The Relationship between Pre-Competition State Anxiety Components and Mood State Sub-Scales Scores and the Result of among College Athletes through Temporal Patterning

International Journal of Sports Science(2015)

Cited 23|Views8
No score
Abstract
Pre-competition psychological states have been claimed to be one of the factors in predicting sport performance in a varied range of sports. The aim of current study is to measure pre-competition mood states and state anxiety components to predict the result of competition. The number of 219 participants were selected for this study, range from 18 to 26 years old (M = 1.74, SD = 0.60 yr.; male = 99, female = 120); who represented the UiTM team participated in the study. The participants completed the Profile of Mood States- Adolescent (POMS-A) and the Competitive State Anxiety Inventory-2 (CSAI-2) at three difference temporal patterns including one week, one day and one hour prior to competition. Logistic regression was conducted to assess whether the nine variables, anger, confusion, depression, fatigue, tension, vigor, cognitive anxiety, somatic anxiety and self-confidence, significantly predicted whether the result of the competition is win or lose. The Wald statistics and change in -2 log-likelihood were used to examining the significance of the regression coefficients of the hypothesized predictors. The result of binary logistic regression showed that there were not significant differences in one week and one day hour before competition. However, results revealed that the model was significantly meaningful for three sub-scales of mood state (fatigue, tension and vigor) out of nine measured independent variables only at one day before competition. Remarkably, it means that the model could correctly predict 63.1% of the winners and 26% of the losers. This shows that the regression model for only one day before competition was excelled at predicting the winner and not the losers.
More
Translated text
Key words
Sport Psychology,Motivational Climate,Performance Analysis,Training Load,Coaching Effectiveness
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined