Objectives of Programme's Learning Outcomes

• Understand the fundamentals (theories and tools) in human and social sciences
• Analyse historical and contemporary phenomena of societies.
• Master the principles and methodologies of scientific approaches applicable in the disciplines within social and human sciences
• Know the principles of different methodological approaches.
• Give a critique and argue a point of view as part of a scientific approach
• Appropriately collect, analyse and interpret empirical data on issues within social and human sciences.
• Understand and implement tools and methods for collecting quantitative data.
• Process empirical data collected with care.

Learning Outcomes of UE

At the end of this course, students will be able to...
-Master the logic of sampling, inference and hypothesis testing,
-Select appropriate hypothesis tests according to the problem at hand,
-Understand the fundamentals of statistical modeling,
-Understand the principle of local and global adjustment to data,
-Select and generate simple graphs to visually exploit the data,
-Understand the fundamentals of multidimensional descriptive statistics,
-Select a lexicometric analysis technique adapted to a corpus,
-Understand the description of a network in the form of a graph and the quantities associated with it,
-Identify statistical errors in scientific publications in SHS.

UE Content: description and pedagogical relevance

The table of contents of the course is as follows:
-Introduction - Reminder of the previous course
-Chapter 1 - Elements of Algorithms - basic functions in R
Concepts: loops, functions, vectors, data frame, factors, graphical windows, database loading, ...
-Chapter 2 - Back to inferential statistics
Concepts: inference, margin of error, confidence level, sample, population, generalization
-Chapter 3 - Bivariate Analysis
Concepts: correlation and causality, student's t test, 1 factor ANOVA, chi^2 test and correlation test
-Chapter 4 - Elements of Multivariate Descriptive Statistics
Concepts: CFA, PCA, MCA, Burt's Table
-Chapter 5 - Elements of Lexicometry
Concepts: forms and active forms, context units, co-occurrences, form frequencies, ALCESTE analysis, similarity analysis
-Chapter 6 - Regression Models
Concepts: linear model, multilinear model, interaction, 2-factor ANOVA model, linearization, polynomial modeling
-Chapter 7 - Network Models
Concepts: graphs, weighted and directed graphs, degrees, density, centrality, core, halo, multipolarity

The "theoretical" courses are an opportunity to discover the concepts, the statistical tools and their uses. The exercises aim at the appropriation of the notions as well as the autonomous application of the tools. The course works by going back and forth between the theoretical notions and their application. Students are expected to work regularly and to attend regularly throughout the four-month period.

Prior Experience

Bases pour l'analyse statistique en sciences humaines

Type(s) and mode(s) of Q2 UE assessment

• Written examination - Face-to-face
• Production (written work, report, essay, collection, product, etc.) - To be submitted online

Q2 UE Assessment Comments

The evaluation is based on the completion of
* a written exam consisting of 4 parts (critical analysis of a scientific article, analysis of the results of a hypothesis test or a data visualization, analysis of a model, bonus)
*an optional written work, allowing to obtain up to 3 additional points (bonus), involving the exploitation of a database.

Type(s) and mode(s) of Q3 UE assessment

• Written examination - Face-to-face
• Production (written work, report, essay, collection, product, etc.) - To be submitted online

Q3 UE Assessment Comments

The evaluation is based on the completion of
* a written exam consisting of 4 parts (critical analysis of a scientific article, analysis of the results of a hypothesis test or a data visualization, analysis of a model, bonus)
*if not done previously, an optional written assignment, allowing to obtain up to 3 additional points (bonus), involving the exploitation of a database.