ساختار مقیاس هوشی وکسلر برای کودکان – نسخه چهارم در یک گروه از کودکان مبتلا به ADHD Structure of the Wechsler Intelligence Scale for Children – Fourth Edition in a Group of Children with ADHD

One aim of the study was to examine the applicability of the WISC/four-factor model, CHC/five-factor model, WISC/higher order model, CHC/ higher order model, and WISC/bifactor model for the 10 WISC–IV core subtests in a group of children with ADHD. As predicted, the findings supported all five models. The correlations among the factors in the WISC/four-factor and CHC/five-factor models were all high (ranging from 0.42 to 0.81), and the factor pattern coefficients of the primary factors on the general factor in the WISC/higher order factor and CHC/higher order factor models were also high (ranging from 0.64 to 0.99). Such high correlations suggest more preference for either the WISC/higher order factor model, CHC/higher order factor model, or WISC/bifactor model. Between these models, the WISC/bifactor model showed better fit. Thus despite the good fit for all models tested, the WISC/bifactor model can be considered more preferable than the WISC/ higher order factor model, or the CHC/higher order factor model. Consistent with our findings, past studies involving children with ADHD have also reported support for the WISC/fourfactor model (Yang et al., 2013; Styck and Watkins, 2014; Thaler et al., 2015), CHC/five-factor model (Thaler et al., 2015), and WISC/higher order model (Styck and Watkins, 2014). Also, previous studies involving the 10 core WISC subtests with the general community, clinic-referred samples, and children with learning disorders, have supported the WISC/four-factor, WISC/higher order factor, and WISC/bifactor models, with better support for the WISC/bifactor model (Watkins et al., 2006, 2013; Watkins, 2010; Devena et al., 2013; Nakano and Watkins, 2013; Canivez, 2014; Styck and Watkins, 2016). For all 15 subtests, support has also been reported for the CHC/fivefactor model and CHC/ higher order factor model (Chen et al., 2009; Keith et al., 2006; Golay et al., 2013). For the WISC/bifactor in the current study, all subtests showed statistically significant and salient factor pattern coefficients on the general factor. Although eight subtests also showed statistically significant and salient factor pattern coefficients on their specific factors, in an absolute sense, except for two subtests, all the factor pattern coefficients were higher on the general than the specific factors. The ECV values for the general factors were much higher (0.70) than that for the specific factors (ranging from 0.00 to 0.12). The ωh value for the general factor in this model was also much higher (0.81) than the ωs values of the specific factors (ranging from 0.02 to 0.43). Thus, the WISC/bifactor model can be considered an optimum model to represent the factor structure of the 10 core WISC-IV subtests. These findings were as expected and are consistent with existing data involving children in general (Devena et al., 2013; Watkins et al., 2013; Canivez, 2014). Although our findings are highly comparable with existing data, they also extend existing data. This is the first study to demonstrate support for the WISC/bifactor model for the 10 WISC–IV core subtests in a group of children with ADHD. Although Styck and Watkins (2014) did not find an admissible solution for this model, our findings supportive of this model are likely to be more accurate. It is possible that the sample size (N = 233) in the Styck and Watkins (2014) study may have been too low for estimating this model (with 30 parameters to be estimated). Our findings are also likely to be more relevant for ADHD than the findings reported by Styck and Watkins (2014). While Styck and Watkins (2014) did not screen for medication used by participants, all participants in the current study were medication-free at the time of testing. Also, as noted by Styck and Watkins (2014), the ADHD diagnosis in their study may not have adhered to the standard diagnostic criteria, such as in DSM- 5 (American Psychiatric Association [APA], 2013), as diagnosis had to also adhere to the Individuals With Disabilities Education Improvement Act (2004). A third new finding in the current study is in relation to how the factors in the bifactor predict reading and arithmetic performance. The findings showed that the general factor and the WM specific factor predicted reading and arithmetic ability. None of the specific factors predicted reading or arithmetic. These relations were as predicted. As these relations have not been examined for children with ADHD, these findings