Profile clusters of mood responses
Paper
Paper/Presentation Title | Profile clusters of mood responses |
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Presentation Type | Paper |
Authors | Parsons-Smith, Renee L. (Author), Terry, Peter C. (Author) and Machin, Tony (Author) |
Journal or Proceedings Title | Proceedings of the 28th International Congress of Applied Psychology (ICAP 2014) |
Year | 2014 |
Place of Publication | Paris, France |
Web Address (URL) of Paper | https://b-com.mci-group.com/Abstract/Statistics/AbstractStatisticsViewPage.aspx?AbstractID=183467 |
Conference/Event | 28th International Congress of Applied Psychology: From Crisis to Sustainable Well-Being (ICAP 2014) |
Event Details | 28th International Congress of Applied Psychology: From Crisis to Sustainable Well-Being (ICAP 2014) Event Date 08 to end of 13 Jul 2014 Event Location 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. |
Keywords | BRUMScluster; analysis; mood; profiles; psychometric |
ANZSRC Field of Research 2020 | 520406. Sensory processes, perception and performance |
520107. Sport and exercise psychology | |
520105. Psychological methodology, design and analysis | |
Public Notes | Only abstracts published in conference proceedings, as supplied here. Permanent restricted access to published version in accordance with the copyright policy of the publisher. |
Byline Affiliations | School of Psychology, Counselling and Community |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q2z08/profile-clusters-of-mood-responses
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