The Potential Clinical Relevance of the WISC-V in ADHD Assessment: An Analysis of Structural Models and Within- Subject Cognitive Discrepancies

The Potential Clinical Relevance of the WISC-V in ADHD Assessment: An Analysis of Structural Models and Within- Subject Cognitive Discrepancies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Potential Clinical Relevance of the WISC-V in ADHD Assessment: An Analysis of Structural Models and Within- Subject Cognitive Discrepancies Javier Fenollar-Cortés, Aroa Caminero-Ruiz, Deseada Auxiliadora Ruiz-Aranda, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8378890/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background : The current study examined the clinical utility of the Wechsler Intelligence Scale for Children – Fifth Edition (WISC-V) as a valid tool for the evaluation of Attention-Deficit/Hyperactivity Disorder (ADHD) in children and adolescents. The primary objectives were to explore the convergent validity of the WISC-V based on its original factor structure and alternative structural models, including hierarchical and bifactorial models. Additionally, the study aimed to investigate whether significant differences between primary and complementary indices could reveal a cognitive pattern associated with ADHD. Methods : A total of 241 participants, aged 6 to 17 years and recently diagnosed with ADHD, were included in the study. Confirmatory factor analyses were conducted to evaluate the fit of different models. Results : Results indicated that four-factor models, both hierarchical and bifactorial, showed superior fit compared to five-factor models. However, the original hierarchical five-factor model proposed by Wechsler, while demonstrating a poorer fit compared to alternative models, was still adequate for use in clinical settings. Moreover, scores on the Working Memory and Processing Speed indices were significantly lower, with medium to large effect sizes, than those on Verbal Comprehension, Visual-Spatial Reasoning, and Fluid Reasoning indices. Additionally, the Cognitive Proficiency Index was significantly lower than the General Ability Index. Conclusions : These findings suggest that these discrepancies may help identify ADHD cognitive profiles. However, while these patterns may hold clinical relevance, they should not be overinterpreted as diagnostic markers. The study highlights the need for further research to validate the WISC-V's clinical utility as a supplementary tool in ADHD assessment. WISC-V ADHD Multiple group CFA Intelligence Construct Validity Figures Figure 1 Figure 2 Figure 3 Introduction Attention-deficit/hyperactivity disorder (ADHD) is characterized by a persistent pattern of inattention and/or hyperactivity-impulsivity that interferes with or reduces the quality of social, academic, and occupational functioning [1]. ADHD is one of the most prevalent mental disorders among children and adolescents, with an estimated worldwide prevalence of 5.6-8% [2] The core symptoms of ADHD often co-occur with deficits in specific neurocognitive domains such as reaction time variability, vigilance, working memory, response inhibition, and intelligence/achievement [3]. Thus, in addition to behavioral measures to assess ADHD, a wide range of neuropsychological measures have been proposed as clinical tools to discriminate ADHD profiles, but with poor results (e.g., [4, 5]). Widely used continuous performance tests (CPTs) had slightly better results regarding their use as ADHD diagnostic measures [6, 7]. In addition to neuropsychological measures, the usefulness of including cognitive measures in the ADHD assessment process has also been explored. The Wechsler Intelligence Scale for Children-Fifth Edition (WISC-V; [8]) is the most widely used cognitive assessment tool for children and adolescents [9]. Research on the relationship between ADHD and the WISC can be divided into two approaches: those looking for specific cognitive profiles in the WISC for ADHD samples [10–14] and those exploring the construct validity of the WISC in ADHD samples [15–19]. Similar studies have explored the relationship between the Wechsler scales and various disorders or medical conditions, such as autism spectrum disorder [20], specific learning disabilities [21, 22], epilepsy [23], gifted students [24], or in mixed clinical samples (e.g., [23, 25–27]). Several studies have investigated the possible inclusion of the Wechsler Intelligence Scale for Children (WISC) as part of a comprehensive assessment of ADHD in childhood and adolescence. Fenollar-Cortés et al. [28] identified a cognitive pattern in individuals with ADHD, characterized by significantly lower Working Memory (WMI) and Processing Speed (PSI) scores compared to Verbal Comprehension (VCI) and Perceptual Reasoning (PRI) on the WISC-IV. This pattern has been observed across different studies (e.g., [18, 29]), reinforcing the notion that deficits in WMI and PSI are hallmarks of the ADHD cognitive profile. Additionally, these deficits have been studied in comorbid conditions, such as Specific Learning Disorders (SLD), as children with ADHD often show similar cognitive impairments [30, 31]. Deficits in Working Memory have been particularly noted in children with both ADHD and SLD [32], while significant impairments in processing speed have also been reported [32]. Moreover, Thaler et al. [19] found that children with ADHD tend to have lower PSI and WMI scores, supporting the five-factor model proposed by Keith and adopted in the WISC-V [8]. While the diagnostic utility of these differences remains debated [29], other studies have found that these deficits persist into adulthood [33]. Overall, these findings suggest that the presence of cognitive deficits on WMI and PSI may be an important aspect of the ADHD profile, although more research is needed to explore the heterogeneity of cognitive profiles [34]. However, before the different scores of the WISC-V can be used in clinical practice to discriminate specific clinical profiles, the psychometric properties of the scale, particularly reliability and dimensionality, must be adequate. Consistent with this, Watkins & Canivez [35] recommended that clinicians "go beyond structural goodness of fit and evaluate IQ test scores in terms of their reliability and ability to provide information that is not available from the general ability score as well as their predictive and treatment validity" (p. 619). Thus, numerous studies of the factorial structure of the Wechsler scales conclude that only the Full-Scale Intelligence Quotient (FSIQ) score may be sufficiently reliable for clinical use [36–39]. Moreover, similar results have also been found in studies examining the construct validity of the WISC-IV and the WISC-V in populations from different countries (e.g., [36, 40–44]) The suitability of the higher-order five-factor structure of the WISC-V over other alternative structural models (e.g., bifactorial or higher-order four-factor models) is still under debate [36, 40, 45–48]. Moreover, there are some doubts regarding the long-term stability of WISC-V in clinical samples [49]. Regardless of whether the most appropriate factorial structure for the WISC-V is a four or five-factor model, and a hierarchical or bifactorial structure, the controversy may be more related to different views of the structure of intelligence, which would lead researchers to choose other statistical methods of factor analysis [50]. Beyond the methodological aspects, which are indeed relevant, and given the existence of a considerable research-to-practice gap [51], it would be interesting to provide professional clinical psychologists, school psychologists, and mental health professionals with clear and straightforward guidelines regarding the use of the WISC in daily professional practice with children and adolescents diagnosed with ADHD. The overall objective of this study was to explore whether the WISC-V could be considered a valid instrument for inclusion as an additional tool in the assessment of ADHD in children and adolescents. However, before using the WISC-V among individuals with ADHD, the psychometric properties of the scale need to be established before profile analyses can be conducted (especially if construct validity is unclear). To this end, the present study established two main objectives: first, to explore the construct validity of the WISC-V in a population of children and adolescents recently diagnosed with ADHD, examining both its original structure and alternative structures proposed in previous scientific literature; second, contingent on the first objective, to investigate whether significant differences exist between the primary indices of the scale, as well as certain complementary indices, in order to propose a cognitive pattern associated with children and adolescents with ADHD, as measured by the WISC-V. Methods Participants The study included 241 children and adolescents between the ages of 6 and 17 who had recently been diagnosed with ADHD. The mean age of the participants was 9.92 years (SD = 2.87). The sample included a higher proportion of males (76.8%). Statistical analyses revealed no significant age differences between male and female participants ( p = .725), ensuring that age-related factors did not differ across genders. Participants were recruited from child and adolescent mental health clinics. The diagnostic test battery included behavioral measures, executive function measures, reading and math performance measures, neuropsychological measures, and academic performance outcomes. ADHD diagnoses were confirmed by a child and adolescent psychiatrist or a neuropsychiatrist from different public child mental health centers in Madrid, Valencia, and Alicante. The WISC-V was part of this systematic psychological assessment battery. Inclusion criteria were being between 6 and 17 years of age, an FSIQ score of 70 or greater, and no severe neurological or psychological problems that could affect performance on the WISC-V [52]. The ADHD diagnosis was later confirmed by a mental health professional outside the research team. Participants with comorbid disorders (other than specific learning disorders) were excluded from the study. Participants in the typically developing group had to be clinically assessed and not have relevant clinical symptoms or a previous mental health diagnosis at the time of the study. All participants were required to complete the 10 primary tests of the WISC-V. Families were informed about the study and signed informed consent forms. Instruments The WISC-V is a norm-referenced, individually administered intelligence battery focused on children and adolescents aged 6–16 years. According to the WISC-V manual, Cattell-Horn-Carroll (CHC) theory, neurodevelopmental research, and clinical utility were considered in its development. These frameworks can be used to interpret WISC-V and WISC-V Spain scores. This instrument contains ten primary and five secondary subtests ( M = 10, SD = 5). The primary index scales ( M = 100, SD = 15) are computed as follows: Similarities (SI) and Vocabulary (VO) subtests create the Verbal Comprehension Index (VCI); Block Design (BD) and Visual Puzzles (VP) subtests create the Visual Spatial Index (VS); Matrix Reasoning (MR) and Figure Weights (FW) subtests create the Fluid Reasoning Index (FR); Digit Span (DS) and Picture Span (PS) subtests create the Working Memory Index (WMI); and Coding (CD) and Symbol Search (SS) subtests create the Processing Speed Index (PSI). The Full-Scale IQ is computed using only seven primary subtests: SI, VO, BD, MR, FW, DS, and CD. The five secondary subtests are suggested to load on the same factors as the primary subtests: Information (IN) and Comprehension (CO) on the VC factor, Arithmetic (AR) on the FR factor, Letter-Number Sequencing (LN) on the WM factor, and Cancellation (CA) on the PS factor. However, alternative models have been proposed depending on the number of latent variables and the number of indicator loading combinations (single and cross-loadings) (Table 1). Most include a g-factor as a second-order latent variable (on which all first-order latent variables are loaded) or a first-order factor (on which all indicators are loaded). Data Analysis Data distribution was explored using normal Q-Q plots and z -values (skewness and kurtosis values divided by their standard errors; z -value > |3.29| indicates data are not normally distributed) [53]. The K 2 statistic was used to assess multivariate normality (the statistic is computed by summing the squared z-scores for kurtosis and skewness; If the value of K 2 exceeds 5.99, the null hypothesis of normality is rejected at the 5% significance level, suggesting multivariate non-normality). All confirmatory factor analyses (CFAs) were conducted using JASP software [54] from the raw data using the maximum likelihood (ML) estimator. Latent variable scales were identified by setting a reference indicator in the higher-order models and the variance of latent variables in the bifactor models [55]. Parameter estimates were constrained to equality in the models with only two indicators per factor, and in the higher-order models, the model fit was assessed both with and without these constraints. Parameter estimates were constrained to equality in the models with only two indicators per factor, and in the higher-order models, the model fit was assessed both with and without these constraints. Consistent with previous WISC-IV and WISC-V factorial analyses, only higher-order and bifactor models were examined because the WISC-IV and the WISC-V score structures (i.e., implied by an FSIQ score) indicate a hierarchical structure. The evaluated models were taken from Weschler [56] and are detailed in Table 1. Following Fenollar-Cortés & Watkins [57], only the bifactor versions of Models 4a and 5a (with correlated FR and VS factors) were computed. As recommended by Kline [58], global model fit was evaluated using multiple fit indices: model chi-square (χ 2 ), comparative fit index (CFI), Tucker–Lewis index (TLI), root mean square error of approximation (RMSEA), the standardized root mean square residual (SRMR), and Akaike’s information criterion (AIC; Akaike [59]). Based on the combinational rules of Hu & Bentler [60], a good fit required CFI/TLI ≥ .95 and RMSEA ≤ .06. For AIC, lower values identify models that are more likely to be generalizable [61]. Meaningful differences between well-fitting models were also evaluated using ΔCFI/ΔTLI ≥ .01, ΔRMSEA ≥ .015, ΔSRMR ≥ .03 [62], ΔAIC ≥ 10 [63], and non-significant Δχ 2 . In addition to the goodness of fit indices, the standardized residuals, modification indices, localized areas of strain, interpretability, and size of the parameter estimates were examined for each model, as acceptable overall goodness-of-fit indices may mask problems in some indicator relationships, especially in complex models [55]. The absence of localized areas of poor fit in the solution was considered if the Z value was ≦ 2.58 [64] and the Modification indices were ≦ 4.00 [65]. Omega coefficients were calculated for model-based reliability of the bifactor models [66]. The most comprehensive omega coefficient is the total omega (ω), which estimates the proportion of variance in the observed total score that can be attributed to all sources of common variance included in the model. High omega values indicate a highly reliable multidimensional total score. The omega subscale (ω s ) was also computed for each unit-weighted subscale score, which indexes the proportion of variance in each unit-weighted subscale score attributable to a mixture of general and group factor variance. High ω s values indicate a highly reliable multidimensional group factor score. Values below .90 were considered unacceptable for decision-making about individuals, similar to alpha values [67]. Additionally, Hancock & Mueller's [68] H coefficient was calculated to provide another perspective on construct reliability. The H coefficient represents the correlation between a factor and an optimally weighted composite score, indicating how well a latent variable is represented by its indicators. H coefficient values below .70 were considered insufficient. In the case of high-order models, ω was also calculated, which estimates the proportion of variance in the observed scores attributable to the second-order factor and its derived first-order factors. In the case of the high-order models with five Wechsler factors, we explored the potential existence of significant differences among the different subscales (VC, RF, VS, WM, and PS), as well as other indices such as GAI and ICC, to determine whether the mean scores on these measures differed within the ADHD population. To accomplish this, a one-way repeated measures ANOVA was conducted, followed by the appropriate post-hoc analyses. The effect size was estimated using omega squared (ω²) for the ANOVA and Cohen's d for the post-hoc t -tests. A commonly used interpretation refers to effect sizes as small ( d = 0.2; ω 2 = .01), medium ( d = 0.5; ω 2 = .06), and large ( d = 0.8; ω 2 = .14) based on benchmarks suggested by Cohen [69]. However, these values are arbitrary and should not be interpreted rigidly [70]. Two one-way repeated measures ANOVAs were conducted with a within-subjects factor of subscale: the first ANOVA was conducted on the Primary indices, and the second ANOVA was conducted on the FSIQ, the General Ability Index, and the Cognitive Proficiency Index. This approach was chosen because it takes into account the within-subject correlations inherent in repeated assessments, allowing for a more accurate analysis of differences across the subscales within the same group of participants. This method provides a sensitive test for detecting variations in cognitive performance across the different domains measured by the WISC-V. Post-hoc analyses were conducted using Holm's correction, as it provides an appropriate balance between controlling for Type I error and maintaining robust statistical power. Sphericity was assessed using Mauchly's test. In cases where Mauchly's test indicated a violation of the sphericity assumption ( p < .05), the Greenhouse-Geisser and Huynh-Feldt corrections were applied. The ϵ values for both Greenhouse-Geisser and Huynh-Feldt were calculated to adjust the degrees of freedom accordingly. Following the guidelines recommended by Field [71], the Greenhouse-Geisser correction was used when ϵ 0.75 to ensure appropriate adjustment for sphericity violation. The distribution of the values corresponding to the WISC indices (Primary, General Ability, and Cognitive Proficiency indices) was examined using normal Q-Q plots and z-values. Results The variables exhibited univariate normality, as evidenced by z-values ranging from -1.12 to 2.54 for kurtosis and -0.41 to 2.53 for skewness. Multivariate normality of the data distribution was also established ( K 2 values from 0.35 to 4.98). Confirmatory Factor Analysis and Multi-sample Invariance Analysis Upon examining the model fit, only the four-factor models—in which factors with two indicators were constrained to equality for identification, whether within high-order or bifactor structures—strictly met the predefined fit criteria (see Table 2 and Figure 1). However, the four-factor high-order model with no constrained factors had a low TLI value (< .971), while the other fit indices remained within acceptable ranges. For the five-factor models, the constrained indicators version met the minimum fit requirements, although the upper limit of the RMSEA confidence interval slightly exceeded the threshold (90% CI RMSEA, .018–.071). This suggests that while the model generally meets the criteria for good fit, caution is warranted. Greater caution should be exercised with the five-factor high-order model without constraints, as its TLI = .947 (which could be rounded to .95), but it shares the same issue regarding the upper limit of the RMSEA confidence interval (.018–.074). Finally, the five-factor bifactor model did not fit adequately due to negative residual variance estimates. Although restrictions were applied to address this, it ultimately renders the model unsuitable for selection. After evaluating the fit of each model with different factorial structures and constraints, we proceeded to assess whether any model was significantly superior and, therefore, preferable for selection. Based on the previously established cut-off points, we can conclude that the four-factor high-order and bifactor models were not significantly different. However, the four-factor high-order model without constraints showed a significantly worse fit than the reference models (ΔCFI/ΔTLI > .01). In addition, the five-factor high-order models showed significantly poorer fit than the reference models (four-factor high-order and bifactor models). It is important to note that a factorial structure that shows a significantly lower fit than other models does not necessarily mean that the model should be rejected, but it is certainly an aspect that should be considered along with other evaluations beyond the statistical results. Inter-Index Comparisons on WISC-V: Primary and Complementary Indices Mauchly's test indicated that the sphericity assumption was violated for both the ANOVA with the primary indices (W = 0.85, χ 2 (9) = 38.5, p < .001) and the ANOVA with the complementary indices (W = 0.23, χ 2 (2) = 350.9, p < .001). Therefore, degrees of freedom were corrected using Huynh-Feldt (ε = 0.940) for the ANOVA with the primary indices and Greenhouse-Geisser estimates of sphericity (ε = 0.546) for the ANOVA with the complementary indices. All indices met the normality assumption (z-values ranging from -1.94 to 2.45 for kurtosis and 0.17 to 2.03 for skewness). The ANOVA revealed a significant main effect of the within-subjects factor, F(3.76,903) = 62.1, p < .001, ω 2 = .12, indicating substantial differences among the primary indices. Post-hoc comparisons using the t-tests with Holm correction showed that the mean scores for the Working Memory (WMI; M = 90.9, SD = 11.9) and Processing Speed (PSI; M = 90.8, SD = 12.8) indices were significantly lower than those for the Verbal Comprehension (VCI; M = 101.4, SD = 12.5), Visospatial (VSI; M = 99.3, SD = 12.7) and Fluid Reasoning (FRI; M = 100.0, SD = 12.5) indices. Curiously, the post-hoc comparisons revealed identical effect sizes for both the Working Memory Index (WMI) and the Processing Speed Index (PSI) when compared to the other three indices. Specifically, the Cohen's d for the comparison between WMI and VCI was d = 0.85, identical to that of PSI and VCI. Similarly, the effect size for WMI versus VSI was d = 0.68, which