BUSINESS RESEARCH METHODS QUANTITATIVE
VU University Amsterdam
School of Business and Economics
BSC INTERNATIONAL BUSINESS ADMINISTRATION
COURSE MANUAL
Academic Year 2024-2025
Period 2.5
CONTENTS
1
2
3
4
5
6
7
8
9
10
11
12
13
Course Description Study Guide ......................................................................................................................3
Course Coordinators and Lecturers..................................................................................................................5
Communication and Contact Opportunities ......................................................................................................6
Promise: What to expect from this course? ......................................................................................................6
The IBA roadmap: where are we? ....................................................................................................................7
Learning Objectives ..........................................................................................................................................7
Study Material ...................................................................................................................................................8
Form of Tuition..................................................................................................................................................9
Assessment ......................................................................................................................................................9
Plagiarism ...................................................................................................................................................12
AI in This Course ........................................................................................................................................13
Study Load..................................................................................................................................................13
Course Schedule ........................................................................................................................................14
1
COURSE DESCRIPTION STUDY GUIDE
Weblink to course
description online
https://studiegids.vu.nl/en/Bachelor/2024-2025/international-businessadministration/E_IBA2_BRMN#/
Course Name
Business research Methods – Quantitative
Course Code
E_IBA2_BRMN
Period
2.5
Credits
6
Language
English
Course Coordinator
Baris Kocaman
Teachers
Baris Kocaman, Meike Morren
Course Objectives
This course is part of the academic core and specifically builds on the courses 1.1
Business Mathematics, 1.3 Academic Skills, and 1.4 Business Statistics. Together
with 2.5 Business Research Methods - Qualitative (period 2.4) and the integrative
research project (period 2.6), this course forms the methodological background in
year 2.
ACADEMIC AND RESEARCH SKILLS – STUDENTS CAN CONDUCT A BASIC
INTERNATIONAL RESEARCH PROJECT FROM START TO FINISH
ACADEMIC SKILLS
After successfully completing this course, the student
• is able to put forward well-founded, substantiated points of view regarding
choices in quantitative research design and analysis (Argumentation)
RESEARCH SKILLS
After successfully completing this course, the student
• is able to critically reflect on (the quality of) academic research
• is able to translate academic research into practically relevant outcomes
• is able to translate practically relevant problems into academically
relevant research questions
• can apply all the quantitative research skills needed to complete a
quantitative, international research process in full, including design,
data collection and analysis methods
BRIDGING THEORY AND PRACTICE
KNOWLEDGE: Demonstrates theoretical and empirical knowledge concerning
the relevant areas in international business administration
After successfully completing this course, the student:
• is able to use R to answer research questions
• can explain basic theory and statistical techniques relevant to
International Business Administration, specifically:
o translating a research question into a formal model that can be tested;
o developing a survey design (formulating survey questions, collecting data);
o how to set up a sampling design;
o analyzing validity and reliability of survey data using factor analysis;
o significance testing with hypothesis tests;
o how to use multiple regression analysis to explain and predict;
o how to use dummy variables;
o how to assess moderation and mediation.
3
QUANT SKILLS: Demonstrates how to translate research questions into
quantitative measures and formal tests
After successfully completing this course, the student is able to:
• detect and screen appropriate data to test a research question quantitatively
• understand and recognize a variety of advanced modeling techniques
• is able to select the correct method and / or technique for
quantifying, analyzing and solving a specific problem.
APPLICATION: Demonstrates applications in statistical software to propose a
solution to an international real-life business problem
• is able to use R to analyze data
• is able to interpret R output to propose a solution to a business problem
Content
Form of tuition
The understanding of research methods to analyse large datasets and how these
methods can be used to compare countries and cultures becomes ever more
important. To use analytics to solve research problems, you need to have a solid
background not only in the available statistical methods, but also in the inherent
boundaries of these statistical methods. The BRM Quantitative course teaches
technical skills while simultaneously deepening the understanding of modelling,
research designs, and the limitations of data analysis. Primary and secondary data
from different countries will be used and discussed.
Lecture
Tutorials in R
Assessment
Computer exam – Individual assessment
Computer interim exam – Individual assessment
Tutorial progress submission – Group assessment
Quizzes – Individual assessment
Literature
Compulsory:
• Malhotra, Naresh, Daniel Nunan, and David Birks (2017). Marketing Research.
An Applied Approach. 5th (6th) European Edition, United Kingdom: Pearson
Education Limited (ISBN: 978-1292103129)
•
Bergkvist, L., & Rossiter, J. R. (2007). The predictive validity of multiple-item
versus single-item measures of the same constructs. Journal of Marketing
Research, 44(2), 175-184.
•
Aguinis, H., Edwards, J. R., & Bradley, K. J. (2017). Improving our
understanding of moderation and mediation in strategic management
research. Organizational Research Methods, 20(4), 665-685.
• Zhao, Xinshu, John G. Lynch Jr, and Qimei Chen (2010). "Reconsidering
Baron and Kenny: Myths and truths about mediation analysis." Journal of
Consumer Research, 37(2), 197-206.
Optional:
• Chapman, C., & Feit, E. M. (2019). R For Marketing Research and Analytics.
Springer International Publishing. https://doi.org/10.1007/978-3-030-14316-9§
Entry Requirements
1.2 Business Mathematics
Recommended
knowledge
1.1 Academic Skills
1.4 Business Statistics
1.6 Business Processes
Remarks
-
4
2
COURSE COORDINATORS AND LECTURERS
Dr. Baris Kocaman
Course coordinator, lecturer (b.kocaman@vu.nl)
I have been an assistant professor in the marketing department at VU since
January 2023. I hold a Ph.D. in marketing at TU/e, and an M.Sc. in operations
management from Columbia Business School. My research interests lie at
the intersection of innovation and customer value creation. Methodologically,
I use advanced regression-based models and causal inference to explore
customer behavior from large datasets. My current focus is on customer
(economic and sustainable) behavior in the B2B subscription economy.
Looking forward to a fun and productive course with you all!
Dr. Meike Morren
Course co-coordinator, lecturer (meike.morren@vu.nl)
I am an Assistant Professor in Marketing at VU since 2012, and obtained my
PhD in Method and Statistics at Tilburg University. My research evolves
around sustainability issues in which I like to use advanced modeling
approaches to discover patterns in environmentally friendly behavior. At the
moment, I apply Natural Language Processing methods to discover hidden
concepts such as ‘gratitude’ or ‘social comfort’ in restaurant review texts. My
aim is to develop an approach that can be used in other settings.
David de Jong
Teaching assistant (d.de.jong2@vu.nl )
David de Jong is a Ph.D. candidate at the Vrije Universiteit Amsterdam.
Before starting his Ph.D., David followed a Research Master and completed
the Marketing master and pre-master at the VU. He is interested in almost
any marketing related challenge, especially with regard to product imitation
strategies or sustainability. Next to his research interest, David is lab
coordinator in the marketing department of the VU. Next to his academic
life, David is active as online marketer for several companies and likes to
help students who are writing their thesis. Feel free to ask him anything via
email.
Teaching assistant (e.mavrina@vu.nl )
Ekaterina (Katya) started PhD position in KIN Centre for Digital Innovation
at Vrije Universiteit Amsterdam in November 2020. In her PhD, she studies
how interdisciplinary teams solve complex innovative tasks while
contributing to the common goal – advancing research in dementia. She is
also interested in technology adoption and diffusion. In her research, she
uses both quantitative and qualitative methodologies.
Ekatarina Mavrina
Josua Mittelstaedt
Teaching assistant (j.j.mittelstaedt@student.vu.nl)
Hey, I'm Josua. I'm currently studying International Management (CEMS) at
RSM. Just like last year, when I was still VU student, I'll host a few of the R
Tutorials and will try to make them as nice as possible :)
See you there!
