Teacher(s)
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
The course comprises:
1. Introduction to research logic
2. Cross table analysis
3. Index construction
4. Introduction to classification analysis
5. Introduction to principal component factorial analysis.
Learning outcomes
At the end of this learning unit, the student is able to : | |
| 1 | The course and practical work are intended to help students acquire basic data analysis skills in the Social Sciences and constitute an introduction to multivariate analysis. By the end of this course, students should be able to: 1°/ specify the different types of problems-questions for which the methods studied in the course are relevant; 2°/ interpret the statistical analyses for which these methods apply; 3°/ make deliberate use of the principal instruments of descriptive statistics and statistical inferencing introduced during the Statistics and Elements of Probability course and which will be reviewed when the related SPSS commands are taught. |
Content
The course is structured around lectures (12 sessions of 2 hours) that introduce key concepts in quantitative data analysis, illustrated with examples, and practical sessions (10 sessions of 2 hours) dedicated to applying these concepts through computer-based exercises.
In the first part, the course introduces the logic of research by highlighting the constructed nature of all data. This introduction covers the following elements: the primacy of the research question, the hypothesis-concept-indicator sequence, levels of measurement, and basic principles of sampling theory.
In the second part, the course focuses on the construction and description of variables, the calculation of indicators, and the interpretation of contingency tables used to test simple hypotheses.
In the third part, the course addresses multivariate data analysis. It first presents the underlying assumptions of these methods, their limitations, the types of questions they can answer, and how to correctly interpret the results.
The course revisits descriptive statistical methods (central tendency indicators, mean comparisons, independence tests, relative risk and odds ratios), and introduces factorial and classification analysis methods.
The software used in practical sessions is SPSS.
In the first part, the course introduces the logic of research by highlighting the constructed nature of all data. This introduction covers the following elements: the primacy of the research question, the hypothesis-concept-indicator sequence, levels of measurement, and basic principles of sampling theory.
In the second part, the course focuses on the construction and description of variables, the calculation of indicators, and the interpretation of contingency tables used to test simple hypotheses.
In the third part, the course addresses multivariate data analysis. It first presents the underlying assumptions of these methods, their limitations, the types of questions they can answer, and how to correctly interpret the results.
The course revisits descriptive statistical methods (central tendency indicators, mean comparisons, independence tests, relative risk and odds ratios), and introduces factorial and classification analysis methods.
The software used in practical sessions is SPSS.
Teaching methods
The course alternates between a presentation of the statistical methods studied and their application via one or more examples.
The course is given face-to-face, as are the practical sessions.
The course is given face-to-face, as are the practical sessions.
Evaluation methods
Assessment is carried out progressively throughout the semester, through three tests focusing on specific parts of the course material.
In the resit session, assessment takes place on a single date and covers the same material as the three tests.
- The first test, held in November, is worth 4 points of the final grade. It covers prerequisites and the basic elements of the course.
- The second test, held in December, is worth 8 points of the final grade. It focuses on data management and data analysis using statistical software.
- The third test, held during the exam period, is worth 8 points of the final grade. It covers multivariate, factorial, and classification analyses.
In the resit session, assessment takes place on a single date and covers the same material as the three tests.
Other information
Prerequisites:
This course builds on the foundations laid in the "Statistics and Elements of Probability" course.
Assessment:
The course is evaluated through three successive tests. These tests assess students' ability to understand the conditions under which the methods taught can be applied, and to correctly interpret the results of analyses presented to them.
Formative assessment methods:
During both lectures and practical sessions, students are presented with a series of problems that allow them to self-assess their understanding.
Teaching materials provided to students:
Lecture and practical session slides; a reading portfolio including articles presenting or discussing the methods studied; data files (with accompanying codebooks) used for examples and student exercises; and videos covering key points of the course content.
Support and supervision:
Two teaching assistants and student tutors lead the practical sessions.
This course builds on the foundations laid in the "Statistics and Elements of Probability" course.
Assessment:
The course is evaluated through three successive tests. These tests assess students' ability to understand the conditions under which the methods taught can be applied, and to correctly interpret the results of analyses presented to them.
Formative assessment methods:
During both lectures and practical sessions, students are presented with a series of problems that allow them to self-assess their understanding.
Teaching materials provided to students:
Lecture and practical session slides; a reading portfolio including articles presenting or discussing the methods studied; data files (with accompanying codebooks) used for examples and student exercises; and videos covering key points of the course content.
Support and supervision:
Two teaching assistants and student tutors lead the practical sessions.
Online resources
All resources are gradually uploaded to Moodle throughout the semester.
Bibliography
La bibliographie est incluse dans les ressources disponibles sur moodle.
Faculty or entity
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Minor in Sociology and Anthropology