was the same for PSI versus VSI. Finally, the comparison between WMI and FRI yielded an effect size of d = 0.73, matching the effect size for PSI versus FRI. This alignment of effect sizes across these comparisons is noteworthy and appears to be coincidental. None of the other comparisons between the indices reached statistical significance. As expected from the results of the primary indices, the complementary indices also showed significant differences (F(1.13,237.51) = 155.3, p < .001, ω 2 = .14). The Cognitive Proficiency Index (CPI; M = 89.0, SD = 11.9) was significantly lower than both the General Ability Index (GAI; M = 100.1, SD = 12.0) and the Full Scale IQ (FSIQ; M = 96.8, SD = 11.5). Effect sizes were large for the difference between CPI and GAI (d = 0.95) and medium for the difference between CPI and FSIQ (d = 0.67). Although the difference between FSIQ and GAI was statistically significant, it had a small effect size (d = -0.28). As expected, the mean scores for the WMI and PSI indices were significantly lower than those for the VCI, VSI, and FRI (see Figure 2). Given that the complementary indices are derived from similar subtest results, the GAI was notably lower, especially compared to the CPI, which had a large effect size (see Figure 3). Discussion The primary objective of this study was to provide support for the hypothesis that Wechsler tests (specifically, the WISC-V) may contribute to ADHD assessment in children and adolescents. This would be achieved by identifying potential patterns in the primary or complementary indices of the WISC-V that may be associated with ADHD. To address this, two secondary objectives were established. First, to explore the construct validity of the WISC-V according to both the Wechsler structure and alternative structural models when applied to a sample of children and adolescents with a recent ADHD diagnosis. Second, if the construct validity of the WISC-V was confirmed for the ADHD population, it aimed to examine whether significant differences would emerge between the mean scores of the primary and complementary indices. This would allow us to hypothesize a distinct cognitive profile characteristic of children and adolescents with ADHD, as measured by the WISC-V. Based on our results, two key conclusions were drawn regarding the factorial structure of the Wechsler scale. First, the models that included four factors, whether hierarchical or bifactorial, showed a significantly better fit than the five-factor models. Second, the bifactorial structure (4 + 1) generally showed a better overall fit, although the difference was not significant when compared to the four-factor hierarchical model, at least within the ADHD population. These results are consistent with those of previous studies in which bifactorial models showed a better fit than other structural models, both for WISC-IV [16, 18, 43] and WISC-V [40–42, 46]. However, contrary to the findings of Fenollar-Cortés & Watkins [72], the model-based reliability statistics of the 4 + 1 bifactorial model in our study do not support the conclusion that the general factor ( g ) is more suitable for use than the primary indices (see supplementary materials). Furthermore, since the fit between the four-factor hierarchical model (with constraints applied to group factors with only two indicators) and the bifactorial model (4 + 1) did not differ significantly and there was no substantial improvement according to the fit indices, the principle of parsimony suggests that the hierarchical four-factor model with one higher-order factor would be the preferred choice. Nonetheless, although the five-factor hierarchical models (5 + 1) showed a significantly poorer fit than the four-factor models (4 + 1), their fit was still adequate, suggesting that they may be viable for clinical use. Regarding the first objective, we conclude that the hierarchical five-factor model (Verbal Comprehension, Visual Spatial, Fluid Reasoning, Working Memory, and Processing Speed indices), along with a general factor (Full-Scale IQ), as originally proposed by Wechsler [8], shows adequate psychometric properties in children and adolescents with ADHD. However, from a statistical perspective, alternative factorial structures provide a significantly better fit. In broader clinical practice, there are clear benefits for mental health professionals and school psychologists in using the indices suggested by the original WISC-V structure. Our results suggest that the mean scores of the Working Memory Index and Processing Speed Index were significantly lower than those of the Verbal Comprehension and Perceptual Reasoning indices, which is consistent with previous studies [28]. Similarly, these findings were reflected in the significantly lower scores on the Cognitive Proficiency Index compared to the General Ability Index. Studies such as Toffalini et al. [12] propose that clinicians may be inclined to consider the presence of a neurodevelopmental disorder, particularly ADHD, when scores on the Working Memory Index and Processing Speed Index are significantly lower than those on the Verbal Comprehension and Perceptual Reasoning indices. Furthermore, these authors suggest that the indices of the WISC-IV may serve as useful tools in distinguishing between individuals with ADHD and typically developing individuals. Thaler et al. [14] hypothesize that inattention and other functional variables may be related to scores on the Working Memory and Processing Speed indices, with a particular emphasis on the impairment of these indices in the clinical profiles of individuals with ADHD. However, altough in our sample the mean scores for Working Memory and Processing Speed were significantly lower than the other primary indices, this does not mean that individuals with ADHD necessarily score low on these indices. In contrast to other studies that, despite finding a pattern similar to ours using earlier versions of the WISC scale, argue that the differences between the indices are too small to be clinically useful (e.g., [29]), our results show significant differences with meaningful effect sizes. Rather, if a pattern can be identified, it would point towards a "cognitive gap" where there is an intrasubject discrepancy, regardless of whether the absolute scores are high or low. Observing this discrepancy in the Wechsler indices in children and adolescents should not be considered a condition for diagnosing ADHD, nor should it be a requirement for a comprehensive ADHD evaluation (e.g., [13]). Rather, it may support the hypothesis of a cognitive profile associated with ADHD that complements other neuropsychological assessments used in the diagnosis of the disorder. The conclusions of this study do not invalidate the findings of Fenollar-Cortés & Watkins [72]. Furthermore, the current study builds on the work of Styck & Watkins [18], which recommends focusing on the general intelligence factor ( g ) when using the WISC-IV to assess individuals with ADHD, while still acknowledging the potential clinical relevance of the other primary indices in providing valuable information. We recommend that psychologists using the WISC-V should exercise caution in interpreting the results, particularly when making decisions about ADHD diagnoses, and avoid relying on it as a necessary component in the evaluation of ADHD. However, we advocate a less restrictive approach to the interpretation of the WISC-V results when applied to individuals with ADHD. Additionally, it is important to consider the practical limitations that psychologists face when translating findings from scientific studies into daily clinical practice, especially when these findings are based on statistical nuances that may be difficult to grasp. Our study has several limitations. The sample size may seem too small to conduct CFAs and structural equation modeling analyses. Sample size is critical in structural equation modeling because it affects the statistical power and precision of the model’s parameter estimates. However, there is no single rule for determining the most appropriate sample size, and the determination depends on many factors (e.g., [73]). Bentler & Chou [73] suggested using at least five cases/observations per free parameter in a model ( N: q ≥ 5). The ratio of cases/observations per free parameter in the current study is 24.6; thus, we consider the sample size sufficient for this research. Another potential limitation of the study is the lack of rating of core symptoms of ADHD (i.e., inattention and hyperactivity/impulsivity dimensions), which would allow more in-depth analyses of the relationship between the WISC-V indices and ADHD symptom levels. Therefore, future studies should include larger sample sizes and ADHD symptom ratings. In addition, it would be interesting to include model-based reliability analyses in future research. Finally, it should be noted that the evaluations were conducted exclusively with the WISC-V Spain , so the results should be replicated with other versions of the scale. Conclusions In summary, although our results support the potential clinical utility of the WISC-V in the cognitive assessment of individuals with ADHD, we agree with the recommendation to avoid overinterpretation of the WISC-V scores [49]. In other words, the possibility of including the WISC-V in the cognitive assessment of ADHD must consider current doubts regarding the construct validity of the scale. However, our results suggest that despite these doubts about the optimal structural model for the WISC-V, the indices proposed by Wechsler meet the psychometric requirements. Rather than focusing on WMI, PSI, or CPI scores in isolation, the most promising approach to identifying ADHD profiles in children and adolescents is to detect an intrasubject pattern characterized by significantly lower scores on the WMI and PSI relative to other primary indices, and significantly lower scores on the CPI relative to the GAI. This creates a kind of 'cognitive gap' in the WISC-V indices, which could help to characterize the performance of children and adolescents with ADHD. Nevertheless, it is recommended that the clinical utility of the WISC-V in screening for ADHD be further explored. In conclusion, this study provides empirical evidence to support the use of the WISC-V in the cognitive assessment of children and adolescents diagnosed with ADHD. Declarations Ethics approval and consent to participate Written informed consent was obtained from all participants (or their legal guardians for minors) at each clinic participating in the study. The study was approved by the Ethics Committee of Universidad de Alicante (UA-2023-06-30_1). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Consent for publication Not applicable Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests No potential conflict of interest was reported by the authors. Funding There is no funding associated with the work featured in this article. Authors' contributions Javier Fenollar-Cortés : Conceptualization, Methodology, Formal Analysis, Investigation, Data Curation, Writing – Original Draft, Visualization, Project Administration Aroa Caminero-Ruiz, Deseada Auxiliadora Ruiz-Aranda, Ignasi Navarro-Soria, Rocio Lavine-Cerván : Investigation Carlos López-Pinar : Investigation, Data Curation, Writing - Review & Editing Acknowledgements Not applicable References American Psychiatric Association. Diagnostic and statistical manual of mental disorders : DSM-5. Arlington, VA: American Psychiatric Association; 2013. Salari N, Ghasemi H, Abdoli N, Rahmani A, Shiri MH, Hashemian AH, et al. The global prevalence of ADHD in children and adolescents: a systematic review and meta-analysis. Ital J Pediatr. 