Credits: 6

Schedule: 29.10.2018 - 26.11.2018

Teacher in charge (valid 01.08.2018-31.07.2020): 

Amber Geurts
Kristiina Herold

Contact information for the course (applies in this implementation): 

Instructor’s contact

Course information

Post-doctoral researcher Amber Geurts

Post-doctoral researcher Kristiina Herold;  

Department of Management Studies

Office Hours: Monday and Wednesday after class

M.Sc. degree, common studies. Participation is limited to 24 students.

Academic Year: 2018-2019, Period II

Location: C-332

Language of Instruction: English

Course Website:

Teaching Period (valid 01.08.2018-31.07.2020): 

Period II (2018-2019), Töölö campus
Period II (2019-2020), Otaniemi campus

Learning Outcomes (valid 01.08.2018-31.07.2020): 

This is a basic level course on quantitative research methods in management studies with an emphasis on developing hands-on knowledge in executing and critically assessing survey-based studies. Different to standard courses in statistics, this course is more focused on practical implementation of a quantitative research project in the field of management studies.

The objective of the course is to advance understanding of various principles and practices of quantitative research, and to improve the students’ readiness to execute a research project in their own discipline.

The main learning outcomes are to gain an understanding of the uses and limitations of common tools for analyzing quantitative data and to develop competence in collecting, modeling, and interpreting quantitative data. Students will improve their capacity to interpret and critically assess methodological execution of empirical management studies. On completion of this course, students are prepared to utilize obtained methodological skills in their master’s theses, doctoral dissertations, and/or other research projects.

Content (valid 01.08.2018-31.07.2020): 

The course covers the fundamentals of the research process, measures development, statistical analysis and modeling of data, and quantitative approaches to research, all with a focus on issues specific to management studies and emphasis on practical experience with application of SPSS analytics software.

Topics covered in the course include survey research design from hypotheses development to data collection; reliability and validity assessment of multi-item measurement scales; exploratory factor analysis; descriptive analysis of quantitative data; significance testing (confidence intervals, hypotheses tests); linear regression analysis; indirect effects (moderation, mediation); variable transformations; logistic regression analysis; introduction to structural equation modeling. Practical examples and exercises are embedded in the course structure.

Details on the course content (applies in this implementation): 


This is an intensive
course that requires a substantial commitment from students. You must be
comfortable with all of the following issues. If in doubt, please contact the
instructors for any required clarification.

  • The course is designed for students who want to gain a
    practical knowledge about doing quantitative management research. This involves
    carrying out multiple in-class computer assignments, taking part in class
    discussions, and working independently on the provided case-study assignment
    during the exercise sessions. We expect you to be an active part of the
    learning process and ensure that your schedule allows you to attend all

  • It is your responsibility to ask for clarification
    during the lectures and exercise sessions (or after them, or during office
    hours) if something is not clear.

  • There are four assignments to be returned during the
    course. You must be prepared to present and discuss the completed work in the
    class and contribute to other students’ learning. In addition, a successful
    study strategy involves keeping up with the readings as we go. Your schedule
    should allow for this.

  • The course reading package consists of scientific
    articles. Unavoidably some parts are demanding, technically as well as language

  • We expect that you have read all the required
    pre-readings (four journal articles specified in section 7 Readings) before the
    first lecture. When reading the articles, please pay particular attention to
    their Hypotheses, Methods, and Result sections.  

  • The course assignments require both quantitative and
    qualitative analysis skills and judgment which reflect the knowledge gained in
    the class and through independent work on the assigned reading. The assignments
    may not have a single correct answer and are graded for quality, as reflected
    in the rigorous execution, strong argumentation of the choices you make, and
    clarity of reporting.


This course is designed to introduce
students in various areas of business to the principles and practice of
quantitative research. The course covers the fundamentals of the research
process, the statistical analysis and modelling of data, and quantitative
approaches to research, all with a focus on issues specific to business studies
and emphasis on hands-on experience. Upon successful completion of this course,
you should:

  • Be
    better able to interpret and critically assess methodological execution of
    empirical management studies

  • Understand
    the uses and limitations of common tools for analyzing quantitative data

  • Develop
    competence in collecting, modelling, and interpreting quantitative data

  • Be
    prepared to utilize obtained methodological skills in master’s thesis and/or
    doctoral dissertation

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

100% assignments

Elaboration of the evaluation criteria and methods, and acquainting students with the evaluation (applies in this implementation): 


  • Assignments 70% (see below for further information):

  • Final course presentations: 10%.

  • Active participation and knowledge of SPSS analytics
    software application: 20%.

  • All assignments need to be completed in order to pass
    the course.

  • Final grade (0 to 5) is based on cutoff points below:

0-50 points     = 0

50-59 points   = 1

60-69 points   = 2

70-79 points   = 3

80-89 points   = 4

90-100 points = 5

All assignments have to be returned and
a final course presentation made in order to get a final grade for the course.

Note that turning in class assignments
is considered acknowledgement of guidelines on scholastic honesty and academic


1: article-based measurement scales assignment, 10 points available

2: article-based analysis methods assignment, 10 points available

3: classroom SEM exercise, 15 points available

4: case-based coursework project, 35 points available

Workload (valid 01.08.2018-31.07.2020): 

Contact teaching 36h
Independent work 124h
Total 160h (6 ECTS)

Details on calculating the workload (applies in this implementation): 

Classroom hours


Class preparation







160h (6 op)

Study Material (valid 01.08.2018-31.07.2020): 

Compulsory readings: Articles included in the syllabus.

Recommended readings (both textbooks are recommended, although not compulsory, for completing the course assignments): Field, A. (2009) Discovering Statistics Using SPSS. Sage Publications Ltd: London, UK.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014 or any earlier edition) Multivariate data analysis. Pearson Education Limited: Essex, UK.


