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

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. The accuracy of the solutions were assessed with neural networks, by classification accuracy using both test and holdout samples, predicted-byobserved chart, ROC curve, cumulative gains and lift charts, independent variable importance and normalised importance; and discriminant function analysis by both original and cross-validation samples, lambda values, p-values, tolerance, F to remove and in stepwise discriminant analysis by the hierarchy of inclusion steps. Similar methods were applied when assessing classification accuracy using logistic regression. The results in terms of MLP analysis was overall correct percent of 64.4% and the order of importance was competition, selfesteem, affiliation, recognition, weight concern, health orientation, goal achievement, psychological coping and life meaning. In terms of RBF analysis training sample overall correct percent was 60.5% and order of importance was competition, affiliation, recognition, psychological coping, weight concern, life, meaning, self-esteem, goal achievemnt and health orientation. For the discriminant analysis the overall correct classification rate was 63.3% and for logistic regression 63.0% and the stepwise entry order into both analyses was affiliation, competition, self-esteem, recognition, weight concern and health orientation. The classification accuracies based on MLP, discriminant analysis and logistic regression were very similar in outcome for both the classification of gender and combined classification accuracy. None of the classification techniques based on neural network analyses and multivariate method of discriminant analysis and logistic regression were overtly superior to each other. Although it is important to note the RBF neural network displayed classification accuracy slightly lower than the other three methods.