Investigating the factor structure of the child behavior checklist dysregulation profile in children and adolescents with autism spectrum disorder

Abstract

Dysregulation has been identified as an important risk factor for the development of psychiatric disorders in individuals with autism spectrum disorder (ASD). Therefore, it is necessary to empirically characterize dysregulation and identify psychometrically sound and readily available assessment methods in the ASD population. We sought to evaluate the factor structure of the Child Behavior Checklist‐Dysregulation Profile (CBCL‐DP), an established dysregulation measure in neurotypical children that is derived from the CBCL, in a large, clinically referred sample of children, ages 6–18 years, with ASD (n = 727). Confirmatory factor analysis was used to characterize dysregulation and assess the validity of the CBCL‐DP in children with ASD. Our findings support a bi‐factor model of dysregulation in which dysregulation is a broad and distinct syndrome that is associated with the three subdomains of the CBCL‐DP, anxiety/depression (AD), attention problems (AP), and aggressive behavior (AGG). Dysregulation was associated with most items in the AD and AGG domains and few items in the AP domain. This association with AD and AGG indicates that dysregulation in ASD may be conceptualized as the combined experience of internalized, negative mood states and externalized, reactive behaviors. These findings provide support as well as important caveats for the use of the CBCL‐DP as a measure of dysregulation in the ASD population. Autism Res 2020, 13: 436–443. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary Dysregulation is a risk factor for psychiatric disorders in ASD. This study examined if the CBCL‐DP, an established measure of dysregulation in neurotypical children, can be used to assess dysregulation in children with ASD. Findings provide evidence that in ASD, dysregulation is a broad construct that exists alongside anxiety/depression, attention problems, and aggression. These findings indicate that the CBCL‐DP can be considered a valid measure of dysregulation in the ASD population and could be used in clinical settings.

The Strengths and Difficulties Questionnaire Dysregulation Profile Teacher-Report: Psychometric Properties and Associations with Aspects of School Experience

“…This might be particularly suitable when studying youth dysregulation since behavioral–genetic studies and theoretically based models showed that, when described by a DP, dysregulation is distinct from its specific components [ 33 – 36 ], and so it should be conceptualized as a transdiagnostic syndrome that exists next to specific problems (i.e., Hyperactivity–Inattention, Emotional Symptoms, Conduct Problems) [ 37 ]. A bifactor model would better capture this conceptualization, as also suggested by our results and several studies exploring the DP with different measures (e.g., SDQ, CBCL, Youth Self Report, Teacher Report Form) and samples [ 9 , 36 , 38 , 39 ]. Finally, the examination of the factor loadings revealed that all the items significantly loaded into the general factor DP and their specific factors (see Table 4 ), with most of the factor loadings of the general factor above 0.40, supporting the bifactor nature of the SDQ-DP 15-item teacher-report.…”

The Child Behavior Check List Usefulness in Screening for Severe Psychopathology in Youth: A Narrative Literature Review

“…The CBCL-DP is a profile derived from three different subscales that covers aspects of a dysregulation construct: an affective component (Anxiety and Depressive problems), a behavioral component (Aggressive problems), and a cognitive component (Attention problems; Keefer et al, 2020). Recent evidence supports the association of the CBCL-BP/DP (Biederman et al, 1995, Mick et al, 2003) with a diagnosis of pediatric bipolar disorder.…”

Self-Report Measurement of Well-Being in Autistic Adults: Psychometric Properties of the PERMA Profiler

“…Then, we performed the CFA to test the fit of the five-factor PERMA model specified by Bulter and Kern 27 as well as a bifactor model, which tests whether PERMA can be represented as a single overall factor, rather than with five discrete factors. 32 , 33 We evaluated the fit of each model using the root mean square error of approximation (RMSEA) and standardized root mean residual (SRMR). An RMSEA of 0.08 or below (and a lower 90% confidence interval bound <0.05) and an SRMR of 0.08 or lower indicate acceptable model fit, while values less than 0.06 indicate excellent fit.…”