Profile clusters of mood responses

Profile clusters of mood responses

Research into mood and performance relationships has had a strong focus on psychometric testing, commonly referred to as mood profiling. Although mood profiling has been used extensively in applied psychology since the 1970s, there are no published investigations of whether distinct mood profile clusters can be identified among the general population. In the present investigation, an online mood profiling system (www.moodprofiling.com) was developed, based on the Brunel Mood Scale and the conceptual framework of Lane and Terry (2000). The mood responses of 2,364 participants were analysed using agglomerative, hierarchical cluster analysis, which identified six distinct and theoretically meaningful profiles. K-means clustering with a prescribed six-cluster solution was used to further refine the final parameter solution. The mood profiles identified in the cluster analysis were termed the iceberg (n = 695, 29.4%), inverse iceberg (n = 244, 10.3%), inverse Everest (n = 64, 2.7%), shark fin (n = 409, 17.3%), surface (n = 349, 14.8%), and submerged profiles (n = 603, 25.5%). A multivariate analysis of variance showed significant differences between clusters on each dimension of mood, being tension [F(5, 2358) = 615.96, p < .001], depression [F(5, 2358) = 874.00, p < .001], anger [F(5, 2358) = 715.04, p < .001], vigour [F(5, 2358) = 613.03, p < .001], fatigue [F(5, 2358) = 873.92, p < .001], and confusion [F(5, 2358) = 861.90, p < .001]. A chi-square test of goodness-of-fit indicated that gender [χ²(5, N = 2,364) = 25.48, p < .001], age [χ²(25, N = 2,364) = 78.30, p < .001], and education level [χ²(15, N = 2,364) = 41.86, p < .001], were unequally distributed across clusters. Further, a discriminant analysis showed that cluster membership could be correctly classified with a high degree of accuracy: iceberg (100%), inverse iceberg (92.2%), inverse Everest (98.4%), shark fin (94.4%), surface (82.8%), and submerged (98.3%). Identification of discrete mood profile clusters will assist in the interpretation of individual mood profiles by applied practitioners.

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