are new. It is to be noted, however, that our findings and interpretations differ from those reported by Glutting et al. (2006) for a normative sample. They reported that the FSIQ accounted for approximately 60% of the variance for both reading and arithmetic scores, and the subscale index scores added less than 1% variance in the predictions. Our findings have implications for the use of WISC-IV with children with ADHD. As the ECV of a general factor can be interpreted as the degree of unidimensionality of general factor to the specific factors (Reise et al., 2013a), these findings indicate support for utilization of FSIQ scores, but not the subscale scores. As ωh is a measure of internal consistency reliability (Brunner et al., 2012), our findings indicate high level of measurement precision for the FSIQ index, and low precision for the subscale scale scores, thereby adding further support for the utilization of the FSIQ score and not subscale scores (Schwean and McCrimmon, 2008; Flanagan et al., 2013). In this respect, although our findings showed that the WM specific factor predicted reading and arithmetic abilities, its low ECV and the ωs values (0.05 and 0.17, respectively) means that predictions from this factor may not be interpretable. Overall, it can be argued that profile analysis that aims to ascertain strengths and weaknesses on the basis of discrepancies in subtest scores (Wechsler, 2003; Flanagan and Kaufman, 2004) may be of little value. Our recommendation for the use of the FSIQ over the subscale scores is consistent with existing recommendations for children in general (Bodin et al., 2009; Watkins, 2010; Devena et al., 2013; Golay et al., 2013; Nakano and Watkins, 2013; Watkins et al., 2013; Canivez, 2014; Styck and Watkins, 2016). Since this recommendation is based indirectly via support for the bifactor model, such practice needs to ensure that there will be no bias in the FSIQ score. According to Reise et al. (2013b) this can be assumed if the ECV and ωh values of the general factor are ≥۰٫۶۰ and ≥۰٫۷۰, respectively. As this was the case for the general factors in both the WISC/bifactor and CHC/higher order factor models, it follows that the FSIQ score will not be biased. Although we have argued in favor of the FSIQ score, the study findings showed a relatively high ωs value for the PS subscale, with both its subtests (Coding and Symbol Search) having higher factor pattern coefficients on the specific factor than the general factor. These findings raise the possibility that the PS could, in part, provide a measure of abilities in PS that is not captured by the FSIQ. There are limitations in this study that need to be considered when interpreting the findings and conclusions in this study. First, all the participants in this study were from the same clinic, and did not constitute a random sample. Thus, it is possible that this may constitute a bias for the sample examined, limiting the findings and conclusions made in this study to ADHD in general. At a practical level, however, it is difficult and virtually impossible to obtain random samples involving clinical samples. Indeed, the previous studies that have examined the factor structure of the WISC-IV in children with ADHD have not used random samples (Yang et al., 2013; Styck and Watkins, 2014; Thaler et al., 2015). Second, as it is possible that as our sample, like previous studies in this area, was highly heterogeneous in terms of psychopathology, the findings may have been confounded. Although there is evidence that sample heterogeneity could potentially influence the results of the factor analysis in general (Delis et al., 2003), Devena et al. (2013) found no difference in factor analysis of the WISC-IV between their full sample and a subsample of this sample that excluded children without disabilities and ADHD. Thus it is possible that sample heterogeneity may not be a confounding variable in relation to WISC-IV scores. Third, in the current study the factor pattern coefficients of the subtests in the WISC/bifactor model with the WM and PS factors were constrained equal, as this model was otherwise empirically under-identified. Thus, the findings may have been confounded. Although we have highlighted a number of limitations, we believe that the findings in the current study add to the literature on the structural model of the WISC-IV and for children with ADHD and children in general, and also the adequacy of using the FSIQ score (over the scale scores) for research and clinical practice. It would be useful if more studies were conducted in this area, taking into consideration the limitations highlighted here.