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.
A Cluster Analysis of Affective States Before and During Competition
Guillaume Martinent, Michel Nicolas, Patrick Gaudreau, and Mickaël Campo
Goal Profiles in Sport Motivation: A Cluster Analysis
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.
Cluster Analysis of Movement Patterns in Multiarticular Actions: A Tutorial
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.
Differences in Pedaling Technique in Cycling: A Cluster Analysis
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.
Physical Behavior Profiles Among Older Adults and Their Associations With Physical Capacity and Life-Space Mobility
Lotta Palmberg, Antti Löppönen, Matti Hyvärinen, Erja Portegijs, Taina Rantanen, Timo Rantalainen, and Laura Karavirta
using a combination of several accelerometer-derived metrics could better capture the multidimensional nature of daily activity. Data-driven, person-centered approaches such as mixture modeling and cluster analysis allow for the use of multiple variables of PA in the analyses and can provide a better
Motivational Profiles in Physical Education: Evidence From Secondary School Students in Hong Kong
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
Faster and Slower Posttraining Recovery in Futsal: Multifactorial Classification of Recovery Profiles
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
What Psychosocial Factors Determine the Physical Activity Patterns of University Students?
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
Thriving, Depleted, and At-Risk Canadian Coaches: Profiles of Psychological Functioning Linked to Self-Regulation and Stress
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
Clustering of Multilevel Factors Among Children and Adolescents: Associations With Health-Related Physical Fitness
Shan Cai, Yunfei Liu, Jiajia Dang, Panliang Zhong, Di Shi, Ziyue Chen, Peijin Hu, Jun Ma, Yanhui Dong, Yi Song, and Hein Raat
health-related physical fitness, which included small samples and lacked representativeness, 10 , 11 while no study explored the clustering effect of family- and school-level factors. Therefore, it is necessary to conduct a cluster analysis in the large national sample to allocate individuals into