:
          Name:
            NameFull: Espen Walderhaug
      – PersonEntity:
          Name:
            NameFull: Amir Poreh
      – PersonEntity:
          Name:
            NameFull: Jens Egeland
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 11
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 1087-0547
            – Type: issn-electronic
              Value: 1557-1246
          Numbering:
            – Type: volume
              Value: 29
            – Type: issue
              Value: 13
          Titles:
            – TitleFull: Journal of Attention Disorders
              Type: main
ResultId 1