5
3
Pascal Boertje
Teaching assistant (p.m.boertje@student.vu.nl)
Hey, I’m Pascal! I recently graduated from IBA and have now started my
MSc in Corporate Finance at VU. I really enjoy diving into finance topics,
and I’m looking forward to helping you navigate this course! Outside of
studying, I like to play tennis and go indoor climbing. If you have any
questions feel free to reach out to me.
Sophia Bauer
Teaching assistant (s.p.bauer@student.vu.nl)
My name is Sophia and I am currently a third year IBA student in the
Finance track. After completing my bachelor's I want to continue my
education at the VU with the Finance and Technology master. In my free
time I like to spend as much time as possible around animals.
Sietse Wildöer
Teaching assistant (s.i.wildoeer@student.vu.nl)
My name is Sietse, I’m currently a third-year IBA student, specializing in
accounting. I will be leading three of the workgroups on Wednesday. Please
feel free to reach out if you have any questions! See you in the tutorials!
COMMUNICATION AND CONTACT OPPORTUNITIES
Canvas will be our main channel of communication for the course (planning, content, quizzes/assignments,
lectures, etc.). Keep track of the announcements to be up-to-date!
You can ask questions through various channels, depending on the type of question:
•
Practical questions about the course organization or the tutorials can be asked on the discussion forum on
Canvas. Students can answer each other’s questions on the discussion forum. The lecturers will also keep
track of the conversations and contribute where necessary.
•
Substantive questions about the course content (incl. tutorials) should be discussed with your fellow students
first. You can also post these questions on the discussion forum of the week. Substantive questions can also
be asked during the tutorials and end of the lectures when time allows.
•
For immediate questions about the material discussed during the lectures, please contact the lecturer during
the lecture. If it concerns the tutorial, please contact your tutorial teacher during the tutorial. Taking note of the
slide number and the question helps for efficient communication. The lecturer/tutorial teacher will respond at
the end of the lecture/tutorial.
•
Personal circumstances should be reported to study advisors. Please make sure you report these in a timely
manner.
4
PROMISE: WHAT TO EXPECT FROM THIS COURSE?
The research methods to analyze large datasets and a data-driven understanding of country and cultural
differences become ever more important in today's global economies. To use analytics in solving research
problems, you need to have a solid background not only in the statistical methods available but also in the inherent
boundaries of these methods. The Business Research - Quantitative will provide you with technical skills while
6
deepening your understanding of modeling, research designs, and the limitations of data analysis. You will also
learn how to code using R and how to report business research findings.
4.1
INTERNATIONAL FOCUS
The course pays attention to differences in setting up the research and survey design across countries and cultures.
You will analyse primary and secondary data coming from multiple countries explain the differences and discuss
the implications for business. There are various datasets that you will tackle during the course. In the first three
weeks, you will analyze a survey dataset from the World Values Survey and generate insights. In the next weeks,
the dataset includes customer purchases of an online retail store in the UK among different regions. The focus of
the applications is the analysis and interpretation of these country/region differences.
5
THE IBA ROADMAP: WHERE ARE WE?
This course builds on the courses Business Mathematics (period 1.1) and Business Statistics (period 1.4). Together
with Business Research Methods - Qualitative (period 2.4) and the integrative research project (period 2.6), this
course forms the methodological background in year 2.
CHANGES COMPARED TO LAST YEAR:
-
-
-
-
6
An optional, computer-administered midterm exam on R programming and interpreting your own code
results will be held in week 4. This exam counts for 15% of the final grade, if it is higher than your final
exam score. If you take the midterm, the higher of the midterm or final exam score will count towards 15%
of your final grade. Thus, it is always beneficial to take the midterm exam. If you do not take the midterm,
your final exam score will count for 85% (70% + 15%) of your final grade. The midterm exam is not
compulsory and there is no resit.
