Quantitative Research on Marathon Times Essay Example | Topics and Well Written Essays - 2500 words

Quantitative Research on Marathon Times - Essay Example Even though marathon running has increased its popularity, there exist no data concerning the relationship between marathon times, age, athlete’s level of fitness (expert runner, beginner), fuel (carbohydrates), weather in the course of the marathon, and the miles ran during training per week. This paper sought to find out whether the performance of marathon is linked to age, athlete’s level of fitness (expert runner, beginner), fuel (carbohydrates), weather in the course of the marathon, and the miles ran during training per week. The study involved 15 marathon runners. The participants were given packets of materials which were inclusive of a questionnaire. After filling the questionnaire, they were brought back for analysis. The study established that there existed some relationship among the variables. Introduction For decades, taking part in marathon running by people of different ages and potentials has been increasing at a repaid rate. A research conducted in this field notes out that the approximate marathon number in U S increased to one hundred and forty in 1975 from forty-four in 1969. In this period, there was an increment in the participants by about 500%. Even though marathon is always popular and requires enough preparation, training practice and knowledge, its effectiveness may tend to remain incomplete. More often than not, many runners come up with their programs of training through taking after marathoners who may look successful. For many years, the philosophy of humans has been adjusted to cover distances that are long in each day so as to obtain food for sustaining the metabolism of the brain (Macarthur & North, 2005). In this regard, the high marathon popularity in humans that are modern of all abilities and ages can be seen as a legacy of the human species’ evolutionary capacity to race for distances that are long and using metabolism that is aerobic (Macarthur & North, 2005). Over the last decade, the starters in marathon have increased from 8,000 to 40,000. This implies that taking part in road racing has been increasing by over 50% in the last decade. This popularity is characterized by the existence of recreational marathon racers who finish the 42,195 km in about two hours. However, marathon has a negative effect to the cardiac status and the existences of cardiac deaths that are sudden during marathon have evoked increased debate (Macarthur & North, 2005). Additionally, endurance and energy c ost are the two benchmarks for obtaining exceptional performance in running. This is so because marathon runners show out an increased fractional use of oxygen. Considering the coming up of recreational marathon amid the middle-aged class, the debate concerning physiological strain also comes up. A couple of surveys done in marathoners have failed to give out information which could be generalized for the runner’s broad spectrum. This study explores the relationship between one dependent variable and several independent variables. In this regard, the dependent variable is marathon times, whereas the independent variables are age, athlete’s level of fitness (expert runner, beginner), fuel (carbohydrates), weather in the course of the marathon, and the miles ran during training per week. The paper seeks to develop a multiple regression model in which the variable that is dependent is explained by all the given independent variable. Method Participants The opportunity sam ple for this study was made up of fifteen participants. The participants were obtained from marathon runners. The participants were selected by using the fact that they had to be individuals whose age was below forty years and that they were recreational runners. The participants were also free of pulmonary and cardiac diseases. The participants of this research were volunteers. Three groups consisting of five participants were made. The three groups were given names according to the age range. The first group, which was the 10 to 20 years group,