Profile clusters of mood responses

Parsons-Smith, Renee L. and Terry, Peter C. and Machin, Tony (2014) Profile clusters of mood responses. In: 28th International Congress of Applied Psychology: From Crisis to Sustainable Well-being (ICAP 2014), 8-13 July 2014, Paris, France.

Abstract

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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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Only abstracts published in conference proceedings, as supplied here. Permanent restricted access to published version in accordance with the copyright policy of the publisher.
Faculty / Department / School: Historic - Faculty of Health, Engineering and Sciences - School of Psychology, Counselling and Community
Date Deposited: 15 Feb 2016 23:47
Last Modified: 02 Mar 2018 03:40
Uncontrolled Keywords: BRUMScluster; analysis; mood; profiles; psychometric
Fields of Research : 17 Psychology and Cognitive Sciences > 1701 Psychology > 170112 Sensory Processes, Perception and Performance
17 Psychology and Cognitive Sciences > 1701 Psychology > 170114 Sport and Exercise Psychology
17 Psychology and Cognitive Sciences > 1701 Psychology > 170110 Psychological Methodology, Design and Analysis
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970117 Expanding Knowledge in Psychology and Cognitive Sciences
URI: http://eprints.usq.edu.au/id/eprint/27158

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