There are no longer assignments. The tutorials cover R applications similar to the assignments of the
previous year(s). There is a pass/fail submission of your work on tutorial days which is given on the basis
of a running code, but does not have to answer all of the questions. These assignments collectively
account for 5% of the final grade.
There are no longer weekly help desks.
For tutorial assignments, you can work alone or in teams of 2, but team registration is not required. Each
team member must submit their work individually.
The total weight of the quizzes has increased from 2% to 10%.
The weight of the final exam decreases from 80% to 70% if you take the midterm exam (see details first
bullet point above). The weight of the final exam increases from 80% to 85% if you do not take the midterm
exam.
All lectures are on campus.
Some weeks have knowledge clips and reading assignments as a preparation.
Chapman et al. (2019) is a new optional supplementary material for R programming
LEARNING OBJECTIVES
Academic & Research Skills
Can conduct a basic international research project from start to finish
ACADEMIC SKILLS (THREE A’S)
After successfully completing this course, the student:
• is able to put forward well-founded, substantiated points of view regarding choices in quantitative research
design and analysis
(Argumentation)
RESEARCH SKILLS
After successfully completing this course, the student:
• is able to critically reflect on (the quality of) academic research
7
• is able to translate academic research into practically relevant outcomes
• is able to translate practically relevant problems into academically relevant research questions
• can apply all the quantitative research skills needed to complete a quantitative, international research process
in full, including design, data collection and analysis methods
Bridging Theory and Practice
KNOWLEDGE: Demonstrates theoretical and empirical knowledge concerning the relevant areas in
international business administration
After successfully completing this course, the student can explain:
• can explain basic theory and statistical techniques relevant to International Business Administration,
specifically:
o translating a research question into a formal model that can be tested;
o developing a survey design (formulating survey questions, collecting data);
o how to set up a sampling design;
o analyzing validity and reliability of survey data using factor analysis;
o significance testing with hypothesis tests;
o how to use multiple regression analysis to explain and predict;
o how to use dummy variables;
o how to assess moderation and mediation.
QUANT SKILLS: Demonstrates how to translate research questions into quantitative measures and formal
tests
After successfully completing this course, the student is able to:
• detect and screen appropriate data to test a research question quantitatively
• understand and recognize a variety of advanced modeling techniques
• select the correct method and / or technique for
quantifying, analyzing and solving a specific problem.
APPLICATION: Demonstrates applications in statistical software to propose a solution to an international reallife business problem
After successfully completing this course, the student is able to:
• use R to analyze data
• interpret R output to propose a solution to a business problem.
7
STUDY MATERIAL
•
Malhotra, Naresh, Daniel Nunan, and David Birks (2017). Marketing Research. An Applied Approach. 5th
European Edition, United Kingdom: Pearson Education Limited (ISBN: 978-1292103129) – Exam Material
(indicated chapters and pages only, 6th edition is also approved)
Aguinis, H., Edwards, J. R., & Bradley, K. J. (2017). Improving our understanding of moderation and mediation
in strategic management research. Organizational Research Methods, 20(4), 665-685. – Exam Material
Bergkvist, L., & Rossiter, J. R. (2007). The predictive validity of multiple-item versus single-item measures of
the same constructs. Journal of Marketing Research, 44(2), 175-184. – Exam Material
Zhao, Xinshu, John G. Lynch Jr, and Qimei Chen (2010). Reconsidering Baron and Kenny: Myths and truths
about mediation analysis. Journal of Consumer Research, 37(2), 197-206.
(Optional) Chapman, C., & Feit, E. M. (2019). R For Marketing Research and Analytics. Springer International
Publishing. https://doi.org/10.1007/978-3-030-14316-9§ - Supplementary Material for R programming
•
•
•
•
8
8
FORM OF TUITION
•
•
Lectures: 4 hours per week [not mandatory but highly encouraged]
Tutorials: 2 tutorial hours per week [not mandatory but highly encouraged]
Lectures
The lectures are on campus and cover the fundamentals and various statistical techniques for quantitative
business research. Attendance is not obligatory but highly recommended. To encourage participation, there will be
four unannounced quizzes in some of the lectures (could be anytime during the lecture). To get the most out of the
lectures you are advised to prepare yourself before attending the lectures by studying the lecture slides of that
week, watching the knowledge clips, and reading the required literature, if any.
