The purposes of the current study were to identify affective profiles of athletes both before and during the competition and to examine differences between these profiles on coping and attainment of sport goals among a sample of 306 athletes. The results of hierarchical (Ward’s method) and nonhierarchical (k means) cluster analyses revealed four different clusters both before and during the competition. The four clusters were very similar at the two measurement occasions: high positive affect facilitators (n = 88 and 81), facilitators (n = 75 and 25), low affect debilitators (n = 83 and 127), and high negative affect debilitators (n = 60 and 73). Results of MANOVAs revealed that coping and attainment of sport achievement goal significantly differed across the affective profiles. Results are discussed in terms of current research on positive and negative affective states.
Guillaume Martinent, Michel Nicolas, Patrick Gaudreau, and Mickaël Campo
Ken Hodge and Linda Petlichkoff
This investigation compared cluster analysis with the mean-split procedure for examining goal-orientation profiles and examined whether the goal-profile groups revealed differences in athletes’ perceptions of their physical abilities. Rugby players (N = 257, mean age = 20.62 years, SD = 3.64) completed a questionnaire assessing goal orientation, perceived rugby ability and competence, and self-concept of physical ability. Unlike the mean-split procedure, in which scores are forced into high/high, high/low, low/high, or low/low groups, cluster analysis revealed groups that varied in low-, moderate-, and high-task and -ego goals. Moreover, no extreme group profiles (high-ego/high-task or low-ego/low-task) emerged when cluster analysis was used. Multivariate results from the cluster analysis revealed that Cluster 4 (low-ego/moderate-task) reported significantly lower levels of perceived rugby ability/competence than did Cluster 3 (high-ego/moderate-task), indicating that ego might be the determining orientation in adaptive or maladaptive goal profiles. The Cluster 3 goal-profile group (high-ego/moderate-task) scored highest on all 3 dependent measures related to perception of physical abilities.
Robert Rein, Chris Button, Keith Davids, and Jeffery Summers
The present paper proposes a technical analysis method for extracting information about movement patterning in studies of motor control, based on a cluster analysis of movement kinematics. In a tutorial fashion, data from three different experiments are presented to exemplify and validate the technical method. When applied to three different basketball-shooting techniques, the method clearly distinguished between the different patterns. When applied to a cyclical wrist supination-pronation task, the cluster analysis provided the same results as an analysis using the conventional discrete relative phase measure. Finally, when analyzing throwing performance constrained by distance to target, the method grouped movement patterns together according to throwing distance. In conclusion, the proposed technical method provides a valuable tool to improve understanding of coordination and control in different movement models, including multiarticular actions.
Fábio J. Lanferdini, Rodrigo R. Bini, Pedro Figueiredo, Fernando Diefenthaeler, Carlos B. Mota, Anton Arndt, and Marco A. Vaz
To employ cluster analysis to assess if cyclists would opt for different strategies in terms of neuromuscular patterns when pedaling at the power output of their second ventilatory threshold (POVT2) compared with cycling at their maximal power output (POMAX).
Twenty athletes performed an incremental cycling test to determine their power output (POMAX and POVT2; first session), and pedal forces, muscle activation, muscle–tendon unit length, and vastus lateralis architecture (fascicle length, pennation angle, and muscle thickness) were recorded (second session) in POMAX and POVT2. Athletes were assigned to 2 clusters based on the behavior of outcome variables at POVT2 and POMAX using cluster analysis.
Clusters 1 (n = 14) and 2 (n = 6) showed similar power output and oxygen uptake. Cluster 1 presented larger increases in pedal force and knee power than cluster 2, without differences for the index of effectiveness. Cluster 1 presented less variation in knee angle, muscle–tendon unit length, pennation angle, and tendon length than cluster 2. However, clusters 1 and 2 showed similar muscle thickness, fascicle length, and muscle activation. When cycling at POVT2 vs POMAX, cyclists could opt for keeping a constant knee power and pedal-force production, associated with an increase in tendon excursion and a constant fascicle length.
Increases in power output lead to greater variations in knee angle, muscle–tendon unit length, tendon length, and pennation angle of vastus lateralis for a similar knee-extensor activation and smaller pedal-force changes in cyclists from cluster 2 than in cluster 1.
Jing Dong Liu and Pak-Kwong Chung
profiles have been inconclusive in both of the profile number and the profile combination, which may suggest that the motivational profiles might be context-sensitive ( Haerens et al., 2010 ). In previous cluster analysis studies on motivational profiles, internal validity has been extensively examined
Carolina F. Wilke, Felipe Augusto P. Fernandes, Flávio Vinícius C. Martins, Anísio M. Lacerda, Fabio Y. Nakamura, Samuel P. Wanner, and Rob Duffield
hierarchical cluster analysis based on Euclidean distance and average linkage criteria was performed (Python 2.7; Python Software Foundation, https://www.python.org/ ). Briefly, each subject’s data for each measure are plotted in a multidimensional plan, and the Euclidean distance between subjects is
Joseph J. Murphy, Ciaran MacDonncha, Marie H. Murphy, Niamh Murphy, Alan M. Nevill, and Catherine B. Woods
sociodemographic data and for both transport and recreational PA. Pearson chi-square test for independence was performed to note any significant differences in the transport and recreational physical activities between sexes. A 2-step cluster analysis was used as an explanatory tool to identify the PA patterns of
Kylie McNeill, Natalie Durand-Bush, and Pierre-Nicolas Lemyre
two-stage cluster analysis to identify profiles of psychological functioning (i.e., burnout and well-being) within the sample using standardized z -scores for the three subscales of the MBI-ES and the three subscales of the MHC-SF. Following procedures outlined by Hair, Black, Babin, and Anderson
C.K. John Wang and Stuart J.H. Biddle
A great deal has been written about the motivation of young people in physical activity, and the determinants of activity for this age group have been identified as a research priority. Despite this, there are few large-scale studies identifying “types” or “clusters” of young people based on their scores on validated motivation inventories. This study reports the results of a cluster analysis of a large national sample (n = 2,510) of 12- to 15-year-olds using contemporary approaches to physical activity motivation: achievement goal orientations, self-determination theory (including amotivation), the nature of athletic ability beliefs, and perceived competence. Five meaningful clusters were identified reflecting two highly motivated and two less well-motivated clusters, as well as a clearly amotivated cluster. Groupings were validated by investigating differences in physical activity participation and perceptions of physical self-worth. Some clusters reflected age and gender differences. The results provide valuable information for likely strategies to promote physical activity in young people.
Sarah Ullrich-French and Anne Cox
According to self-determination theory, motivation is multidimensional, with motivation regulations lying along a continuum of self-determination (Ryan & Deci, 2007). Accounting for the different types of motivation in physical activity research presents a challenge. This study used cluster analysis to identify motivation regulation profiles and examined their utility by testing profile differences in relative levels of self-determination (i.e., self-determination index), and theoretical antecedents (i.e., competence, autonomy, relatedness) and consequences (i.e., enjoyment, worry, effort, value, physical activity) of physical education motivation. Students (N = 386) in 6th- through 8th-grade physical education classes completed questionnaires of the variables listed above. Five profiles emerged, including average (n = 81), motivated (n = 82), self-determined (n = 91), low motivation (n = 73), and external (n = 59). Group difference analyses showed that students with greater levels of self-determined forms of motivation, regardless of non-self-determined motivation levels, reported the most adaptive physical education experiences.