2023;49. https://doi.org/10.1186/s13052-023-01456-1. Pievsky MA, McGrath RE. The Neurocognitive Profile of Attention-Deficit/Hyperactivity Disorder: A Review of Meta-Analyses. 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Eur Child Adolesc Psychiatry. 2016;25:677–99. https://doi.org/10.1007/s00787-015-0798-x. Weschler D. Wechsler Intelligence Scale for Children - Fifth Edition. Pearson Clinical Assessment; 2014. Rabin LA, Paolillo E, Barr WB. 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. 2016;31:206–30. https://doi.org/10.1093/arclin/acw007. Becker A, Daseking M, Koerner JKA. Cognitive profiles in the wisc-v of children with adhd and specific learning disorders. Sustainability (Switzerland). 2021;13:1–15. https://doi.org/10.3390/su13179948. Mayes SD, Calhoun SL. WISC-IV and WISC-III profiles in children with ADHD. J Atten Disord. 2006;9:486–93. https://doi.org/10.1177/1087054705283616. Toffalini E, Buono S, Cornoldi C. The structure, profile, and diagnostic significance of intelligence in children with ADHD are impressively similar to those of children with a specific learning disorder. Res Dev Disabil. 2022;129:104306. https://doi.org/10.1016/j.ridd.2022.104306. Ünal D, Mustafaoğlu Çiçek N, Çak T, Sakarya G, Artik A, Karaboncuk Y, et al. Comparative analysis of the WISC-IV in a clinical setting: ADHD vs. non-ADHD. Archives de Pediatrie. 2021;28:16–22. https://doi.org/10.1016/j.arcped.2020.11.001. Thaler NS, Bello DT, Etcoff LM. WISC-IV Profiles Are Associated With Differences in Symptomatology and Outcome in Children With ADHD. J Atten Disord. 2013;17:291–301. https://doi.org/10.1177/1087054711428806. Fenollar-Cortés J, López-Pinar C, Watkins MW. Structural validity of the Spanish Wechsler Intelligence Scale for Children–Fourth Edition in a large sample of Spanish children with attention-deficit hyperactivity disorder. Int J Sch Educ Psychol. 2019;7:2–14. https://doi.org/10.1080/21683603.2018.1474820. Gomez R, Vance A, Watson SD. Structure of the Wechsler Intelligence Scale for Children – Fourth Edition in a Group of Children with ADHD. Front Psychol. 2016;7. https://doi.org/10.3389/fpsyg.2016.00737. Nuñez A, Zink DN, Barchard KA, San Miguel LE, Allen DN. Factor Structure of the Wechsler Intelligence Scale for Children-Fourth Edition Spanish in a Clinical Sample. Arch Clin Neuropsychol. 2019;34:89–95. https://doi.org/10.1093/arclin/acy011. Styck KM, Watkins MW. Structural Validity of the WISC-IV for Students With ADHD. J Atten Disord. 2017;21:921–8. https://doi.org/10.1177/1087054714553052. Thaler NS, Barchard KA, Parke E, Jones WP, Etcoff LM, Allen DN. Factor Structure of the Wechsler Intelligence Scale for Children: Fourth Edition in Children with ADHD. J Atten Disord. 2015;19:1013–21. https://doi.org/10.1177/1087054712459952. Stephenson KG, Beck JS, South M, Norris M, Butter E. Validity of the WISC-V in Youth with Autism Spectrum Disorder: Factor Structure and Measurement Invariance. Journal of Clinical Child & Adolescent Psychology. 2021;50:669–81. https://doi.org/10.1080/15374416.2020.1846543. Rotsika V, Vlassopoulos M, Legaki L, Sini A, Rogakou E, Sakellariou K, et al. The WISC-III Profile in Greek Children with Learning Disabilities: Different Language, Similar Difficulties. Int J Test. 2009;9:271–82. https://doi.org/10.1080/15305050903106891. Watkins MW, Kush JC. Confirmatory Factor Analysis of the WISC-III for Students with Learning Disabilities. J Psychoeduc Assess. 2002;20:4–19. https://doi.org/10.1177/073428290202000101. MacAllister WS, Maiman M, Vasserman M, Fay-Mcclymont T, Brooks BL, Sherman EMS. The WISC-V in children and adolescents with epilepsy. Child Neuropsychology. 2019;25:992–1002. https://doi.org/10.1080/09297049.2019.1571181. Rowe EW, Dandridge J, Pawlush A, Thompson DF, Ferrier DE. Exploratory and confirmatory factor analyses of the WISC-IV with gifted students. School Psychology Quarterly. 2014;29:536–51. https://doi.org/10.1037/spq0000009. Lace JW, Merz ZC, Kennedy EE, Seitz DJ, Austin TA, Ferguson BJ, et al. Examination of five- and four-subtest short form IQ estimations for the Wechsler Intelligence Scale for Children-Fifth edition (WISC-V) in a mixed clinical sample. Appl Neuropsychol Child. 2022;11:50–61. https://doi.org/10.1080/21622965.2020.1747021. Bradley Burton D, Sepehri A, Hecht F, VandenBroek A, Ryan JJ, Drabman R. A Confirmatory Factor Analysis of the WISC-III in a Clinical Sample With Cross-validation in the Standardization Sample. Child Neuropsychology. 2001;7:104–16. https://doi.org/10.1076/chin.7.2.104.3130. Bodin D, Pardini DA, Burns TG, Stevens AB. Higher Order Factor Structure of the WISC-IV in a Clinical Neuropsychological Sample. Child Neuropsychology. 2009;15:417–24. https://doi.org/10.1080/09297040802603661. Fenollar-Cortés J, Navarro-Soria I, González-Gómez C, García-Sevilla J. Detección de perfiles cognitivos mediante WISC-IV en niños diagnosticados de TDAH: ¿Existen diferencias entre subtipos? Revista de Psicodidactica. 2015;20:157–76. https://doi.org/10.1387/RevPsicodidact.12531. Devena SE, Watkins MW. Diagnostic Utility of WISC-IV General Abilities Index and Cognitive Proficiency Index Difference Scores Among Children With ADHD. J Appl Sch Psychol. 2012;28:133–54. https://doi.org/10.1080/15377903.2012.669743. Willcutt EG, Betjemann RS, McGrath LM, Chhabildas NA, Olson RK, DeFries JC, et al. Etiology and neuropsychology of comorbidity between RD and ADHD: The case for multiple-deficit models. Cortex. 2010;46:1345–61. https://doi.org/10.1016/j.cortex.2010.06.009. Mayes SD, Calhoun SL, Crowell EW. Learning Disabilities and ADHD. J Learn Disabil. 2000;33:417–24. https://doi.org/10.1177/002221940003300502. Shanahan MA, Pennington BF, Yerys BE, Scott A, Boada R, Willcutt EG, et al. Processing Speed Deficits in Attention Deficit/Hyperactivity Disorder and Reading Disability. J Abnorm Child Psychol. 2006;34:584–601. https://doi.org/10.1007/s10802-006-9037-8. Theiling J, Petermann F. Neuropsychological Profiles on the WAIS-IV of Adults With ADHD. J Atten Disord. 2016;20:913–24. https://doi.org/10.1177/1087054713518241. Nuñez A, San Miguel L, Keene J, Donohue B, Allen DN. Deconstructing Cognitive Heterogeneity in Puerto Rican Spanish-Speaking Children With ADHD. Journal of the International Neuropsychological Society. 2020;26:714–24. https://doi.org/10.1017/S135561772000020X. Watkins MW, Canivez GL. Assessing the Psychometric Utility of IQ Scores: A Tutorial Using the Wechsler Intelligence Scale for Children–Fifth Edition. School Psych Rev. 2022;51:619–33. https://doi.org/10.1080/2372966X.2020.1816804. Canivez GL, Watkins MW, McGill RJ. Construct validity of the Wechsler Intelligence Scale For Children – Fifth UK Edition: Exploratory and confirmatory factor analyses of the 16 primary and secondary subtests. British Journal of Educational Psychology. 2019;89:195–224. https://doi.org/10.1111/bjep.12230. Dombrowski SC, Canivez GL, Watkins MW, Alexander Beaujean A. Exploratory bifactor analysis of the Wechsler Intelligence Scale for Children—Fifth Edition with the 16 primary and secondary subtests. Intelligence. 2015;53:194–201. https://doi.org/10.1016/j.intell.2015.10.009. Canivez GL, Dombrowski SC, Watkins MW. Factor structure of the WISC-V in four standardization age groups: Exploratory and hierarchical factor analyses with the 16 primary and secondary subtests. Psychol Sch. 2018;55:741–69. https://doi.org/10.1002/pits.22138. Canivez GL, Watkins MW, Good R, James K, James T. Construct validity of the Wechsler Intelligence Scale for Children – Fourth UK Edition with a referred Irish sample: Wechsler and Cattell–Horn–Carroll model comparisons with 15 subtests. British Journal of Educational Psychology. 2017;87:383–407. https://doi.org/10.1111/bjep.12155. Watkins MW, Dombrowski SC, Canivez GL. Reliability and factorial validity of the Canadian Wechsler Intelligence Scale for Children–Fifth Edition. Int J Sch Educ Psychol. 2018;6:252–65. https://doi.org/10.1080/21683603.2017.1342580. Fenollar-Cortés J, Watkins MW. Construct validity of the Spanish Version of the Wechsler Intelligence Scale for Children Fifth Edition (WISC-VSpain). Int J Sch Educ Psychol. 2019;7:150–64. https://doi.org/10.1080/21683603.2017.1414006. Canivez GL, Grieder S, Buenger A. Construct Validity of the German Wechsler Intelligence Scale for Children–Fifth Edition: Exploratory and Confirmatory Factor Analyses of the 15 Primary and Secondary Subtests. Assessment. 2021;28:327–52. https://doi.org/10.1177/1073191120936330. Kush JC, Canivez GL. Construct validity of the WISC–IV Italian edition: A bifactor examination of the standardization sample: Chi niente sa, di niente dubita. Int J Sch Educ Psychol. 2021;9:73–87. https://doi.org/10.1080/21683603.2019.1638854. Reverte I, Golay P, Favez N, Rossier J, Lecerf T. Structural validity of the Wechsler Intelligence Scale for Children (WISC-IV) in a French-speaking Swiss sample. Learn Individ Differ. 2014;29:114–9. https://doi.org/10.1016/j.lindif.2013.10.013. Canivez GL, Watkins MW, Dombrowski SC. Structural validity of the Wechsler Intelligence Scale for Children-Fifth Edition: Confirmatory factor analyses with the 16 primary and secondary subtests. Psychol Assess. 2017;29:458–72. https://doi.org/10.1037/pas0000358. Lecerf T, Canivez GL. Complementary exploratory and confirmatory factor analyses of the French WISC-V: Analyses based on the standardization sample. Psychol Assess. 2018;30:793–808. https://doi.org/10.1037/pas0000526. Lecerf T, Canivez GL. Exploratory Factor Analyses of the French WISC-V (WISC-V FR ) for Five Age Groups: Analyses Based on the Standardization Sample. Assessment. 2022;29:1117–33. https://doi.org/10.1177/10731911211005170. Pauls F, Daseking M. Revisiting the Factor Structure of the German WISC-V for Clinical Interpretability: An Exploratory and Confirmatory Approach on the 10 Primary Subtests. Front Psychol. 2021;12. https://doi.org/10.3389/fpsyg.2021.710929. Watkins MW, Canivez GL, Dombrowski SC, McGill RJ, Pritchard AE, Holingue CB, et al. Long-term stability of Wechsler Intelligence Scale for Children–fifth edition scores in a clinical sample. Appl Neuropsychol Child. 2022;11:422–8. https://doi.org/10.1080/21622965.2021.1875827. Egeland J, Andreassen T, Lund O. Factor structure of the new Scandinavian WISC-V version: Support for a five‐factor model. Scand J Psychol. 2022;63:1–7. https://doi.org/10.1111/sjop.12780. Kranzler JH, Maki KE, Benson NF, Eckert TL, Floyd RG, Fefer SA. How Do School Psychologists Interpret Intelligence Tests for the Identification of Specific Learning Disabilities? Contemp Sch Psychol. 2020;24:445–56. https://doi.org/10.1007/s40688-020-00274-0. Becker A, Daseking M, Kerner auch Koerner J. Cognitive Profiles in the WISC-V of Children with ADHD and Specific Learning Disorders. Sustainability. 2021;13:9948. https://doi.org/10.3390/su13179948. Kim H-Y. Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restor Dent Endod. 