Tutorials
The weekly tutorials cover the applications of methods discussed during the lectures using R and R Markdown.
The tutorials will start with an introduction by the tutorial teacher, but the main aim is for you to work on the tutorial
sheet. You can work on the tutorials as a team of two or alone. You do not need to register teams but make sure
your team member is in the same working group (WG) as you. Attendance to the tutorials is not mandatory, though
attendance and active participation in tutorials are crucial for midterm and final exams.
9
ASSESSMENT
Your overall course grade is based on five tutorial progress submissions, four quizzes, one midterm exam (optional)
and one computer exam. To pass the course, your overall grade needs to be a 5.50 or higher AND you should get
a minimum of 5.00 from the final exam.
Tutorial Progress Submissions
Each tutorial will include a guided sheet that requires you to apply the fundamentals and statistical procedures
learned in lectures. These tutorials will help prepare you for both the midterm and the R-programming questions in
the final exam.
The tutorial teams consist of at most 2 students (i.e., solo or 2-person) who are in the same tutorial group (i.e.,
labeled as working group - WG). You need to form your own teams, registration of teams is not required.
Each tutorial includes an RMarkdown file with questions and an Excel sheet. Questions in the RMarkdown file are
categorized as coding, interpretation, or theoretical. For coding questions, write and print analysis results in the
RMarkdown file. For interpretation and theoretical questions, select the correct choice using theory and/or your R
analysis and enter answers in the Excel sheet. Questions will clearly be marked as "Report in R" or "Report in
Excel".
At the end of each tutorial (same day at 23:55), students must submit two files (an R script and an Excel sheet) to
show their progress via Canvas individually (i.e., a tutorial progress submission). These submissions are autograded on CodeGrade (an external tool in Canvas), so it is crucial to follow instructions carefully. If your submission
has a running code on CodeGrade, you will receive 1% (pass/fail style) as long as you show effort, regardless of
the correctness or fullness of your answers. Follow the end-of-document instructions in RMarkdown to create the
R script. Late submissions are allowed until the end of the day (23:55) after the tutorial with a 50% penalty. There
are no resits or deadline exceptions for these progress submissions.
•
Did you take assignments in 2024 P5? Then, you can carry your average assignment grade (check the
published list on Canvas) toward tutorial submissions and the midterm exam. These grades will
automatically be carried if you do not make any submissions this year (no need to email).
Honesty declaration: For the tutorial progress submissions, you need to sign an honesty declaration that declares
the tutorial progress and quizzes as your own work. The deadline for this is the same deadline as the first tutorial
progress submission. You do not receive a grade if you haven't signed the declaration! More details on how
you are allowed to use AI in the tutorials can be found under “AI in this course” section of the course manual.
9
Quizzes
The four quizzes will be administered randomly during some lectures. These quizzes can be at any part of the
lecture. Quizzes are intended to encourage your preparation and participation. They include questions related to
the previous week and the current lecture.
• Did you take quizzes in 2024 P5? Then, you can carry your average quiz grade (check the published list
on Canvas) toward the midterm exam and the tutorial submissions. These grades will automatically be
carried if you do not make any submissions this year (no need to email).
Midterm (interim) exam
The optional midterm exam is a computer-administered (TestVision) exam that only includes R programming
questions (which also includes the interpretation of the outputs from the program). It takes place in week 4 of the
course.
Contribution of the midterm exam to the final grade: If you take the midterm, the higher of the midterm or final
exam score will count towards 15% of your final grade. Thus, you are always better off to take the midterm exam.
