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Background  Mood instability (MI) and irritability are related to depression but are not considered core symptoms. Instruments typically code clusters of symptoms that are used to define syndromic depression, but the place of MI and irritability has been under-investigated. Whether they are core symptoms can be examined using Rasch analysis.    Method  We used the UK Psychiatric Morbidity Survey 2000 data ( n  = 8,338) to determine whether the nine ICD/DSM symptoms, plus MI and irritability, constitute a valid depression scale. Rasch analysis was used, a method concerned with ensuring that items constitute a robust scale and tests whether the count of symptoms reflects an underlying interval-level measure. Two random samples of 500 were drawn, serving as calibration and validation samples. As part of the analysis, we examined whether the candidate symptoms were unidimensional, followed a Guttman pattern, were locally independent, invariant with respect to age and sex, and reliably distinguished different levels of depression severity.    Results  A subset of five symptoms ( sad, no interest, sleep, cognition, suicidal ideas ) together with  mood instability  and  irritability  satisfactorily fits the Rasch model. However, these seven symptoms do not separate clinically depressed persons from the rest of the population with adequate reliability (Cronbach α = 0.58; Person Separation Index = 0.35), but could serve as a basis for scale development. Likewise, the original nine DSM depression symptoms failed to achieve satisfactory reliability (Cronbach α = 0.67; Person Separation Index = 0.51).    Limitations  The time frame over which symptoms were experienced varied, and some required recall over the last year. Symptoms other than those examined here might also be core depression symptoms.    Conclusion  Mood instability and irritability are candidate core symptoms of the depressive syndrome and should be part of its clinical assessment.

Mood Instability and Irritability as Core Symptoms of Major Depression: An Exploration Using Rasch Analysis