2013;38:52. https://doi.org/10.5395/rde.2013.38.1.52. JASP Team. JASP. 2022. Brown TA. Confirmatory Factor Analysis for Applied Research, Second Edition. Second Edition. The Guilford Press; 2015. Wechsler D. Wechsler Intelligence Scale for Children - Fifth Edition. Pearson Clinical Assessment; 2014. Fenollar-Cortés J, Watkins MW. Construct validity of the Spanish Version of the Wechsler Intelligence Scale for Children Fifth Edition (WISC-VSpain). Int J Sch Educ Psychol. 2019;7:150–64. https://doi.org/10.1080/21683603.2017.1414006. Kline RB. Principles and practice of structural equation modeling (4th ed.). The Guildford Press; 2016. Akaike H. Factor analysis and AIC. Psychometrika. 1987;52:317–32. https://doi.org/10.1007/BF02294359. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Modeling. 1999;6:1–55. https://doi.org/10.1080/10705519909540118. Akaike H. Factor Analysis and AIC. Psychometrika. 1987;52:317–32. https://doi.org/10.1007/BF02294359. Chen FF. Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance. Struct Equ Modeling. 2007;14:464–504. https://doi.org/10.1080/10705510701301834. Anderson DR. Model based inference in the life sciences: A primer on evidence. Springer; 2008. Byrne BM. Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Psychology Press; 2014. Jaccard J, Wan CK. LISREL approaches to interaction effects in multiple regression. SAGE; 1996. Watkins MW, Dombrowski SC, Canivez GL. Reliability and factorial validity of the Canadian Wechsler Intelligence Scale for Children–Fifth Edition. Int J Sch Educ Psychol. 2018;6:252–65. https://doi.org/10.1080/21683603.2017.1342580. Kranzler JH, Floyd RG. Assessing intelligence in children and adolescents: A practical guide. Guildford Press; 2013. Hanckock GR, Mueller RO. Rethinking construct reliability within latent variable systems. In: Cudeck R, editor. Structural equation modeling: Present and future. Scientific Software International; 2001. p. 195–216. Cohen J. Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates; 1988. Thompson B. Effect sizes, confidence intervals, and confidence intervals for effect sizes. Psychol Sch. 2007;44:423–32. https://doi.org/10.1002/pits.20234. Field A. Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE Publications; 2013. Fenollar-Cortés J, Watkins MW. Construct validity of the Spanish Version of the Wechsler Intelligence Scale for Children Fifth Edition (WISC-V). Int J Sch Educ Psychol. 2019;7:150–64. https://doi.org/10.1080/21683603.2017.1414006. Bentler PM, Chou C. Practical Issues in Structural Modeling. Sociol Methods Res. 1987;16:78–117. https://doi.org/10.1177/0049124187016001004. Tables Table 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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ADHD is one of the most prevalent mental disorders among children and adolescents, with an estimated worldwide prevalence of 5.6-8%\u0026nbsp;[2]\u0026nbsp;The core symptoms of ADHD often co-occur with deficits in specific neurocognitive domains such as reaction time variability, vigilance, working memory, response inhibition, and intelligence/achievement\u0026nbsp;[3]. Thus, in addition to behavioral measures to assess ADHD, a wide range of neuropsychological measures have been proposed as clinical tools to discriminate ADHD profiles, but with poor results (e.g.,\u0026nbsp;[4, 5]). Widely used continuous performance tests (CPTs) had slightly better results regarding their use as ADHD diagnostic measures\u0026nbsp;[6, 7].\u003c/p\u003e\n\u003cp\u003eIn addition to neuropsychological measures, the usefulness of including cognitive measures in the ADHD assessment process has also been explored. The Wechsler Intelligence Scale for Children-Fifth Edition (WISC-V;\u0026nbsp;[8])\u0026nbsp;is the most widely used cognitive assessment\u0026nbsp;tool for children and adolescents\u0026nbsp;[9]. Research on the relationship between ADHD and the WISC can be divided into two approaches: those looking for specific cognitive profiles in the WISC for ADHD samples\u0026nbsp;[10\u0026ndash;14]\u0026nbsp;and those exploring the construct validity of the WISC in ADHD samples\u0026nbsp;[15\u0026ndash;19]. Similar studies have explored the relationship between the Wechsler scales and various disorders or medical conditions,\u0026nbsp;such\u0026nbsp;as autism spectrum disorder\u0026nbsp;[20], specific learning disabilities\u0026nbsp;[21, 22], epilepsy\u0026nbsp;[23], gifted students\u0026nbsp;[24], or in mixed clinical samples (e.g.,\u0026nbsp;[23, 25\u0026ndash;27]).\u003c/p\u003e\n\u003cp\u003eSeveral studies have investigated the possible inclusion of the Wechsler Intelligence Scale for Children (WISC) as part of a comprehensive assessment of ADHD in childhood and adolescence. Fenollar-Cort\u0026eacute;s et al.\u0026nbsp;[28]\u0026nbsp;identified a cognitive pattern in individuals with ADHD, characterized by significantly lower Working Memory (WMI) and Processing Speed (PSI) scores compared to Verbal Comprehension (VCI) and Perceptual Reasoning (PRI) on the WISC-IV. This pattern has been observed across different studies (e.g.,\u0026nbsp;[18, 29]), reinforcing the notion that deficits in WMI and PSI are hallmarks of the ADHD cognitive profile. Additionally, these deficits have been studied in comorbid conditions, such as Specific Learning Disorders (SLD), as children with ADHD often show similar cognitive impairments\u0026nbsp;[30, 31]. Deficits in Working Memory have been particularly noted in children with both ADHD and SLD\u0026nbsp;[32], while significant impairments in processing speed have also been reported\u0026nbsp;[32]. Moreover, Thaler et al.\u0026nbsp;[19]\u0026nbsp;found that children with ADHD tend to have lower PSI and WMI scores, supporting the five-factor model proposed by Keith and adopted in the WISC-V\u0026nbsp;[8]. While the diagnostic utility of these differences remains debated\u0026nbsp;[29], other studies have found that these deficits persist into adulthood\u0026nbsp;[33]. Overall, these findings suggest that the presence of cognitive deficits on WMI and PSI may be an important aspect of the ADHD profile, although more research is needed to explore the heterogeneity of cognitive profiles\u0026nbsp;[34].\u003c/p\u003e\n\u003cp\u003eHowever, before the different scores of the WISC-V can be used in clinical practice to discriminate specific clinical profiles, the psychometric properties of the scale, particularly reliability and dimensionality, must be adequate. Consistent with this, Watkins \u0026amp; Canivez\u0026nbsp;[35]\u0026nbsp;recommended that clinicians \u0026quot;go beyond structural goodness of fit and evaluate IQ test scores in terms of their reliability and ability to provide information that is not available from the general ability score as well as their predictive and treatment validity\u0026quot; (p. 619). Thus, numerous studies of the factorial structure of the Wechsler scales conclude that only the Full-Scale Intelligence Quotient (FSIQ) score may be sufficiently reliable for clinical use\u0026nbsp;[36\u0026ndash;39]. Moreover, similar results have also been found in studies examining the construct validity of the WISC-IV and the WISC-V in populations from different countries (e.g.,\u0026nbsp;[36, 40\u0026ndash;44])\u0026nbsp;The suitability of the higher-order five-factor structure of the WISC-V over other alternative structural models (e.g., bifactorial or higher-order four-factor models) is still under debate\u0026nbsp;[36, 40, 45\u0026ndash;48]. Moreover, there are some doubts regarding the long-term stability of WISC-V in clinical samples\u0026nbsp;[49]. Regardless of whether the most appropriate factorial structure for the WISC-V is a four\u0026nbsp;or five-factor model, and a hierarchical or bifactorial structure, the controversy may be more related to different views of the structure of intelligence, which would lead researchers to choose other statistical methods of factor analysis\u0026nbsp;[50]. Beyond the methodological aspects, which are indeed relevant, and given the existence of a considerable research-to-practice gap\u0026nbsp;[51], it would be interesting to provide professional clinical psychologists, school psychologists, and mental health professionals with clear and straightforward guidelines regarding the use of the WISC in daily professional practice with children and adolescents diagnosed with ADHD.\u003c/p\u003e\n\u003cp\u003eThe overall objective of this study was to explore whether the WISC-V could be considered a valid instrument for inclusion as an additional tool in the assessment of ADHD in children and adolescents. However, before using the WISC-V among individuals with ADHD, the psychometric properties of the scale need to be established before profile analyses can be conducted (especially if construct validity is unclear). To this end, the present study established two main objectives: first, to explore the construct validity of the WISC-V in a population of children and adolescents recently diagnosed with ADHD, examining both its original structure and alternative structures proposed in previous scientific literature; second, contingent on the first objective, to investigate whether significant differences exist between the primary indices of the scale, as well as certain complementary indices, in order to propose a cognitive pattern associated with children and adolescents with ADHD, as measured by the WISC-V.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study included 241 children and adolescents between the ages of 6 and 17 who had recently been diagnosed with ADHD. The mean age of the participants was 9.92 years (SD = 2.87). The sample included a higher proportion of males (76.8%). Statistical analyses revealed no significant age differences between male and female participants (\u003cem\u003ep\u003c/em\u003e = .725), ensuring that age-related factors did not differ across genders.\u003c/p\u003e\n\u003cp\u003eParticipants were recruited from child and adolescent mental health clinics. The diagnostic test battery included behavioral measures, executive function measures, reading and math performance measures, neuropsychological measures, and academic performance outcomes. ADHD diagnoses were confirmed by a child and adolescent psychiatrist or a neuropsychiatrist from different public child mental health centers in Madrid, Valencia, and Alicante. The WISC-V was part of this systematic psychological assessment battery. Inclusion criteria were being between 6 and 17 years of age, an FSIQ score of 70 or greater, and no severe neurological or psychological problems that could affect performance on the WISC-V\u0026nbsp;[52]. The ADHD diagnosis was later confirmed by a mental health professional outside the research team. Participants with comorbid disorders (other than specific learning disorders) were excluded from the study. Participants in the typically developing group had to be clinically assessed and\u0026nbsp;not have relevant clinical symptoms or a previous\u0026nbsp;mental health diagnosis at the time of the study. All participants were required to complete the 10 primary tests of the WISC-V. Families were informed about the study and signed\u0026nbsp;informed consent\u0026nbsp;forms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe WISC-V is a norm-referenced, individually administered intelligence battery focused on children and adolescents aged 6\u0026ndash;16 years. According to the WISC-V manual, Cattell-Horn-Carroll (CHC) theory, neurodevelopmental research, and clinical utility were considered in its development. These frameworks can be used to interpret WISC-V and WISC-V\u003csup\u003eSpain\u003c/sup\u003e scores.\u003c/p\u003e\n\u003cp\u003eThis instrument contains ten primary and five secondary subtests (\u003cem\u003eM\u003c/em\u003e = 10, \u003cem\u003eSD\u003c/em\u003e = 5). The primary index scales (\u003cem\u003eM\u0026nbsp;\u003c/em\u003e= 100, \u003cem\u003eSD\u003c/em\u003e = 15) are computed as follows: Similarities (SI) and Vocabulary (VO) subtests create the Verbal Comprehension Index (VCI); Block Design (BD) and Visual Puzzles (VP) subtests create the Visual Spatial Index (VS); Matrix Reasoning (MR) and Figure Weights (FW) subtests create the Fluid Reasoning Index (FR); Digit Span (DS) and Picture Span (PS) subtests create the Working Memory Index (WMI); and Coding (CD) and Symbol Search (SS) subtests create the Processing Speed Index (PSI). The Full-Scale IQ is computed using only seven primary subtests: SI, VO, BD, MR, FW, DS, and CD. The five secondary subtests are suggested to load on the same factors as the primary subtests: Information (IN) and Comprehension (CO) on the VC factor, Arithmetic (AR) on the FR factor, Letter-Number Sequencing (LN) on the WM factor, and Cancellation (CA) on the PS factor.\u003c/p\u003e\n\u003cp\u003eHowever, alternative models have been proposed depending on the number of latent variables and the number of indicator loading combinations (single and cross-loadings) (Table 1). Most include a g-factor as a second-order latent variable (on which all first-order latent variables are loaded) or a first-order factor (on which all indicators are loaded).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData distribution was explored using normal Q-Q plots and \u003cem\u003ez\u003c/em\u003e-values (skewness and kurtosis values divided by their standard errors; \u003cem\u003ez\u003c/em\u003e-value \u0026gt; |3.29| indicates data are not normally distributed)\u0026nbsp;[53]. The \u003cem\u003eK\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e statistic was used to assess multivariate normality (the statistic is computed by summing the squared z-scores for kurtosis and skewness; If the value of \u003cem\u003eK\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e exceeds 5.99, the null hypothesis of normality is rejected at the 5% significance level, suggesting multivariate non-normality).\u003c/p\u003e\n\u003cp\u003eAll confirmatory factor analyses (CFAs) were conducted using JASP software\u0026nbsp;[54]\u0026nbsp;from the raw data using the maximum likelihood (ML) estimator. Latent variable scales were identified by setting a reference indicator in the higher-order models and the variance of latent variables in the bifactor models\u0026nbsp;[55]. Parameter estimates were constrained to equality in the models with only two indicators per factor, and in the higher-order models, the model fit was assessed both with and without these constraints.\u0026nbsp;Parameter estimates were constrained to equality in the models with only two indicators per factor, and in the higher-order models, the model fit was assessed both with and without these constraints. Consistent with previous WISC-IV and WISC-V factorial analyses, only higher-order and bifactor models were examined because the WISC-IV and the WISC-V score structures (i.e., implied by an FSIQ score) indicate a hierarchical structure. The evaluated models were taken from Weschler\u0026nbsp;[56]\u0026nbsp;and are detailed in Table 1. Following\u0026nbsp;Fenollar-Cort\u0026eacute;s \u0026amp; Watkins\u0026nbsp;[57], only\u0026nbsp;the bifactor versions of Models 4a and 5a\u0026nbsp;(with correlated FR and VS factors) were computed.\u003c/p\u003e\n\u003cp\u003eAs recommended by Kline [58], global model fit was evaluated using multiple fit indices: model chi-square (\u0026chi;\u003csup\u003e2\u003c/sup\u003e), comparative fit index (CFI), Tucker\u0026ndash;Lewis index (TLI), root mean square error of approximation (RMSEA), the standardized root mean square residual (SRMR), and Akaike\u0026rsquo;s information criterion (AIC; Akaike [59]). Based on the combinational rules of Hu \u0026amp; Bentler [60], a good fit required CFI/TLI \u0026ge; .95 and RMSEA \u0026le; .06. For AIC, lower values identify models that are more likely to be generalizable [61]. Meaningful differences between well-fitting models were also evaluated using \u0026Delta;CFI/\u0026Delta;TLI \u0026ge; .01, \u0026Delta;RMSEA \u0026ge; .015, \u0026Delta;SRMR \u0026ge; .03 [62], \u0026Delta;AIC \u0026ge; 10 [63], and non-significant \u0026Delta;\u0026chi;\u003csup\u003e2\u003c/sup\u003e. In addition to the goodness of fit indices, the standardized residuals, modification indices, localized areas of strain, interpretability, and size of the parameter estimates were examined for each model, as acceptable overall goodness-of-fit indices may mask problems in some indicator relationships, especially in complex models\u003cem\u003e\u0026nbsp;\u003c/em\u003e[55]. The absence of localized areas of poor fit in the solution was considered if the Z value was\u0026nbsp;≦\u0026nbsp;2.58\u0026nbsp;[64]\u0026nbsp;and the Modification indices were\u0026nbsp;≦\u0026nbsp;4.00\u0026nbsp;[65].\u003c/p\u003e\n\u003cp\u003eOmega coefficients were calculated for model-based reliability of the bifactor models\u0026nbsp;[66]. The most comprehensive omega coefficient is the total omega (\u0026omega;), which estimates the proportion of variance in the observed total score that can be attributed to all sources of common variance included in the model. High omega values indicate a highly reliable multidimensional total score. The omega subscale (\u0026omega;\u003csub\u003es\u003c/sub\u003e) was also computed for each unit-weighted subscale score, which indexes the proportion of variance in each unit-weighted subscale score attributable to a mixture of general and group factor variance. High \u0026omega;\u003csub\u003es\u003c/sub\u003e values indicate a highly reliable multidimensional group factor score. Values below .90 were considered unacceptable for decision-making about individuals, similar to alpha values\u0026nbsp;[67].\u0026nbsp;Additionally, Hancock \u0026amp; Mueller\u0026apos;s\u0026nbsp;[68]\u0026nbsp;\u003cem\u003eH\u003c/em\u003e coefficient was calculated to provide another perspective on construct reliability. The \u003cem\u003eH\u003c/em\u003e coefficient represents the correlation between a factor and an optimally weighted composite score, indicating how well a latent variable is represented by its indicators. \u003cem\u003eH\u003c/em\u003e coefficient values below .70 were considered insufficient. In the case of high-order models, \u0026omega; was also calculated, which estimates the proportion of variance in the observed scores attributable to the second-order factor and its derived first-order factors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the case of the high-order models with five Wechsler factors, we explored the potential existence of significant differences among the different subscales (VC, RF, VS, WM, and PS), as well as other indices such as GAI and ICC, to determine whether the mean scores on these measures differed within the ADHD population. To accomplish this, a one-way repeated measures ANOVA was conducted, followed by the appropriate post-hoc analyses. The effect size was estimated using omega squared (\u0026omega;\u0026sup2;) for the ANOVA and Cohen\u0026apos;s \u003cem\u003ed\u003c/em\u003e for the post-hoc \u003cem\u003et\u003c/em\u003e-tests. A commonly used interpretation refers to effect sizes as small (\u003cem\u003ed\u003c/em\u003e = 0.2; \u0026omega;\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= .01), medium (\u003cem\u003ed\u003c/em\u003e = 0.5; \u0026omega;\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= .06), and large (\u003cem\u003ed\u0026nbsp;\u003c/em\u003e= 0.8; \u0026omega;\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= .14)\u0026nbsp;based on benchmarks suggested by Cohen\u0026nbsp;[69]. However, these values are arbitrary and should not be\u0026nbsp;interpreted\u0026nbsp;rigidly\u0026nbsp;[70].\u003c/p\u003e\n\u003cp\u003eTwo one-way repeated measures ANOVAs were conducted with a within-subjects factor of subscale: the first ANOVA was conducted on the Primary indices, and the second ANOVA was conducted on the FSIQ, the General Ability Index, and the Cognitive Proficiency Index. This approach was chosen because it takes into account the within-subject correlations inherent in repeated assessments, allowing for a more accurate analysis of differences across the subscales within the same group of participants. This method provides a sensitive test for detecting variations in cognitive performance across the different domains measured by the WISC-V.\u003cem\u003e\u0026nbsp;\u003c/em\u003ePost-hoc analyses were conducted using Holm\u0026apos;s correction, as it provides an appropriate balance between controlling for Type I error and maintaining robust statistical power.\u003c/p\u003e\n\u003cp\u003eSphericity was assessed using Mauchly\u0026apos;s test. In cases where Mauchly\u0026apos;s test indicated a violation of the sphericity assumption (\u003cem\u003ep\u003c/em\u003e \u0026lt; .05), the Greenhouse-Geisser and Huynh-Feldt corrections were applied. The ϵ values for both Greenhouse-Geisser and Huynh-Feldt were calculated to adjust the degrees of freedom accordingly. Following the guidelines recommended by Field [71], the Greenhouse-Geisser correction was used when ϵ \u0026lt; 0.75, and the Huynh-Feldt correction was used when ϵ \u0026gt; 0.75 to ensure appropriate adjustment for sphericity violation. The distribution of the values corresponding to the WISC indices (Primary, General Ability, and Cognitive Proficiency indices) was examined using normal Q-Q plots and z-values.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe variables exhibited univariate normality, as evidenced by z-values ranging from -1.12 to 2.54 for kurtosis and -0.41 to 2.53 for skewness. Multivariate normality of the data distribution was also established (\u003cem\u003eK\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e values from 0.35 to 4.98).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfirmatory Factor Analysis and Multi-sample Invariance Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUpon examining the model fit, only the four-factor models\u0026mdash;in which factors with two indicators were constrained to equality for identification, whether within high-order or bifactor structures\u0026mdash;strictly met the predefined fit criteria (see Table 2 and Figure 1). However, the four-factor high-order model with no constrained factors had a low TLI value (\u0026lt; .971), while the other fit indices remained within acceptable ranges. For the five-factor models, the constrained indicators version met the minimum fit requirements, although the upper limit of the RMSEA confidence interval slightly exceeded the threshold (90% CI RMSEA, .018\u0026ndash;.071). This suggests that while the model generally meets the criteria for good fit, caution is warranted. Greater caution should be exercised with the five-factor high-order model without constraints, as its TLI = .947 (which could be rounded to .95), but it shares the same issue regarding the upper limit of the RMSEA confidence interval (.018\u0026ndash;.074). Finally, the five-factor bifactor model did not fit adequately due to negative residual variance estimates. Although restrictions were applied to address this, it ultimately renders the model unsuitable for selection.