A dispositional trait framework elucidates differences between interview and questionnaire measurement of childhood attention problems
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Updated: May 10, 2026

Measuring Attentional Biases for Threat in Children and Adults
Published on: October 19, 2014
Kathrin Herzhoff1, Jennifer L Tackett, Michelle M Martel
1Department of Psychology, University of Houston.
Abstract:
At present, no single attention-deficit/hyperactivity disorder (ADHD) measure completely and comprehensively captures all ADHD diagnostic criteria (Anastopoulos, 2001). This represents a notable limitation in the assessment of attention problems and suggests the need for research that reconciles differences in information across measures purporting to measure the same or similar constructs. For example, by analyzing differences in measures in relation to a third construct, the third construct can provide an illuminative backdrop against which to view and ultimately reconcile differences between measures of the same attention problem construct. Thus, the purpose of the present study was to draw on a dispositional trait framework to illustrate differences in the ADHD construct assessed by 2 widely used attention problem measures. Parents of 346 children (51% girls) ranging in age from 7 to 12 years (M = 9.92 years, SD = 0.83 years) completed the Child Behavior Checklist (CBCL; Achenbach & Rescorla, 2001), a structured clinical interview based on the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR; American Psychiatric Association, 2000), and dispositional trait questionnaires about their child. Both low Conscientiousness/Effortful Control and high Neuroticism/Negative Affect showed strong, unique associations with the CBCL Attention Problem score, whereas only low Conscientiousness/Effortful Control showed a strong, unique association with DSM-IV-TR ADHD symptoms assessed by clinical interview. These discriminant dispositional trait correlates help us understand the nature of the attention problem construct as assessed by each measure, with important implications for the practice of cross-measure integration in both research and applied settings.
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