Disagreeing about development: An analysis of parent‐teacher agreement in ADHD symptom trajectories across the elementary school years. Issue 3 (30th May 2018)

• Record Type: Journal Article
• Authors: Murray, Aja Louise; Booth, Tom; Ribeaud, Denis; Eisner, Manuel
• Is Part Of: International journal of methods in psychiatric research. Volume 27:Issue 3(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2018-05-30
• Subjects: ADHD -- cross‐informant agreement -- latent growth curve modelling; Psychiatry -- Research -- Methodology -- Periodicals; Psychiatry -- Periodicals; 616.890072
• DOI: 10.1002/mpr.1723
• Languages: English
• ISSNs: 1049-8931

Abstract

Abstract: Objectives: It is well‐known that in cross‐sectional analyses, agreement between informants is modest as best when rating attention‐deficit/hyperactivity disorder and other disruptive behaviour disorder symptoms. We here aimed to develop recommendations for the use of multi‐informant data in the context of longitudinal developmental analyses that examine symptom trajectories over time. Method: Using parallel process modelling, we estimated parent–teacher agreement in inattention and hyperactivity/impulsivity symptom initial levels and slopes across the elementary school years (ages 7, 9, and 11) for a community sample of n = 1, 388 youth. We also used these models to examine whether initial levels and slopes differed significantly across informants. Results: Informant agreement was low to moderate and higher for inattention slopes ( r = .47) than for hyperactivity/impulsivity slopes ( r = .23). Parents and teachers reported opposite developmental trends for inattention with teachers reporting declines and parents reporting increases over time. Parents reported overall higher levels of hyperactivity/impulsivity, but there were no average informant differences in slopes. Conclusion: Of the options available, we recommend specifying separate but correlated factors for different informants in developmental analyses of attention‐deficit/hyperactivity disorder. This can be achieved within latent growth curve and growth mixture models.