If you do not take the midterm, your final exam score will count for 85% (70% + 15%) of your final grade. The
midterm exam is not compulsory and there is no resit.
• Did you take assignments in 2024 P5? Then, you can carry your average assignment grade (check the
published list on Canvas) toward the midterm exam and the tutorial submissions. These grades will
automatically be carried if you do not make any submissions this year (no need to email).
Final exam
The final exam is a comprehensive, computer-administered (TestVision) exam with a combination of multiple
choice, open essay, and R programming questions. It takes place in week 8 of the course.
ASSESSMENT OVERVIEW
Format
Minimum required
% grade
Resit
Honesty declaration
Submit once before the
first progress
submission
Handing in your
running code and Excel
answers
Answers to be
submitted during
lecture
Required to receive tutorial
progress and quiz grade
No resit
5%
(1% each - pass/fail style)
No resit
10%
(2.5% each)
No resit
15% (if it is higher than your
final exam score)
No resit
70% (if your midterm exam
score is higher than your final
exam score)
Please consult
https://rooster.vu.nl
Tutorial progress
submissions (5)*
Quizzes (4)*
Optional R Midterm
(interim) exam*
Final exam**
5.00
85% (if you do not take the
midterm exam, or if your
midterm exam score is lower
than your exam score)
Overall course
grade
5.50
* The results of the progress submissions, quizzes, and midterm are valid in the academic year in which it was taken and the
subsequent academic year.
** The written exam is valid only within the study year of examination (i.e. the result from the two attempts that you are eligible to take
for a course).
NOTE: You will receive an NVD as course grade if you have a grade for at least one assessment component (tutorial progress, quiz,
midterm, or final). If you do not have any grade for any of these components then you will receive an NS.
10
ASSESSMENT MATRIX
Format
Written
exam
Written
interim
exam
Tutorial
Assignments
Bridging theory
and practiceKnowledge
Bridging theory
and practiceQuant skills
Research skills
Academic Skills
Bridging theory
and practiceApplication
Ability to
•
to critically
reflect on (the
quality of)
academic
research
•
complete a
quantitative,
international
research
process in full,
including
design,
data collection
and analysis
methods build
on your
knowledge of
statistical tests
that you learned
during Business
Statistics.
Ability to
•
to translate
academic
research into
practically
relevant
outcomes
•
to translate
practically
relevant
problems into
academically
relevant
research
questions
Ability to
•
translate
theoretical ideas
into testable
models.
•
can explain
basic theory and
statistical
techniques
relevant to
International
Business
Administration
•
interpret
findings, and to
argue correctly
what this means
for the
theoretical
model that is
tested.
Ability to
•
formulate
abstract models,
using
mathematical
notation, and
translating the
results into
correct
interpretations
•
to select the
correct method
and / or
technique for
quantifying,
analyzing and
solving a specific
problem
Ability to
•
apply the
knowledge
learned during
the course to the
practical
problem
Ability to
•
understand and
interpret
statistical
models
•
work with large
datasets and to
extract useful
information from
these datasets
Ability to
•
analyze datasets
using the right
technique.
•
apply correct
statistical tests
•
understand and
recognize a
variety of
advanced
modeling
techniques.
Ability to
•
apply abstract
statistical
models to
concrete
datasets.
•
translate
theoretical ideas
into testable
models.
Ability to
•
conduct
business
research using
the proper
methods and
analyzing and
interpreting them
accordingly.
•
interpret
findings, and to
argue what this
means for the
theoretical
model that is
tested.
Ability to
apply the knowledge
learned during the
course to the practical
problem
Ability to
•
understand and
interpret
statistical
models
•
work with large
datasets and to
extract useful
information from
these datasets
Ability to
•
analyze datasets
using the right
technique.
•
build on your
knowledge of
statistical tests
that you learned
during Business
Statistics.
•
understand and
recognize a
variety of
advanced
modeling
techniques.
