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Two Methods for Calculating Symptom Cluster Scores


Salomon, Rebecca E.; Crandell, Jamie L.; Muscatell, Keely A.; Santos, Hudson P., Jr.; Anderson, Ruth A.; & Beeber, Linda S. (2020). Two Methods for Calculating Symptom Cluster Scores. Nursing Research, 69(2), 133-141. PMCID: PMC7050366


Background: Symptom clusters are conventionally distilled into a single score using composite scoring, which is based on the mathematical assumption that all symptoms are equivalently related to outcomes of interest; this may lead to a loss of important variation in the data.
Objectives: This article compares two ways of calculating a single score for a symptom cluster: a conventional, hypothesis-driven composite score versus a data-driven, reduced rank regression score that weights the symptoms based on their individual relationships with key outcomes.
Methods: We conducted a secondary analysis of psychoneurological symptoms from a sample of 356 low-income mothers. Four of the psychoneurological symptoms (fatigue, cognitive dysfunction, sleep disturbance, and depressed mood) were measured with the Center for Epidemiological Studies Depression Scale; the fifth (pain) was measured using an item from the Medical Outcomes Study 12-item Short Form Health Survey (SF-12). Mothers’ function was measured using the 12-item Short Form Health Survey. The composite score was calculated by summing standardized scores for each individual psychoneurological symptom. In contrast, reduced rank regression weighted the individual symptoms using their respective associations with mothers’ function; the weighted individual symptom scores were summed into the reduced rank regression symptom score.
Results: The composite score and reduced rank regression score were highly correlated at .93. The cluster of psychoneurological symptoms accounted for 53.7% of the variation in the mothers’ function. Depressed mood and pain accounted for almost all the explained variation in mothers’ function at 37.2% and 15.0%, respectively.
Discussion: The composite score approach was simpler to calculate, and the high correlation with the reduced rank regression score indicates that the composite score reflected most of the variation explained by the reduced rank regression approach in this data set. However, the reduced rank regression analysis provided additional information by identifying pain and depressed mood as having the strongest association with a mother’s function, which has implications for understanding which symptoms to target in future interventions. Future studies should also explore composite versus reduced rank regression approaches given that reduced rank regression may yield different insights in other data sets.


Reference Type

Journal Article

Article Type


Year Published


Journal Title

Nursing Research


Salomon, Rebecca E.
Crandell, Jamie L.
Muscatell, Keely A.
Santos, Hudson P., Jr.
Anderson, Ruth A.
Beeber, Linda S.



Data Set/Study

Household, Income and Labour Dynamics in Australia (HILDA) Survey
Achievement for Latinos through Academic Success (ALAS)


United States of America