The total difficulty score of the SDQ (range 0-40) is a fully dimensional measure, with each one-point increase in the total difficulty score corresponding to an increase in the risk of mental health disorder. As such, one can use the total difficulty score as a dimensional measure in research, e.g. comparing mean scores between groups or as a continuous outcome in linear regression analysis (Goodman & Goodman, 2009).
Categories based on single-informant symptoms scores
Alternatively, particularly when using the SDQ for screening purposes, it may be useful to turn symptom scores into categories. An initial three-category solution was proposed by Youth in Mind, the owner of the SDQ (‘normal’/’borderline’/’abnormal’) with the cut-offs chosen using normative data from large population-based UK studies. More recently, an alternative four-category solution has been adopted by Youth in Mind, with cut-offs chosen such that 80% of children score ‘close to average’, 10% are ‘slightly raised’, 5% ‘high’ and 5% ‘very high’.
Categories based on multi-informant symptom + impact scores
The SDQ scoring website also allows one to categorize children according to their risk of ICD-10 or DSM-V disorder based on triangulating information across all available informants (parent, teacher, self-report) and using the impact supplement as well as the symptom score. This is done for the disorder groupings ‘any disorder’, ’emotional disorder’, conduct disorder’ and ‘hyperactivity disorder’. For each diagnostic grouping, there are three possible predictions: low risk, medium risk and high risk. In general, empirical studies show that for children defined as being at ‘low risk’ on the SDQ, around 1-4% turn out to have a disorder when assessed by experts. For children at ‘medium risk’ the proportion is 10-15%, and for children at ‘high risk’ the proportion is 25-60%.
Prevalence estimates for UK research
UK researchers can use validated SDQ algorithms to convert a given SDQ mean score into a predicted prevalence of disorder in a group. This may be useful in translating research findings for policymakers or practitioners, or in assessing likely levels of need for child mental health services (Goodman & Goodman, 2011).