Motivation for physical activity in young people: entity and incremental beliefs about athletic ability

Three studies are reported of children and youth aged 11-19 years (n = 3478) examining the nature of beliefs about athletic ability. Drawing on related research in academic, moral and stereotyping domains, development of a psychometric instrument assessing athletic ability beliefs is detailed. Support was found for a multidimensional hierarchical structure that is invariant across age and gender. Confirmatory factor analysis revealed a structure comprising two higher-order factors of entity and incremental beliefs underpinned by beliefs that athletic ability is stable and a gift (entity), and is open to improvement and can be developed through learning (incremental). Incremental beliefs, indirectly through a task goal orientation, and entity beliefs directly, predicted self-reported amotivation towards physical education and sport. On the other hand, enjoyment of physical activity in youth was predicted directly by task orientation and incremental beliefs. Predictions concerning the moderating role of perceived competence were not supported. Our findings highlight the importance of ability beliefs and goals in understanding the determinants of physical activity in children and youth.     

Correlation analysis of performance measures for multi-label classification

In many important application domains, such as text categorization, scene classification, biomolecular analysis and medical diagnosis, examples are naturally associated with more than one class label, giving rise to multi-label classification problems. This fact has led, in recent years, to a substantial amount of research in multi-label classification. In order to evaluate and compare multi-label classifiers, researchers have adapted evaluation measures from the single-label paradigm, like Precision and Recall; and also have developed many different measures specifically for the multi-label paradigm, like Hamming Loss and Subset Accuracy. However, these evaluation measures have been used arbitrarily in multi-label classification experiments, without an objective analysis of correlation or bias. This can lead to misleading conclusions, as the experimental results may appear to favor a specific behavior depending on the subset of measures chosen. Also, as different papers in the area currently employ distinct subsets of measures, it is difficult to compare results across papers. In this work, we provide a thorough analysis of multi-label evaluation measures, and we give concrete suggestions for researchers to make an informed decision when choosing evaluation measures for multi-label classification.