Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.


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.

Credits: 6

Schedule: 29.10.2018 - 26.11.2018

Teacher in charge (valid 01.08.2020-31.07.2022): Kristiina Herold, Young Ji

Teacher in charge (applies in this implementation): Amber Geurts, Kristiina Herold

Contact information for the course (valid 09.10.2018-21.12.2112):

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:

CEFR level (applies in this implementation):

Language of instruction and studies (valid 01.08.2020-31.07.2022):

Teaching language: English

Languages of study attainment: English


  • Valid 01.08.2020-31.07.2022:

    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; hypotheses testing; linear regression; indirect effects (moderation, mediation); variable transformations. Practical examples and exercises are embedded in the course structure.

  • 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 01.08.2020-31.07.2022:

    100% assignments

  • Applies in this implementation:


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

      • Assignment 1 (10 %): article-based measurement scales

      • Assignment 2 (10 %): article-based analysis methods

      • Assignment 3 (15%): SEM exercise;

      • Assignment 4 (35 %): case-based coursework project.

    • 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

  • Valid 01.08.2020-31.07.2022:

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

  • Applies in this implementation:

    Classroom hours


    Class preparation







    160h (6 op)


Study Material
  • Valid 01.08.2020-31.07.2022:

    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.


  • 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 01.08.2020-31.07.2022:

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

Registration for Courses


Further Information
  • Valid 01.08.2020-31.07.2022:

    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.

  • 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.