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.
 Translated with DeepL.com (free version)

Teaching methods

Lectures and supervised exercises. 

Evaluation methods

The assessment is carried out using a written exam and a computer-based exam.
The written exam will consist of two parts: part A will include multiple-choice questions (with six possible answers and one correct answer), and part B will include open-ended questions.
The computer-based exam (part C) outside of the exam session aims to assess specific skills in using SPSS software.
The final grade is the weighted average of grades A, B, and C.

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