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Dr Orla McBride, University of Ulster presents, ‘Quantifying skip-out information loss when assessing major depression symptoms’

Chair: Kamaldeep Bhui


Mental health surveys widely employ ‘skip-out’ procedures whereby survey respondents are asked one or more screening questions to determine whether they are administered a full diagnostic module to assess for the presence of a common mental disorder, such as major depressive disorder (MDD). Although the procedure adheres faithfully to the conceptualisation of mental disorders in traditional psychiatric classification systems, it is highly problematic due to the negative implications it has on the resulting survey data, which may limit the use of such data for conducting high-quality research of importance to researchers, clinicians, and policymakers. In this paper, we conduct a series of exploratory analyses using the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders (VATSPSUD), a unique survey which suspended use of the ‘skip-out’ procedure, to demonstrate a range of problems associated with commonly used approaches to circumvent the issue of missing data imposed by the ‘skip-out’ procedure (i.e., recoding missing data with zeros, or complete case analysis), using MDD as an applied example. We proffer recommendations for survey methodologists to consider ‘not-missing-by-design’ approaches, which may provide researchers and data analysts with viable alternatives to dealing with sizeable proportions of missing data produced via the ‘skip-out’ procedure in existing or future surveys. 

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