3 ECTS credits
87 h study time

Offer 1 with catalog number 3018122DNW for working students in the 1st semester at a (D) Master - preliminary level.

Semester
1st semester
Enrollment based on exam contract
Impossible
Grading method
Grading (scale from 0 to 20)
Can retake in second session
Yes
Taught in
English
Faculty
Faculty of Social Sciences & SolvayBusinessSchool
Department
Business
Educational team
Marie-Laure Vandenhaute (course titular)
Linde Kerckhofs
Benjamin Kinnart
Activities and contact hours

2 contact hours Lecture
16 contact hours Seminar, Exercises or Practicals
69 contact hours Independent or External Form of Study
Course Content

This course is designed to introduce students to quantitative research methods, including both descriptive statistics and elementary inferential statistics. The aim is to develop students’ knowledge and skills in the field of quantitative research methods in preparation for the independent processing, interpretation and reporting of collected data (in the context of the later master's thesis and business decisions). Further, the course also aims to develop student's ability to understand, assess and interpret existing quantitative research. This introductory course mainly emphasizes interpretation of statistical results over mathematical computation. An introduction to using SPSS, a statistical software, is provided.

This introductory statistics course consists of seven parts. The first part provides an introduction to statistics and statistical concepts. The second part sheds light on displaying and describing data. The third part discusses the principle of hypothesis testing. It discusses the fundamentals of inferential statistics and the logic behind the statistical reasoning process. The fourth part discusses difference and variance tests. The fifth and sixth part focus on correlation and regression. A final part wraps up the course by discussing how to choose the right statistical test given the data available. 

Course material
Digital course material (Required) : (1) Video Tutorials; (2) Exercises and case studies (incl. solutions); (3) Complementary study materials (such as video clips, book chapters or academic papers) to support students’ learning, available via the electronic learning environment, Canvas
Handbook (Recommended) : Statistics for People Who (Think They) Hate Statistics, N.J. Salkind and B.B. Frey, 7th edition, Thousand Oaks, CA: Sage Publications, 9781544381855, 2019
Handbook (Recommended) : A Step-By-Step Introduction to Statistics for Business, R.N. Lander, 2nd edition, Thousand Oaks, CA: Sage Publications, 9781473948112, 2018
Handbook (Recommended) : Discovering statistics using IBM SPSS Statistics, A. Field, 5the edition, London, Sage Publications, 9781526419521, 2017
Additional info

Teaching Methods
- Lecture: collective contact-dependent moments during which the lecturer engages with learning materials
- Seminar, Exercises or Practicals (Practical): collective or individual contact-dependent moments during which the students are guided to actively engage with learning materials
- Independent or External Form of Study (Self): independent study

This description of the teaching methods is indicative, in order to assess the expected study load.

Lecture: 2 hours

  • Introductory and closing Q&A session (2 x 1 hour)

Practical: 16 hours

  • Students practice applying key concepts, methods and techniques by solving exercises in peer groups or individually (3 x 4 hours)
  • Individual guidance via email, discussion forums and during the study guidance days (4 hours)

Self: 69 hours

  • Gaining necessary theoretical knowledge by means of video tutorials (12 hours)
  • Doing homework assignments on Canvas to checking the understanding of the video tutorial and to prepare to participate in class activities (3 hours)
  • Solving exercises via Canvas to extend the learning (6 hours)
  • Consulting additional study material, keeping up with the course material during the semester (24 hours )
  • Preparation for the exam (3 days of 8 hours (24 hours))

 

Learning Outcomes

General competencies

The objective of this course is to clearly present elementary concepts and techniques of quantitative research methods, including both descriptives and inferential statistics. The focus of the course is mainly the interpretation of statistical analyses. Further the course introduces SPSS.

  • The student describes, explains and applies key elements of descriptive and inferential statistics.
  • The student describes, explains, computes and interprets measures of central tendency .
  • The student describes, explains, computes and interprets measures of variability.
  • The student describes, explains, analyses and interprets elementary quantitative research methods (such as t-test, ANOVA, correlation and regression).
  • The student uses regression analysis to make predictions.
  • The student selects appropriate elementary quantitative research methods for answering a research question.
  • The student performs elementary statistical analyses using SPSS.

Grading

The final grade is composed based on the following categories:
Written Exam determines 85% of the final mark.
Other Exam determines 15% of the final mark.

Within the Written Exam category, the following assignments need to be completed:

  • Written Exam with a relative weight of 85 which comprises 85% of the final mark.

Within the Other Exam category, the following assignments need to be completed:

  • Homework Assignments with a relative weight of 15 which comprises 15% of the final mark.

Additional info regarding evaluation

Throughout the course, students will be asked to complete and submit several homework assignments. 

The component "homework assignments" cannot be resumed in the second session. No replacement assignment will be organised. The grade on this component is transferred from the first to the second session, but is not transferred to the following academic year. If the homework assignments were not carried out during the semester, the grade on this component will be zero in the first and second exam session. As such the final grade will be limited to a maximum of 85% out of 20. Homework assignments have strict due dates. No grades will be assigned in case of late submission.

Allowed unsatisfactory mark
The supplementary Teaching and Examination Regulations of your faculty stipulate whether an allowed unsatisfactory mark for this programme unit is permitted.

Academic context

This offer is part of the following study plans:
Preparatory Programme Master of Science in Management: Academische Master
Preparatory Programme Master of Science in Management: Academische Bachelor