Ability to
•
apply abstract
statistical
models to
concrete
datasets.
•
translate
theoretical ideas
into testable
models.
•
go from an
abstract
regression
model to a
concrete
recommendation
based on the
findings in your
data.
Ability to
•
conduct
business
research using
the proper
methods and
analyzing and
interpreting them
accordingly.
•
interpret
findings, and to
argue what this
means for the
theoretical
model that is
tested.
Ability to
•
apply the
knowledge
learned during
the course to the
practical
problem
11
10 PLAGIARISM
The Vrije University Amsterdam is very strict about the conduction of plagiarism. It can lead to exclusion of the
Bachelor IBA program without graduating. For these reasons, every assignment is checked for plagiarism with the
help of both software and visual inspection.
Please familiarize yourself with the VU Student Handbook information on academic integrity:
Fraud investigations tend to be very stressful for students, so it’s best to steer clear from any such involvements.
Fortunately, it’s easy to prevent by adhering to some simple rules. The Academic Skills course (IBA year 1) will
address plagiarism and how to avoid it. To refresh your mind on some relevant principles, you can watch this
general video:
https://youtu.be/Uk1pq8sb-eo
10.1 WHAT IS PLAGIARISM?
If you do not include proper references in your work, you could be accused of plagiarism: passing off others’ work,
ideas or arguments as your own. Plagiarism is regarded as fraud and is taken very seriously in the academic world.
If you commit plagiarism during your studies, you could face serious punishment including exclusion from a course
or even expulsion from the university. For academics, plagiarism can mean the end of their career.
10.2 WHAT IS REGARDED AS PLAGIARISM?
The following are clear examples of plagiarism:
•
Handing in somebody else's work as if it is your own.
•
Copying passages, long or short, from a source without acknowledging it.
But the following also count as plagiarism:
•
‘Borrowing’ somebody else's words or ideas without acknowledgement.
•
Making just a few changes to a text, graph or diagram and then claiming it as your own.
•
‘Forgetting’ to put quotation marks around a literal quote.
•
Including an incorrect or incomplete reference, so that the source cannot be traced.
•
Not including a reference every time you draw upon a particular source; this is equivalent to passing off
part of the information used as your own work.
•
Using so many words or ideas from a source that they make up the bulk of your paper – even if you do
credit the source!
For the source and more information on how to correctly reference literature, please see the link:
http://libguides.vu.nl/b-business-admin/incorporating_literature.
10.3 SELF-PLAGIARISM
Students should also refrain from “re-using” their prior submissions to other courses, or parts thereof, and
submitting this as (uncited) original work.
The VU Student Handbook is clear on such self-plagiarism:
Students should treat their (previous) works as if they are from another author. Their usage
should be dealt with accordingly (i.e., proper citations and citing sparingly). If not correctly
referenced, a teacher may find that a student has committed fraud.
(Quote slightly revised for clarity)
12
11 AI IN THIS COURSE
To complete the assignments in this course you are allowed to use a variety of sources and tools, including web
of science, scholar google, company websites, et cetera. Please check the VU academic integrity rules via this
link: Academic Integrity - Be your best self - Vrije Universiteit Amsterdam (vu.nl).
The text below details on one specific set of tools – generative artificial intelligence (AI) – and if or how these
tools can be used in this course. Generic information about using AI tools can be found on the VU website:
Teaching and AI - More about - Vrije Universiteit Amsterdam (vu.nl). The SBE Regulations and Guidelines outline
specific rules on the use of AI: Examination Board - Vrije Universiteit Amsterdam (vu.nl) See Section 'Plagiarism
and fraud' for more details.
In general, all your work should be compliant with academic guidelines, including but not limited to APA reference
format and clickable digital object identifiers (DOI).
