Date on Master's Thesis/Doctoral Dissertation


Document Type

Doctoral Dissertation

Degree Name

Ph. D.


Counseling and Human Development

Degree Program

Counseling and Personnel Services, PhD

Committee Chair

Pössel, Patrick

Committee Co-Chair (if applicable)

Valentine, Jeffrey

Committee Member

Mitchell, Amanda

Committee Member

Immekus, Jason

Author's Keywords

depression scales; measurement of depressive symptoms; correlations; sources of variance


Depression remains one of the most prevalent and impactful mental health disorders worldwide. A large number of measures of depressive symptoms are currently available, all designed to assess the same underlying construct. However, the measures tend not to correlate highly with one another. Measurement error cannot fully account for this observation, suggesting that clinical heterogeneity – that is, differences in symptom coverage across measures – might contribute to the lower-than-expected correlation between measures. We conducted two concurrent studies to examine this hypothesis. In Study 1, we developed a coding scheme to categorize items into one of 10 symptom categories. We then categorized measures of depressive symptoms into two groups (“multi-symptom” and “non-multi-symptom” measures), based on similarities in the distribution of item percentages across symptom categories. In Study 2, we conducted a meta-analysis on a set of studies that administered at least two measures of depressive symptoms to the same sample and estimated the average overall correlation between two different depression scales. Our moderator analysis revealed that the average correlation between two multi-symptom measures was statistically significantly stronger than the average correlation between measures of depressive symptoms that were not both multi-symptom measures, and the difference was large enough to be theoretically, and, perhaps, practically meaningful. Our results provide a preliminary indication that clinical heterogeneity between measures of depressive symptoms helps to explain variance in the relations between scales. In light of the exploratory nature of our research, we provide recommendations for future studies to further elucidate sources of variance in the relations between depression measures.