Investigating the theoretical structure of the DAS-II core battery at school age using Bayesian structural equation modeling

Investigating the Structure of the French WISC-V (WISC-VFR) for Five Age Groups Using Psychometric Network Modeling

Since the seminal work of Spearman, exploratory and confirmatory factor analysis represents the standard method of examining the dimensionality of psychological instruments. Recently, within the network psychometrics approach, a new procedure was proposed to estimate the dimensionality of psychological instruments: exploratory graph analysis (EGA). This study investigated the structure of the French Wechsler Intelligence Scale for Children-Fifth Edition (WISC-VFR) with five standardization sample age groups (6-7, 8-9, 10-11, 12-13, and 14-16 years) using EGA. The primary research questions include (a) how many WISC-VFR dimensions are identified in each age subgroup? (b) how are subtest scores associated within the dimensions? Because the number and the content of the dimensions identified by EGA could vary with samples, the secondary research questions include (c) is there evidence of reproducibility and generalizability of the dimensions identified by EGA? We used another procedure called bootstrap exploratory graph analysis (bootEGA). EGA and bootEGA suggested only three dimensions, which are consistent with processing speed, verbal comprehension, and the "old" perceptual reasoning factor. Results did not support the distinction between visual-spatial and fluid reasoning dimensions. EGA and bootEGA represent new tools to assess the construct validity of psychological instruments, such as the WISC-VFR.

Factor Analysis and Variance Partitioning in Intelligence Test Research: Clarifying Misconceptions

This article addresses conceptual and methodological shortcomings regarding conducting and interpreting intelligence test factor analytic research that appeared in the Decker, S. L., Bridges, R. M., Luedke, J. C., & Eason, M. J. (2020). Dimensional evaluation of cognitive measures: Methodological confounds and theoretical concerns. Journal of Psychoeducational Assessment. Advance online publication article.

DAS-II Cognitive Profiles Are Not Diagnostically Meaningful For Autism: A ROC Analysis

Intelligence assessment is an integral part of a comprehensive autism evaluation. Many past studies have described a cognitive profile of autistic individuals characterized by higher nonverbal than verbal IQ scores. The diagnostic utility of this profile, however, remains unknown. We leveraged receiver operating characteristic methods to determine the sensitivity, specificity, and area under the curve (AUC) of three different IQ profiles in a large sample of children who have an autism spectrum disorder diagnosis (N = 1,228, Simons Simplex Collection) who completed the Differential Ability Scales-Second Edition (DAS-II), School Age compared to the normative sample provided by the DAS-II publisher (N = 2,200). The frequently discussed nonverbal > verbal IQ profile performed near chance at distinguishing ASD from normative individuals (AUC: 0.54, 95% CI [0.52-0.56]), and performed significantly worse for females than males (AUC: females: 0.46 [0.41-0.52]; males: 0.55 [0.53-0.58]). All cognitive profiles showed AUC < 0.56. We conclude that while significant differences between verbal and nonverbal IQ scores exist at the group level, these differences are small in an absolute sense and not meaningful at an individual level. We do not recommend using cognitive profiles to aid in autism diagnostic decision-making. LAY SUMMARY: Some researchers and clinicians have reported an "autistic cognitive profile" of higher nonverbal intelligence than verbal intelligence. In an analysis of over 1,000 autistic children, we found that the group's average nonverbal intelligence is usually higher than their verbal intelligence. However, this pattern should not be used by clinicians to make an individual diagnosis of autism because our results show it is not helpful nor accurate.

Actual and perceived motor competence: Are children accurate in their perceptions?