\u003c/p\u003e\n\u003cp\u003eAfter evaluating the fit of each model with different factorial structures and constraints, we proceeded to assess whether any model was significantly superior and, therefore, preferable for selection. Based on the previously established cut-off points, we can conclude that the four-factor high-order and bifactor models were not significantly different. However, the four-factor high-order model without constraints showed a significantly worse fit than the reference models (\u0026Delta;CFI/\u0026Delta;TLI \u0026gt; .01). In addition, the five-factor high-order models showed significantly poorer fit than the reference models (four-factor high-order and bifactor models). It is important to note that a factorial structure that shows a significantly lower fit than other models does not necessarily mean that the model should be rejected, but it is certainly an aspect that should be considered along with other evaluations beyond the statistical results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInter-Index Comparisons on WISC-V: Primary and Complementary Indices\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMauchly\u0026apos;s test indicated that the sphericity assumption was violated for both the ANOVA with the primary indices (W\u0026nbsp;= 0.85, \u0026chi;\u003csup\u003e2\u003c/sup\u003e(9) = 38.5,\u0026nbsp;p\u0026nbsp;\u0026lt; .001) and the ANOVA with the complementary indices\u0026nbsp;(W\u0026nbsp;= 0.23, \u0026chi;\u003csup\u003e2\u003c/sup\u003e(2) = 350.9,\u0026nbsp;p\u0026nbsp;\u0026lt; .001). Therefore, degrees of freedom were corrected using Huynh-Feldt (\u0026epsilon; = 0.940) for the ANOVA with the primary indices and Greenhouse-Geisser estimates of sphericity (\u0026epsilon; = 0.546) for the ANOVA with the complementary indices. All indices met the normality assumption (z-values ranging from -1.94 to 2.45 for kurtosis and 0.17 to 2.03 for skewness).\u003c/p\u003e\n\u003cp\u003eThe ANOVA revealed a significant main effect of the within-subjects factor,\u0026nbsp;F(3.76,903) = 62.1,\u0026nbsp;p\u0026nbsp;\u0026lt; .001, \u0026omega;\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= .12, indicating substantial differences among the primary indices. Post-hoc comparisons using the\u0026nbsp;t-tests with Holm correction showed that the mean scores for the Working Memory (WMI;\u0026nbsp;M\u0026nbsp;= 90.9,\u0026nbsp;SD\u0026nbsp;= 11.9) and Processing Speed (PSI;\u0026nbsp;M\u0026nbsp;= 90.8,\u0026nbsp;SD\u0026nbsp;= 12.8) indices were significantly lower than those for the Verbal Comprehension (VCI;\u0026nbsp;M\u0026nbsp;= 101.4,\u0026nbsp;SD\u0026nbsp;= 12.5), Visospatial (VSI;\u0026nbsp;M\u0026nbsp;= 99.3,\u0026nbsp;SD\u0026nbsp;= 12.7) and Fluid Reasoning (FRI;\u0026nbsp;M\u0026nbsp;= 100.0,\u0026nbsp;SD\u0026nbsp;= 12.5) indices. Curiously, the post-hoc comparisons revealed identical effect sizes for both the Working Memory Index (WMI) and the Processing Speed Index (PSI) when compared to the other three indices. Specifically, the Cohen\u0026apos;s\u0026nbsp;d\u0026nbsp;for the comparison between WMI and VCI was\u0026nbsp;d\u0026nbsp;= 0.85, identical to that of PSI and VCI. Similarly, the effect size for WMI versus VSI was\u0026nbsp;d\u0026nbsp;= 0.68, which was the same for PSI versus VSI. Finally, the comparison between WMI and FRI yielded an effect size of\u0026nbsp;d\u0026nbsp;= 0.73, matching the effect size for PSI versus FRI. This alignment of effect sizes across these comparisons is noteworthy and appears to be coincidental. None of the other comparisons between the indices reached statistical significance.\u003c/p\u003e\n\u003cp\u003eAs expected from the results of the primary indices, the complementary indices also showed significant differences\u0026nbsp;(F(1.13,237.51) = 155.3,\u0026nbsp;p\u0026nbsp;\u0026lt; .001,\u0026nbsp;\u0026omega;\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= .14). The Cognitive Proficiency Index (CPI;\u0026nbsp;M\u0026nbsp;= 89.0,\u0026nbsp;SD\u0026nbsp;= 11.9) was significantly lower than both the General Ability Index (GAI;\u0026nbsp;M\u0026nbsp;= 100.1,\u0026nbsp;SD\u0026nbsp;= 12.0) and the Full Scale IQ (FSIQ;\u0026nbsp;M\u0026nbsp;= 96.8,\u0026nbsp;SD\u0026nbsp;= 11.5). Effect sizes were large for the difference between CPI and GAI (d\u0026nbsp;= 0.95) and medium for the difference between CPI and FSIQ (d\u0026nbsp;= 0.67). Although the difference between FSIQ and GAI was statistically significant, it had a small effect size (d\u0026nbsp;= -0.28).\u003c/p\u003e\n\u003cp\u003eAs expected, the mean scores for the WMI and PSI indices were significantly lower than those for the VCI, VSI, and FRI (see Figure 2). Given that the complementary indices are derived from similar subtest results, the GAI was notably lower, especially compared to the CPI, which had a large effect size (see Figure 3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe primary objective of this study was to provide support for the hypothesis that Wechsler tests (specifically, the WISC-V) may contribute to ADHD assessment in children and adolescents. This would be achieved by identifying potential patterns in the primary or complementary indices of the WISC-V that may be associated with ADHD. To address this, two secondary objectives were established. First, to explore the construct validity of the WISC-V according to both the Wechsler structure and alternative structural models when applied to a sample of children and adolescents with a recent ADHD diagnosis. Second, if the construct validity of the WISC-V was confirmed for the ADHD population, it aimed to examine whether significant differences would emerge between the mean scores of the primary and complementary indices. This would allow us to hypothesize a distinct cognitive profile characteristic of children and adolescents with ADHD, as measured by the WISC-V.\u003c/p\u003e\u003cp\u003eBased on our results, two key conclusions were drawn regarding the factorial structure of the Wechsler scale. First, the models that included four factors, whether hierarchical or bifactorial, showed a significantly better fit than the five-factor models. Second, the bifactorial structure (4\u0026thinsp;+\u0026thinsp;1) generally showed a better overall fit, although the difference was not significant when compared to the four-factor hierarchical model, at least within the ADHD population. These results are consistent with those of previous studies in which bifactorial models showed a better fit than other structural models, both for WISC-IV [16, 18, 43] and WISC-V [40\u0026ndash;42, 46]. However, contrary to the findings of Fenollar-Cort\u0026eacute;s \u0026amp; Watkins [72], the model-based reliability statistics of the 4\u0026thinsp;+\u0026thinsp;1 bifactorial model in our study do not support the conclusion that the general factor (\u003cem\u003eg\u003c/em\u003e) is more suitable for use than the primary indices (see supplementary materials).\u003c/p\u003e\u003cp\u003eFurthermore, since the fit between the four-factor hierarchical model (with constraints applied to group factors with only two indicators) and the bifactorial model (4\u0026thinsp;+\u0026thinsp;1) did not differ significantly and there was no substantial improvement according to the fit indices, the principle of parsimony suggests that the hierarchical four-factor model with one higher-order factor would be the preferred choice. Nonetheless, although the five-factor hierarchical models (5\u0026thinsp;+\u0026thinsp;1) showed a significantly poorer fit than the four-factor models (4\u0026thinsp;+\u0026thinsp;1), their fit was still adequate, suggesting that they may be viable for clinical use.\u003c/p\u003e\u003cp\u003eRegarding the first objective, we conclude that the hierarchical five-factor model (Verbal Comprehension, Visual Spatial, Fluid Reasoning, Working Memory, and Processing Speed indices), along with a general factor (Full-Scale IQ), as originally proposed by Wechsler [8], shows adequate psychometric properties in children and adolescents with ADHD. However, from a statistical perspective, alternative factorial structures provide a significantly better fit. In broader clinical practice, there are clear benefits for mental health professionals and school psychologists in using the indices suggested by the original WISC-V structure.\u003c/p\u003e\u003cp\u003e Our results suggest that the mean scores of the Working Memory Index and Processing Speed Index were significantly lower than those of the Verbal Comprehension and Perceptual Reasoning indices, which is consistent with previous studies [28]. Similarly, these findings were reflected in the significantly lower scores on the Cognitive Proficiency Index compared to the General Ability Index. Studies such as Toffalini et al. [12] propose that clinicians may be inclined to consider the presence of a neurodevelopmental disorder, particularly ADHD, when scores on the Working Memory Index and Processing Speed Index are significantly lower than those on the Verbal Comprehension and Perceptual Reasoning indices. Furthermore, these authors suggest that the indices of the WISC-IV may serve as useful tools in distinguishing between individuals with ADHD and typically developing individuals. Thaler et al. [14] hypothesize that inattention and other functional variables may be related to scores on the Working Memory and Processing Speed indices, with a particular emphasis on the impairment of these indices in the clinical profiles of individuals with ADHD. However, altough in our sample the mean scores for Working Memory and Processing Speed were significantly lower than the other primary indices, this does not mean that individuals with ADHD necessarily score low on these indices. In contrast to other studies that, despite finding a pattern similar to ours using earlier versions of the WISC scale, argue that the differences between the indices are too small to be clinically useful (e.g., [29]), our results show significant differences with meaningful effect sizes. Rather, if a pattern can be identified, it would point towards a \"cognitive gap\" where there is an intrasubject discrepancy, regardless of whether the absolute scores are high or low.\u003c/p\u003e\u003cp\u003eObserving this discrepancy in the Wechsler indices in children and adolescents should not be considered a condition for diagnosing ADHD, nor should it be a requirement for a comprehensive ADHD evaluation (e.g., [13]). Rather, it may support the hypothesis of a cognitive profile associated with ADHD that complements other neuropsychological assessments used in the diagnosis of the disorder.