Tutorial Progress Submissions
Regarding all tutorial submissions, you are allowed to use generative AI, specifically to enhance content and
writing. AI can be used to:
•
corroborate methodological decisions;
•
understand differences between statistical methods;
•
collect company information;
•
generate programming code;
•
use an assistant as debugger using the OpenAI playground
Please realize that ChatGPT generates answers with a random component, i.e. every time you ask the same
question, you get a different answer. In the AI playground, you can set the temperature parameter to zero to
avoid this. Also you can create AI assistants that are especially equipped with the tasks for this course, or use
existing AI assistants developed by OpenAI. An assistant helps you following an instruction you give them, this is
more or less focusing the AI to solve your problems at hand (like debugging code).
The use of generative AI (e.g., ChatGPT) offers inspiration and advantages to learn and improve your work.
Nonetheless, the use of AI should be consistent with academic integrity, and for example you are not allowed to
‘copy-paste’ AI generated output into a report. Thus, AI generated cannot be used to substitute your work. All use
of AI needs to be reported in a separate appendix, which describes which AI tools were used, what prompts were
put in, and how output has been incorporated in your work.
ⓘ Note: this use of AI should adhere to academic integrity guidelines, and insights and reflections on the use of
AI are part of the assignment reporting instructions and thus part of the assignment assessment.
12 STUDY LOAD
The estimated time students need for basic study activities in this course are:
Preparing for the lectures and quizzes
Attending lectures
Preparing for the tutorials
Attending the tutorials
Preparing for the interim exam
Taking the interim exam
Preparing for the exam
Taking the exam
Total
50 hours
18 hours
10 hours
18 hours
17 hours
2 hours
50 hours
3 hours
168 hours
13
13 COURSE SCHEDULE
Please check the rooms and times on https://rooster.vu.nl.
Week
Format
Lecturer
Theme/Topics
Preparation/Assignments
1
Lecture 1
BK
Introduction to the course
Knowledge clip: Introduction to Quantitative
Business Research and Hallmarks of Quality
Business Research
Research design
Chapter 1 (pp. 9-12), 2 (pp. 39-55), and 3
Lecture 2
BK
Measurement and survey
design
Knowledge clip: Sources of Error,
Questionnaire Design
Chapter 10, 12, 13
Tutorial 1
Tutorial
teachers
Research design
Submit tutorial progress by 23:55
R basics and data
manipulation, RMarkdown,
Knitting and CodeGrade
2
Lecture 3
BK
Multi-item scales,
reliability, validity, factor
analysis
Chapter 20
Bergkvist & Rossiter (2007)
Lecture 4
BK
Sampling
Chapter 14 and 15
Tutorial 2
Tutorial
teachers
Multi-item scales
Submit tutorial progress by 23:55
Validity and reliability
analysis
Factor analysis
3
Lecture 5
BK
Descriptive statistics,
Chapter 21
Hypothesis testing 1
Lecture 6
BK
Hypothesis testing 2
Chapter 21
Correlation analysis
4
Tutorial 3
Tutorial
teachers
Hypothesis testing in R
Submit tutorial progress by 23:55
Lecture 7
BK
Simple and multiple
regression
Chapter 22
14
Dummy variables in
regression
Lecture 8
BK
Q&A
Regression assumptions
Chapter 22
Midterm preparation
(Tutorials 1-3)
No tutorials this week
R tutorials 1 - 3
R coding, interpretation of R output
Midterm exam
5
No classes
6
Lecture 9
Lecture 10
BK
BK
Regression extensions
Chapter 22
Moderation
Aguinis et al. (2017)
Moderation
Chapter 24
Zhao, Lynch & Chen (2010)
7
Tutorial 4
Tutorial
teachers
Regression analysis
Submit tutorial progress by 23:55
Lecture 11
BK
Mediation
Tutorial 5 (Moderation)
Moderation and Mediation
in R
Chapter 24
Zhao, Lynch & Chen (2010)
Lecture 12
BK
Recap and exam
preparation
Q&A
Tutorial 5l
8
Tutorial
teachers
Moderation and mediation
Submit tutorial progress by 23:55
Final exam
15