The aims of this study were (1) to investigate whether 6−7-year-old children are accurate in perceiving their actual movement competence, and (2) to examine possible age- and gender-related differences. A total of 603 children (301 girls and 302 boys, aged 6 to 7 years) were assessed on the execution accuracy of six locomotor skills and six object control skills using the Test of Gross Motor Development (TGMD-2). The perceived competence of the same skills, plus six active play activities, was also gauged through the Pictorial Scale of Perceived Movement Skill Competence (PMSC-2). The factorial validity of the TGMD-2 and PMSC-2 scales was preliminarily ascertained using a Bayesian structural equation modeling approach. The relationships between the latent factors of the two instruments were then assessed. Gender and age differences were also examined. The factorial validity of the TGMD-2 and the PMSC-2 was confirmed after some adjustments. A subsequent analysis of the relationship between the latent factors (i.e., locomotor skills and object control) of the two instruments yielded very low estimates. Finally, boys and older children showed better competence in object control skills compared to their counterparts. Weak associations between actual and perceived competence suggest that inaccuracy in children’s perceptions can be likely due to a still limited development of cognitive skills needed for the evaluation of the own competence. From an applied perspective, interventions aimed at improving actual motor competence may also increase children’s self-perceived motor competence and their motivation toward physical activity.

Does the Factor Structure of IQ Differ Between the Differential Ability Scales (DAS-II) Normative Sample and Autistic Children?

The Differential Abilities Scales, 2nd edition (DAS-II) is frequently used to assess intelligence in autism spectrum disorder (ASD). However, it remains unknown whether the DAS-II measurement model (e.g., factor structure, loadings), which was developed on a normative sample, holds for the autistic population or requires alternative score interpretations. We obtained DAS-II data from 1,316 autistic individuals in the Simons Simplex Consortium and 2,400 individuals in the normative data set. We combined ASD and normative data sets for multigroup confirmatory factor analyses to assess different levels of measurement invariance, or how well the same measurement model fit both data sets: "weak" or metric, "strong" or scalar, and partial scalar if full scalar was not achieved. A weak invariance model showed excellent fit (Confirmatory Fit Index [CFI] > 0.995, Tucker Lewis Index [TLI] > 0.995, root mean square error of approximation [RMSEA] < 0.025), but a strong invariance model demonstrated a significant deterioration in fit during permutation testing (all p's<0.001), suggesting measurement bias, meaning systematic error when assessing autistic children. Fit improved significantly, and partial scalar invariance was achieved when either of the two spatial subtest (Recall of Designs or Pattern Construction) intercepts was permitted to vary between the ASD and normative groups, pinpointing these subtests as the source of bias. The DAS-II appears to measure verbal and nonverbal-but not spatial-intelligence in autistic children similarly as in normative sample children. These results may be driven by Pattern Construction, which shows higher scores than other subtests in the ASD sample. Clinicians assessing autistic children with the DAS-II should interpret verbal and nonverbal reasoning composite scores over the spatial score or General Composite Ability. Autism Res 2020, 13: 1184-1194. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: The Differential Abilities Scales, 2nd edition (DAS-II) is a popular intelligence quotient (IQ) test for assessing children with autism. This article shows that the DAS-II spatial standardized scores should be interpreted with caution because they hold a different meaning for autistic children. Verbal and nonverbal reasoning scores appear valid and to hold the same meaning for those with and without autism spectrum disorder.

Explicating the South African Psychological Ownership Questionnaire’s confirmatory factor analysis model fit: A Bayesian structural equation modelling approach

Orientation: The rigid application of conventional confirmatory factor analysis (CFA) techniques, the overreliance on global model fit indices and the dismissal of the chi-square statistic appear to have an adverse impact on the research of psychological ownership measures.Research purpose: The purpose of this study was to explicate the South African Psychological Ownership Questionnaire’s (SAPOS’s) CFA model fit using the Bayesian structural equation modelling (BSEM) technique.Motivation for the study: The need to conduct this study derived from a renewed awareness of the incorrect use of the chi-square statistic and global fit indices of CFA in social sciences research.Research approach/design and method: The SAPOS measurement model fit was explicated on two study samples consisting, respectively, of 712 and 254 respondents who worked in various organisations in South Africa. A Bayesian approach to CFA was used to evaluate if local model misspecifications were substantive and justified the rejection of the SAPOS model.Main findings: The findings suggested that a rejection of the SAPOS measurement model based on the results of the chi-square statistic and global fit indices would be unrealistic and unfounded in terms of substantive test theory.Practical/managerial implications: BSEM appeared to be a valuable diagnostic tool to pinpoint and evaluate local CFA model misspecifications and their effect on a measurement model.Contribution/value-add: This study showed the importance of considering local misspecifications rather than only relying the chi-square statistic and global fit indices when evaluating model fit.