\u003c/p\u003e\u003cp\u003eThe conclusions of this study do not invalidate the findings of Fenollar-Cort\u0026eacute;s \u0026amp; Watkins [72]. Furthermore, the current study builds on the work of Styck \u0026amp; Watkins [18], which recommends focusing on the general intelligence factor (\u003cem\u003eg\u003c/em\u003e) when using the WISC-IV to assess individuals with ADHD, while still acknowledging the potential clinical relevance of the other primary indices in providing valuable information. We recommend that psychologists using the WISC-V should exercise caution in interpreting the results, particularly when making decisions about ADHD diagnoses, and avoid relying on it as a necessary component in the evaluation of ADHD. However, we advocate a less restrictive approach to the interpretation of the WISC-V results when applied to individuals with ADHD. Additionally, it is important to consider the practical limitations that psychologists face when translating findings from scientific studies into daily clinical practice, especially when these findings are based on statistical nuances that may be difficult to grasp.\u003c/p\u003e\u003cp\u003eOur study has several limitations. The sample size may seem too small to conduct CFAs and structural equation modeling analyses. Sample size is critical in structural equation modeling because it affects the statistical power and precision of the model\u0026rsquo;s parameter estimates. However, there is no single rule for determining the most appropriate sample size, and the determination depends on many factors (e.g., [73]). Bentler \u0026amp; Chou [73] suggested using at least five cases/observations per free parameter in a model (\u003cem\u003eN: q\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;5). The ratio of cases/observations per free parameter in the current study is 24.6; thus, we consider the sample size sufficient for this research. Another potential limitation of the study is the lack of rating of core symptoms of ADHD (i.e., inattention and hyperactivity/impulsivity dimensions), which would allow more in-depth analyses of the relationship between the WISC-V indices and ADHD symptom levels. Therefore, future studies should include larger sample sizes and ADHD symptom ratings. In addition, it would be interesting to include model-based reliability analyses in future research. Finally, it should be noted that the evaluations were conducted exclusively with the WISC-V\u003csup\u003eSpain\u003c/sup\u003e, so the results should be replicated with other versions of the scale.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Conclusions","content":" \u003cp\u003eIn summary, although our results support the potential clinical utility of the WISC-V in the cognitive assessment of individuals with ADHD, we agree with the recommendation to avoid overinterpretation of the WISC-V scores [49]. In other words, the possibility of including the WISC-V in the cognitive assessment of ADHD must consider current doubts regarding the construct validity of the scale. However, our results suggest that despite these doubts about the optimal structural model for the WISC-V, the indices proposed by Wechsler meet the psychometric requirements. Rather than focusing on WMI, PSI, or CPI scores in isolation, the most promising approach to identifying ADHD profiles in children and adolescents is to detect an intrasubject pattern characterized by significantly lower scores on the WMI and PSI relative to other primary indices, and significantly lower scores on the CPI relative to the GAI. This creates a kind of 'cognitive gap' in the WISC-V indices, which could help to characterize the performance of children and adolescents with ADHD. Nevertheless, it is recommended that the clinical utility of the WISC-V in screening for ADHD be further explored. In conclusion, this study provides empirical evidence to support the use of the WISC-V in the cognitive assessment of children and adolescents diagnosed with ADHD.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all participants (or their legal guardians for minors) at each clinic participating in the study. The study was approved by the Ethics Committee of Universidad de Alicante (UA-2023-06-30_1). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no funding associated with the work featured in this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eJavier Fenollar-Cort\u0026eacute;s\u003c/em\u003e\u003c/strong\u003e: Conceptualization, Methodology, Formal Analysis, Investigation, Data Curation, Writing \u0026ndash; Original Draft, Visualization, Project Administration\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAroa Caminero-Ruiz, Deseada Auxiliadora Ruiz-Aranda, Ignasi Navarro-Soria, Rocio Lavine-Cerv\u0026aacute;n\u003c/em\u003e\u003c/strong\u003e: Investigation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCarlos L\u0026oacute;pez-Pinar\u003c/em\u003e\u003c/strong\u003e: Investigation, Data Curation, Writing - Review \u0026amp; Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmerican Psychiatric Association. 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Scand J Psychol. 2022;63:1\u0026ndash;7. https://doi.org/10.1111/sjop.12780.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKranzler JH, Maki KE, Benson NF, Eckert TL, Floyd RG, Fefer SA. How Do School Psychologists Interpret Intelligence Tests for the Identification of Specific Learning Disabilities? Contemp Sch Psychol. 2020;24:445\u0026ndash;56. https://doi.org/10.1007/s40688-020-00274-0.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBecker A, Daseking M, Kerner auch Koerner J. Cognitive Profiles in the WISC-V of Children with ADHD and Specific Learning Disorders. Sustainability. 2021;13:9948. https://doi.org/10.3390/su13179948.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim H-Y. Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restor Dent Endod. 2013;38:52. https://doi.org/10.5395/rde.2013.38.1.52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJASP Team. 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Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Psychology Press; 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaccard J, Wan CK. LISREL approaches to interaction effects in multiple regression. SAGE; 1996.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWatkins MW, Dombrowski SC, Canivez GL. Reliability and factorial validity of the Canadian Wechsler Intelligence Scale for Children\u0026ndash;Fifth Edition. Int J Sch Educ Psychol. 2018;6:252\u0026ndash;65. https://doi.org/10.1080/21683603.2017.1342580.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKranzler JH, Floyd RG. Assessing intelligence in children and adolescents: A practical guide. Guildford Press; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanckock GR, Mueller RO. Rethinking construct reliability within latent variable systems. In: Cudeck R, editor. Structural equation modeling: Present and future. Scientific Software International; 2001. p. 195\u0026ndash;216.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen J. Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates; 1988.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson B. Effect sizes, confidence intervals, and confidence intervals for effect sizes. Psychol Sch. 2007;44:423\u0026ndash;32. https://doi.org/10.1002/pits.20234.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eField A. Discovering Statistics Using IBM SPSS Statistics (4th ed.). SAGE Publications; 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFenollar-Cort\u0026eacute;s J, Watkins MW. Construct validity of the Spanish Version of the Wechsler Intelligence Scale for Children Fifth Edition (WISC-V). Int J Sch Educ Psychol. 2019;7:150\u0026ndash;64. https://doi.org/10.1080/21683603.2017.1414006.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBentler PM, Chou C. Practical Issues in Structural Modeling. Sociol Methods Res. 1987;16:78\u0026ndash;117. https://doi.org/10.1177/0049124187016001004.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"WISC-V, ADHD, Multiple group CFA, Intelligence, Construct Validity","lastPublishedDoi":"10.21203/rs.3.rs-8378890/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8378890/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: The current study examined the clinical utility of the Wechsler Intelligence Scale for Children – Fifth Edition (WISC-V) as a valid tool for the evaluation of Attention-Deficit/Hyperactivity Disorder (ADHD) in children and adolescents. The primary objectives were to explore the convergent validity of the WISC-V based on its original factor structure and alternative structural models, including hierarchical and bifactorial models. Additionally, the study aimed to investigate whether significant differences between primary and complementary indices could reveal a cognitive pattern associated with ADHD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A total of 241 participants, aged 6 to 17 years and recently diagnosed with ADHD, were included in the study. Confirmatory factor analyses were conducted to evaluate the fit of different models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Results indicated that four-factor models, both hierarchical and bifactorial, showed superior fit compared to five-factor models. However, the original hierarchical five-factor model proposed by Wechsler, while demonstrating a poorer fit compared to alternative models, was still adequate for use in clinical settings. Moreover, scores on the Working Memory and Processing Speed indices were significantly lower, with medium to large effect sizes, than those on Verbal Comprehension, Visual-Spatial Reasoning, and Fluid Reasoning indices. Additionally, the Cognitive Proficiency Index was significantly lower than the General Ability Index.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: These findings suggest that these discrepancies may help identify ADHD cognitive profiles. However, while these patterns may hold clinical relevance, they should not be overinterpreted as diagnostic markers. The study highlights the need for further research to validate the WISC-V's clinical utility as a supplementary tool in ADHD assessment.\u003c/p\u003e","manuscriptTitle":"The Potential Clinical Relevance of the WISC-V in ADHD Assessment: An Analysis of Structural Models and Within- Subject Cognitive Discrepancies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 00:44:16","doi":"10.21203/rs.3.rs-8378890/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"231608069994336680066859960777835579013","date":"2026-05-17T07:57:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"29738534728090008105378234846655363076","date":"2026-04-27T04:54:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T09:28:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"247719911358183737172405762395160845394","date":"2026-03-02T05:20:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-23T13:28:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-05T14:13:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-05T12:19:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-30T09:34:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2025-12-30T09:23:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"90974c85-a283-4d55-8725-eaa2bce06fcb","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"231608069994336680066859960777835579013","date":"2026-05-17T07:57:35+00:00","index":40,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-29T00:44:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 00:44:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8378890","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8378890","identity":"rs-8378890","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}