Details on the course materials (applies in this implementation): 

(read before the first lecture)

Ertug, G.,
Cuypers, I. R. P., Noorderhaven, N. G., Bensaou, B. M. 2013.
Trust between
international joint venture partners: Effects of home countries. Journal of International Business Studies,
44: 263–282.

Renko, M.
2013. Early challenges of nascent social entrepreneurs. Entrepreneurship Theory and Practice, 37(5): 1045–1069.

Schilke, O.
2014. On the contingent value of dynamic capabilities for competitive
advantage: The nonlinear moderating effect of environmental dynamism. Strategic Management Journal, 35:

Sliter, M.,
Kale, A., & Yuan, Z. 2014. Is humor the best medicine? The buffering effect
of coping humor on traumatic stressors in firefighters. Journal of Organizational Behavior, 35: 257–272.



Field, A.
(2009) Discovering Statistics Using
. Sage Publications Ltd: London, UK.

Hair, J. F.,
Black, W. C., Babin, B. J., & Anderson, R. E. (2014 or any earlier edition)
Multivariate data analysis.
Pearson Education Limited: Essex, UK.


research design

Bono, J. E.,
& McNamara, G. 2011. Publishing in AMJ – Part2: Research design. Academy of Management Journal,
54(4): 657-660.

Sparrowe, R.
T., & Mayer, K. 2011. Publishing in AMJ – Part4: Grounding hypotheses. Academy of Management Journal,
54(6): 1098-1102.

Zhang, Y.,
& Shaw, J. D. 2012. Publishing in AMJ – Part5: Crafting the methods and
results. Academy of Management Journal,
55(1): 8-12.

Podsakoff, P.
M., MacKenzie, S. B., Jeong-Yeon, L., & Podsakoff, N. P. 2003. Common
method biases in behavioral research: A critical review of the literature and
recommended remedies. Journal of
Applied Psychology
, 88(5): 879-903.



Tang, J., Kacmar, K. M., & Busenitz,
L. 2012. Entrepreneurial alertness in the pursuit of new opportunities. Journal
of Business Venturing
, 27: 77-94.

Busenitz, L., Gomez, C., & Spencer,
J. 2000. Country institutional profiles: Unlocking entrepreneurial phenomena. Academy
of Management Journal
, 43(5): 994-1003.

Benzing, C., Chu, H. M., & Kara, O.
2009. Entrepreneurs in Turkey: A factor analysis of motivations, success
factors, and problems. Journal of Small Business Management,
47(1): 58-91.


Logistic regression
and analysis of categorical data

Morschett, D. 2006. Firm-specific
influences on the internationalization of after-sales service activities in
foreign markets. Journal of Service Marketing, 20(5): 309-323.


equation modelling

Lei, P.-W., &
Wu, Q. 2007. Introduction to structural equation modelling: Issues and
practical considerations. Retrieved from

Diamantopoulos, A., and J.A. Siguaw
2000. Introducing LISREL. London:

Gerbing, D. W., & Anderson, J. C.
1988. An updated paradigm for scale development incorporating unidimensionality
and its assessment. Journal of
Marketing Research
, XXV(May): 186-192.


Hult, G. T.,
Ketchen, D. J., Griffith, D. A., Finnegan, C. A. et al. 2008. Data equivalence
in cross-cultural international business research: assessment and guidelines. Journal of International Business Studies,
39: 1027-1044.

Substitutes for Courses (valid 01.08.2018-31.07.2020): 

Replaces courses 80E80100 and 26E24000 Quantitative research methods. Please note that you can take either one of the courses.

Course Homepage (valid 01.08.2018-31.07.2020):

Grading Scale (valid 01.08.2018-31.07.2020): 


Registration for Courses (valid 01.08.2018-31.07.2020): 

via WebOodi

Further Information (valid 01.08.2018-31.07.2020): 

Max 24 students. Participants are selected based on their program status, according to the following priority order:

1. MIB / Strategy / Global Management / CEMS
2. Other School of Business students
3. Other Aalto students

Students must attend the first lecture to ensure their place in the course.
There is compulsory lecture attendance (85 %), including the last day of the course, when the group presentations are made.

Additional information for the course (applies in this implementation): 


University Code of Academic Integrity and Handling Thereof  

Details on the schedule (applies in this implementation): 









Introduction to the course. Research design
in management studies.

Bono & McNamara (2011)

Sparrowe & Mayer (2011)

Zhang, Y., & Shaw (2012)

Podsakoff et al. (2003)

At the end of this session, we will provide
you with the guidelines for course assignments. 




Measurement scales: reliability and validity
assessment. Exploratory factor analysis.

Tang et al. (2012)

Busenitz et al. (2000)

Benzing et al. (2009)





Descriptive statistics: graphical methods of
data examination, missing values, outliers, fundamental assumptions for
multivariate analysis (normality, homoscedasticity, linearity, absence of correlated
errors). Transformations, categorical predictors (dummy


Returning and presenting Assignment 1*




Regression analysis: simple and multiple
regression, assumptions, estimation of regression model and assessment of
overall model fit, output interpretation. Hypotheses testing.

Morschett (2006)





Session: W
orking on your research project






Regression analysis cont.: Logistic regression


Returning and presenting Assignment 2*





Regression analysis cont.: indirect effects (moderation, mediation).







Structural Equation Modeling – Theory

Lei & Wu (2007)





Structural Equation Modeling - Practice


Assignment 3* as classroom exercise





Continue working on your research project






Course project day


Returning and presenting Assignment 4*

There is a possibility to improve your work
based on the feedback from the presentations session.

The updated version of Assignment 4 is due in
written form by 9.12.2018

* Note: Be ready to present all home
assignments in the class.


Registration and further information