Evaluating the higher-order structure of the Profile of Emotional Competence (PEC): Confirmatory factor analysis and Bayesian structural equation modeling

Emotional competence (EC) reflects individual differences in the identification, comprehension, expression, regulation, and utilization of one’s own and others’ emotions. EC can be operationalized using the Profile of Emotional Competence (PEC). This scale measures each of the five core emotional competences (identification, comprehension, expression, regulation, and utilization), separately for one’s own and others’ emotions. However, the higher-order structure of the PEC has not yet been systematically examined. This study aimed to fill this gap using four different samples (French-speaking Belgian, Dutch-speaking Belgian, Spanish, and Japanese). Confirmatory factor analyses and Bayesian structural equation modeling revealed that a structure with two second-order factors (intrapersonal and interpersonal EC) and with residual correlations among the types of competence (identification, comprehension, expression, regulation, and utilization) fitted the data better than alternative models. The findings emphasize the importance of distinguishing between intrapersonal and interpersonal domains in EC, offer a better framework for differentiating among individuals with different EC profiles, and provide exciting perspectives for future research.

Construct Validity of the WISC-V in Clinical Cases: Exploratory and Confirmatory Factor Analyses of the 10 Primary Subtests

Independent exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) research with the Wechsler Intelligence Scale for Children-Fifth Edition (WISC-V) standardization sample has failed to provide support for the five group factors proposed by the publisher, but there have been no independent examinations of the WISC-V structure among clinical samples. The present study examined the latent structure of the 10 WISC-V primary subtests with a large (N = 2,512), bifurcated clinical sample (EFA, n = 1,256; CFA, n = 1,256). EFA did not support five factors as there were no salient subtest factor pattern coefficients on the fifth extracted factor. EFA indicated a four-factor model resembling the WISC-IV with a dominant general factor. A bifactor model with four group factors was supported by CFA as suggested by EFA. Variance estimates from both EFA and CFA found that the general intelligence factor dominated subtest variance and omega-hierarchical coefficients supported interpretation of the general intelligence factor. In both EFA and CFA, group factors explained small portions of common variance and produced low omega-hierarchical subscale coefficients, indicating that the group factors were of poor interpretive value.

Investigating the Theoretical Structure of the Differential Ability Scales—Second Edition Through Hierarchical Exploratory Factor Analysis

When the Differential Ability Scales–Second Edition (DAS-II) was developed, the instrument’s content, structure, and theoretical orientation were amended. Despite these changes, the Technical Handbook did not report results from exploratory factor analytic investigations, and confirmatory factor analyses were implemented using selected subtests across the normative age groups from the total battery. To address these omissions, the present study investigated the theoretical structure of the DAS-II using principal axis factoring followed by the Schmid–Leiman procedure with participants from the 5- to 8-year-old age range to determine the degree to which the DAS-II theoretical structure proposed in the Technical Handbook could be replicated. Unlike other age ranges investigated where at most 14 subtests were administered, the entire DAS-II battery was normed on participants aged 5 to 8 years, making it well suited to test the full instrument’s alignment with theory. Results suggested a six-factor solution that was essentially consistent with the Cattell–Horn–Carroll (CHC)-based theoretical structure suggested by the test publisher and simple structure was attained. The only exception involved two subtests (Picture Similarities and Early Number Concepts) that did not saliently load on a group factor. Implications for clinical practice are discussed.