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Advanced Data Analysis in Sports Sciences

Code 11238
Year 1
Semester S1
ECTS Credits 15
Workload OT(10H)/PL(30H)/TP(10H)
Scientific area Sports Sciences
Entry requirements None.
Mode of delivery Classroom and distance.
Work placements None.
Learning outcomes The goal of UC is to provide students with practical knowledge of the use of data analysis tools that allow you autonomy in carrying out any research work in the area of Sports Sciences.
. 1 Be able to analyze and understand various quantitative and qualitative methods in the analysis of data on CD;
. 2 Be able to understand the Sport through quantitative and qualitative analysis of observed data;
. 3 Demonstrate the ability to analyze, synthesize and interpret data using quantitative and qualitative methods;
. 4 Demonstrate the ability to use with independence and autonomy IT facilities;
5. Able to conduct a review and critical analysis of the scientific literature by mastering techniques of systematic review and meta-analysis.
Syllabus 1. Data Analysis
1.1. Principles and steps.
2. Construction of variables;
2.1. Creation of a database;
2.2. The notion of variable, variable types and scales of measurement;
2.3. Dependent and independent variables;
2.4. Assumptions of parametric and non-parametric tests (Chi-square, Mann-Whitney, McNemar, Wilcoxon, Kruskal-Wallis; Cochran);
2.5. Analysis of the distribution of the data and its implications;
2.6. Control variables;
2.7. Interpretation of results.
3. Data analysis using different software.
4. Review and critical analysis of the scientific literature;
4.1. Structure and fundamental characteristics of a research report;
4.2 Summary of Approach ethical aspects of research and documentary research;
4.3. Quantitative and qualitative methods: differences, limitations and complementarity:
Main Bibliography • Antonius, R. (2013). Interpreting quantitative data with IBM SPSS Statistics. London: SAGE Publications.
• Balnaves, M.E, & Caputi, P. (2001). Introduction to quantitative research methods. An investigative approach. London: SAGE Publications.
• Bazeley, P. (2007). Qualitative data analysis with NVivo. London: Sage Publications.
• Borenstein, M., Hedges, L.V., Higgins, J.P.T., & Rothstein, H.R. (2009). Introduction to meta-analysis. Chichester: John Wiley & Sons, Ltd.
• Cornillon, P.A. (2012). R for Statistics. Boca Raton, FL: Chapman & Hall/CRC Press.
• Denzin, N. K, & Lincoln, Y. S. (2007). The handbook of qualitative research. London: Sage Publications.
• Falissard, B. (2011). Analysis of questionnaire data with R. Boca Raton, FL: Chapman & Hall/CRC Press.
• Frost, N. (2011). Qualitative research methods in psychology. Combining core approaches. UK: McGraw Hill.

Teaching Methodologies and Assessment Criteria In this UC are presented a set of seminars, of obligatory presence, in which students should participate, seeking to encourage the critical spirit, and the exchange of ideas and experiences. In this way, the classes will have an expository part and another one of debate / discussion of ideas. In order to encourage this debate, the support bibliography will be made available in advance, allowing students to pre-prepare the topics to be presented at each seminar.
Aiming at the development of critical analysis capabilities in the selection and treatment of documentation, argumentation and scientific communication and writing, the evaluation will consist of the accomplishment, presentation and discussion of a group work in which these aspects will be contemplated. Group work has been chosen to enable students to become more involved in the academic community, but also to allow for the gains of working with people with different profiles, training and working methods, which is a integratio
Language Portuguese. Tutorial support is available in English.
Last updated on: 2021-10-19

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