3 ECTS credits
80 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)
Thomas Selleslagh
Activities and contact hours

2 contact hours Lecture
9 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 computer program used for statistics, is provided.

This introductory statistics course consists of six parts. The first part provides an introduction to statistics and statistical concepts. The second part sheds light on displaying and describing data. The third and fourth part discuss difference and variance tests. The fifth and sixth part focus on correlation and regression.

Course material
Handbook (Recommended) : Discovering statistics using IBM SPSS Statistics, A. Field, 5th edition, London, Sage Publications, 9781526419521, 2017
Digital course material (Required) : (1) Video Tutorials; (2) Online homework assignments; (3) Solutions of exercises; (4) Complementary study materials, Available on Canvas, 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
Additional info

The teaching method of this course consists of a 3-step approach, including 2 pre-class activities, 1 in-class activity and 1 after-class activity.

(1) The pre-class activities consists of two online components that include: (a) gaining necessary theoretical knowledge by means of video tutorials, and
(b) doing homework assignments on Canvas to check the understanding of the video tutorial and to prepare to participate in class activities.

(2) The in-class activity is designed to reinforce important concepts and includes interaction, participation and engagement. Students work through course material individually and/or in peer groups to gain practice applying techniques and knowledge gained prior to class. The focus lays on solving exercises. These face-to-face contact moments involve extra guidance by the instructors of the course and provide a platform to answer questions students may have.

(3) The aim of the after-class activity is to extend students’ learning. Students do homework assignments on Canvas which mainly consist of solving exercises.

Additionally, an introductory session will be held at the beginning of the semester to explain the teaching approach and to provide students with an overview of the several activities and corresponding due dates. At the end of the semester, a closing Q&A session will be held.

Digital course material consists of: (1) Video Tutorials (+ powerpoint slides used in the video tutorials will be available on Canvas); (2) Online homework assignments both theory and exercises on Canvas; (3) Exercises solved in-class (after the class, model solutions for exercises will be available on Canvas); (4) Complementary study materials (such as video clips, book chapters or academic papers) to support your learning will be made available on Canvas.

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: 9 hours

  • Students practice applying key concepts, methods and techniques by solving exercises in peer groups or individually in class (3 x 3 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 75% of the final mark.
Other Exam determines 25% of the final mark.

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

  • Written Exam with a relative weight of 75 which comprises 75% of the final mark.

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

  • Homework Assignment Theory with a relative weight of 10 which comprises 10% of the final mark.
  • Homework Assignment Ex with a relative weight of 15 which comprises 15% of the final mark.

Additional info regarding evaluation

The final grade is composed on the following components:

  • Online homework assignments on Canvas (Theory, open book) 10%
  • Online homework assignments on Canvas (Exercises, open book) 15%
  • Written final exam (both in the 1st and the 2nd exam session, closed book, both theoretical and application-oriented) 75%

The components homework assignments on Canvas cannot be resumed in the second session. No replacement assignment will be organised. Grades on these components are transferred from the first to the second session, but are not transferred to the following academic year. If the homework assignments were not carried out during the semester, the grade on these components will be zero in the first and second exam session. As such the final grade will be limited to a maximum of 75% out of 20. Homework assignments on Canvas have strict due dates. No grades will be assigned in case of late submission.

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