Research Methods

Teacher(s)

Catanzaro Daniele (compensates Chevalier Philippe); Chevalier Philippe; Taskin Laurent; Verardi Vincenzo;

Language

English

Prerequisites

None

Main themes

The objective for this course is to provide students in business management with tools and skills necessary in Qualitative and Quantitative Research Methods, and to strengthen their logic reasoning skills, in order to help them develop rigorous arguments. A good understanding of principles and techniques of research in management will enable students to apply these techniques, as well as acquire on their own additional techniques rooted in their field of research.

Learning outcomes

At the end of this learning unit, the student is able to :
1	On successful completion of this program, each student will acquire the following skills : A scientific and systematic approach Knowledge and reasoning Project management Personal and professional development At the end of this course, the student will be able to : confidently conceive, formulate and motivate his/her personal research project, from the research questions to the choice of data analysis approaches; show their understanding of major qualitative and quantitative research methods and their ability to make use, and interpret the results of the used research techniques; develop a qualitative and a quantitative research design; identify and conduct the appropriate techniques for different kinds of research questions; critically analyze a scientific research contribution in management.

Content

            Qualitative Research Methods:

• General characteristics of qualitative approaches
• Research design and data collection
• Interview Guide and questioning
• Analyzing and making sense of data
• Data Quality Control
• Reflexivity and heterodox approaches
• Ethnographic and Visual Approaches

Quantitative Research Methods

• Revision of linear regression models (least squares, inference, bootstrap)
• Maximum Likelihood estimation (location, scale and regression)
• Qualitative dependent variable models (binary, polychoromous ordered and unordered, count)
• Limited dependent variable regression (censoring and selection)
• Panel data (pooled, fixed-effects, random effects, conditional fixed-effects logit, diff-in-diff)
• An introduction to structural equation modelling SEM (latent variable, multilevel modelling)

Advanced Discrete Optimization

• Polytopes
• Efficiency
• TDI Systems
• Matroid Theory
• Equivalence between separation and optimization
• Branch-&-cut 
• Case Studies

Teaching methods

Methodological and theoretical lectures of teachers, accompanied by empirical studies illustrations, alternate with discussions and applications with participants. Teaching is based on reading of scientific articles and book chapters deemed essential to master qualitative and quantitative research methodologies in Management. Students are expected to summarize and present some of these and to discuss it in groups. The content of this course (for example Quantitative Research Methods) will be adapted to the level of advancement of students in order to follow them in their research projects.

Evaluation methods

Continuous evaluation : continuous assessment not remediable, in group/individual, written preparations, reading scientific articles, oral presentations and exam, etc.

Other information

Dans le cadre des travaux écrits demandés dans le cadre de ce cours, l’utilisation d’outils d’intelligence artificielle (IA) est balisée conformément aux lignes directrices de la note IA smart teaching de l'UCLouvain.
Voici les principes à respecter :
• Transparence : Si vous utilisez un outil d’IA pour vous assister dans la rédaction, la recherche ou l'organisation de vos idées, vous devez le mentionner explicitement dans votre travail. Cela inclut la correction linguistique, la traduction, la création d'un plan ou la synthèse d'un texte.
• Authenticité : Les travaux soumis doivent refléter votre compréhension et vos compétences personnelles. L’utilisation de l’IA ne doit pas masquer ou remplacer votre démarche intellectuelle et critique.
• Responsabilité : Vous êtes entièrement responsable du contenu soumis, même si des outils d’IA ont été utilisés. Tout usage non référencé ou abusif peut être considéré comme une irrégularité et sera sanctionné conformément au règlement des études et des examens (notamment au regard du chapitre 4, section 7 du RGEE).
• Conservation : Les dialogues et interactions avec les outils d'IA utilisés pour produire des contenus doivent être conservés et disponibles pour vérification jusqu'à la proclamation des résultats.
En outre, dans une optique de sobriété énergétique et écologique, les interactions avec une IA générative doivent être limitées au strict nécessaire de la tâche.

Online resources

Moodle

Bibliography

See on Moodle

Faculty or entity

> CLSM