Curtailment: a method to reduce the length of self-report questionnaires while maintaining diagnostic accuracy

Minimizing the respondent burden and maximizing the classification accuracy of tests is essential for efficacious screening for common mental health disorders. In previous studies, curtailment of tests has been shown to reduce average test length considerably, without loss of accuracy. In the current study, we simulate Deterministic (DC) and Stochastic (SC) Curtailment for three self-report questionnaires for common mental health disorders, to study the potential gains in efficiency that can be obtained in screening for these disorders. The curtailment algorithms were applied in an existing dataset of item scores of 502 help-seeking participants. Results indicate that DC reduces test length by up to 37%, and SC reduces test length by up to 46%, with only very slight decreases in diagnostic accuracy. Compared to an item response theory based adaptive test with similar test length, SC provided better diagnostic accuracy. Consequently, curtailment may be useful in improving the efficiency of mental health self-report questionnaires.