Data analysis methodology

Teacher(s)

Bugli Céline;

Language

French

Prerequisites

The prerequisite(s) for this Teaching Unit (Unité d’enseignement – UE) for the programmes/courses that offer this Teaching Unit are specified at the end of this sheet.

Main themes

• Interpret qualitative and quantitative data 
• Presenting data (table, graph, figure, etc.) 
• Descriptive statistics 
• Probabilities (including sample - population), inferences and statistical modelling 
• Difference between causality and correlation 
• Use of SPSS software 
• Notions of reliability, validity, repeatability and reproducibility 
• Thematic qualitative analysis 

Learning outcomes

At the end of this learning unit, the student is able to :

Describe and justify the relevance of data analysis methods (qualitative or quantitative) used in motor sciences (6.1 kiné) / (2.3. Master EP)  Carry out relevant data analysis (6.1, 6.2 Kiné/2.3 EP)  Analyse and interpret results to the point of arguing critically (2.3, 24 Master EP)  Identify bias in a scientific article using appropriate scales (6.3 Kiné - 2.4 EP)  Apply different statistical tests with relevance (6.2, 6.3 Kiné/2.3. EP)

Content

This course aims to develop students' ability to collect, analyze, and interpret qualitative and quantitative data in motor sciences. It covers interpretation, quantitative and qualitative analysis, and data presentation. The basics of descriptive statistics, probability, statistical inference, and modeling will be taught, with an emphasis on critical thinking, particularly by highlighting the distinction between causality and correlation. Students will learn to present their results in the form of tables, graphs, and figures. The use of SPSS software will be integrated for statistical analysis. Particular attention will be paid to the reliability, validity, repeatability, and reproducibility of analytical methods. Finally, an introduction to qualitative thematic analysis will enrich students' understanding of non-numerical data. The course will alternate between explanations of theoretical concepts, demonstrations of how to use SPSS software, and interpretations of statistical results. Practical sessions will provide a concrete and applied approach. Students will be encouraged to develop their autonomy in the relevant choice of statistical analyses and their practical implementation.
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Teaching methods

Lectures and supervised exercises. 

Evaluation methods

☒ The course assessment consists of two components.
☒ Component A – Written examination during the examination period 
The written examination will consist of multiple-choice questions (MCQs) with six answer options and one correct answer, with no penalty for incorrect responses, as well as open-ended questions. 
           ☒ Weighting of Component A : 80 %
☒ Component B – Computer-based examination outside the examination period                
            ☒ Weighting of Component B : 20 %
☒ Final grade calculation :
The final course grade will only be awarded if the student participates in both assessment components. In the event of absence from, or the award of a participation mark for, either component, the final course result will be replaced by the grade “A” (Absent) or “0” (Participation Mark), respectively.
The final grade is calculated as the weighted average of the marks obtained for Components A and B.
☒ Resit examination period: 
The organisation of the resit examination period is identical to that of the first examination period:

• A practical SPSS test in a computer laboratory ;
• A written examination consisting of open-ended questions and multiple-choice questions (MCQs), with no penalty for incorrect answers..

Students are required to retake only the component(s) that were not successfully completed during the first examination period (SPSS test, written examination, or both).

Other information

This course is strictly reserved for FSM students. It is not open to other UCLouvain students.

Online resources

Various documents (videos, lecture slides, etc.) available via Moodle.

Faculty or entity

> FSM