A Comparison of Classification Accuracy for Gender Using Neural Networks Multilayer Perceptron (MLP), Radial Basis Function (RBF) Procedures Compared to Discriminant Function Analysis and Logistic Regression Based on Nine Sports Psychological Constructs to Measure Motivations to Participate in Masters Sports Competing at the 2009 World Masters Games

摘要

Neural networks can be applied to many predictive data mining applications due to their power, flexibility and relatively easy operations. Predictive neural networks are very useful for applications where the underlying process is complex, such as in classification using a mix of nominal and ratio level variables and for predictive validity based on classification modelling. A neural network can approximate a wide range of statistical models without requiring the researcher to hypothesize in advance certain relationships between the dependent and independent variables. The two major applications are multilayer perceptron (MLP) and radial basis function (RBF) procedures. In contrast to MLP networks, in the RBS networks it is only the output units that have a bias term. Discriminant analysis (or discriminant function analysis) based on classification modelling is applied to classify cases into the values of a categorical dependent variable, usually a dichotomy. Logistic regression is useful for situations in which you want to be able to predict the presence or absence of a characteristic or outcome based on values of a set of predictor variables. It is similar to a linear regression model but is suited to models where the dependent variable is dichotomous. The aim of this research was to apply both neural networks, discriminant function analysis (a more traditional statistical approach under the general linear model) and logistic regression and compare their ability as statistical techniques to classify the different genders based nine sports psychological constructs to measure motivations to participate in masters sports. The sample consisted of 3687 male and 3488 female master's athletes who participated in the 2009 World Masters Games and represented a volunteer/convenient sample in the study and a cross-sectional non-experimental research design. The Motivations of Marathoners Scales (MOMS) psychometric instrument assessed participant motivation by nine constructs/factors using factor scores from a 56 item seven Likert type survey instrument measuring motivations to participate. These factors were health orientation, weight concern, personal goal achievement, competition, recognition, affiliation, psychological coping, life meaning and self-esteem.