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Depression Research and Treatment
Volume 2018 (2018), Article ID 9250972, 13 pages
Research Article

Assigning Clinical Significance and Symptom Severity Using the Zung Scales: Levels of Misclassification Arising from Confusion between Index and Raw Scores

School of Behavioural, Cognitive and Social Sciences, University of New England, Armidale, NSW 2351, Australia

Correspondence should be addressed to Debra A. Dunstan

Received 20 June 2017; Accepted 19 December 2017; Published 21 January 2018

Academic Editor: Bernard Sabbe

Copyright © 2018 Debra A. Dunstan and Ned Scott. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Background. The Zung Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) are two norm-referenced scales commonly used to identify the presence of depression and anxiety in clinical research. Unfortunately, several researchers have mistakenly applied index score criteria to raw scores when assigning clinical significance and symptom severity ratings. This study examined the extent of this problem. Method. 102 papers published over the six-year period from 2010 to 2015 were used to establish two convenience samples of 60 usages of each Zung scale. Results. In those papers where cut-off scores were used (i.e., 45/60 for SDS and 40/60 for SAS), up to 51% of SDS and 45% of SAS papers involved the incorrect application of index score criteria to raw scores. Inconsistencies were also noted in the severity ranges and cut-off scores used. Conclusions. A large percentage of publications involving the Zung SDS and SAS scales are using incorrect criteria for the classification of clinically significant symptoms of depression and anxiety. The most common error—applying index score criteria to raw scores—produces a substantial elevation of the cut-off points for significance. Given the continuing usage of these scales, it is important that these inconsistencies be highlighted and resolved.