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Duration
Identical coursesResearch Methodology (B-KUL-Y05082)
Aims
This course evaluates the following learning outcomes:
The student
5.a Uses static and dynamic models, graphically and algebraically, to analyse and solve (business) economic problems.
5.b Uses descriptive and inferential statistical methods and techniques to solve (business) economic problems.
5.c Studies and interprets associations between variables using linear regression techniques.
6.c In line with the given practical relevance and the definition of the (business) economics problem, chooses and uses the appropriate techniques to acquire, analyse and interpret data.
6.e From qualitative and quantitative research findings, draws scientific conclusions that bear practical relevance.
More information
Upon completion of this course, the student can:
- select and execute adequate research methods to solve statistical problems. (6.c)
- interpret the output of statistical computations that have been generated (5.a, 5.b, 6.e)
- check the underlying assumputions of methods used (5.a, 5.b)
Previous knowledge
There is no specific preknowledge required for this course.
Is included in these courses of study
Activities
3 ects. Research Methodology (B-KUL-Y55082)
Content
Depending on the results from the initial test, and taking into account time constraints, a selection from the following topics will be discussed:
- probability theory and applications based on simulation and sampling methods
- discrete and continuous distributions (Bernouilli, Binomial, Normal, Student, Fisher, Chi-squared, etc.)
- descriptive statistics for qualitative datasets
- descriptive statistics and exploratory data analysis for quantitative datasets
- classical hypothesis testing (based on the Central Limit Theorem) and applications
- alternative types of hypothesis testing (based on bootstrapping, simulation, etc.) and applications
- regression methods (multiple linear regression, logistic regression, etc.)
- time series methods and applications
- qualitative research methods (social networks, discourse analysis, etc.)
- machine learning and artificial intelligence (tree-based methods, boosting, deep learning, etc.)
In any case, this course mainly focuses on statistical inference based on real datasets from businesses.
Course material
Compulsory Course Material
Slides and additional course materials are available through the learning platform which accompanies the following handbook:
Wessa, P. (2017) "Statistical Analytics for Small and Big Data", Big Analytics Ltd, 2nd edition
Recommended Course Material
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Toledo is not being used for this learning activity
Format: more information
During the first week of the course, students are required to take an initial test which serves to identify strengths and weaknesses of each participant. Based on these test results the instructor selects a series of chapters and exercises from the handbook that are most relevant. The lectures are designed to explain the main, theoretical ideas and guide students through their learning process based on cases and exercises.
Evaluation
Evaluation: Research Methodology (B-KUL-Y75082)
Explanation
Features of the evaluation
Students are required to solve statistical problems on the computer and defend their results orally.
Determination of final grades
The result is calculated and expressed as an integer out of 20.
Second examination opportunity
The features of the evaluation and determination of grades are identical to those of the first examination opportunity, as described above.