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.

LEARNING OUTCOMES

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: 28.10.2019 - 25.11.2019

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

Teacher in charge (applies in this implementation): Kristiina Herold, Young Ji

Contact information for the course (valid 17.09.2019-21.12.2112):

Instructor’s contact
information

Course information

Post-doctoral researcher Kristiina Herold

Post-doctoral researcher Young Ji

kristiina.herold@aalto.fi  

young.ji@aalto.fi;

Department of Management Studies

Office Hours: Wednesday after class

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

Academic Year: 2019-2020, Period II

Location: A046, Otakaari 1

Language of Instruction: English

Course Website: https://mycourses.aalto.fi/course/view.php?id=23686



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

CONTENT, ASSESSMENT AND WORKLOAD

Content
  • 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:

    PREREQUISITES

    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 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 lectures.
    • 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 wise.

    • We expect that you have read all the required pre-readings (three journal articles specified under "Compulsory pre-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.

    LEARNING OUTCOMES

    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 modeling 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, modeling, 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:

    ASSESSMENT AND GRADING

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

      • Assignment 1 (10 %): Article-based analysis (conceptual domain);

      • Assignment 2 (10 %): Article-based analysis (observable domain);

      • Assignment 3 (15%): In-class 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 acknowledgment of guidelines on scholastic honesty and academic integrity.

    ASSIGNMENTS

    Assignment 1: Article-based analysis (conceptual domain), 10 points available

    Assignment 2: Article-based analysis (observable domain), 10 points available

    Assignment 3: In-class data analysis exercise (using EFA & multiple regression or CFA & SEM), 15 points available

    Assignment 4: Case-based coursework project, 35 points available


Workload
  • Valid 01.08.2020-31.07.2022:

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

  • Applies in this implementation:

    1. Classroom hours

    36h

    Class preparation

     

    33h

     

    Assignments

    91h

    Total

    160h (6 op)



DETAILS

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.

    Availability

  • Applies in this implementation:

    If you are unfamiliar with getting access to academic articles, please find more information here: https://learningcentre.aalto.fi/en/access-to-scientific-articles

    Compulsory pre-readings (read before the first lecture):

    Recommended readings

    Readings covered in class (see "Details on the schedule" for specific book chapters associated with each lecture):

    Field, A. (2009) Discovering Statistics Using SPSS. Sage Publications Ltd: London, UK., or any newer edition

    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.

    Survey 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.

    Measurement scales:

    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.

    Structural equation modeling:

    Lei, P.-W., & Wu, Q. 2007. Introduction to structural equation modeling: Issues and practical considerations. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.1745-3992.2007.00099.x/epdf

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

    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.

    Advanced reading:

    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

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:

    ETHICAL RULES

    Aalto University Code of Academic Integrity and Handling Violations Thereof

    https://into.aalto.fi/display/ensaannot/Aalto+University+Code+of+Academic+Integrity+and+Handling+Violations+Thereof

Details on the schedule
  • Applies in this implementation:

     

    Date

    Topic

    Readings covered in class

    Assignments

    1.

    28.10.2019

    9.15-12.00

    Introduction
    to the course and quantitative research
    methods: research
    design in management studies.

    Field A. (2018) 5th ed. Chapter 1 & 2

    or

    Field A. (2009) 3rd ed. Chapter 1 & 2

    During the first day, we will provide
    you with the guidelines for course assignments. 

    2.

    28.10.2019

    13.15-16.00

    Introduction to
    quantitative research methods 2: Survey research & SPSS

    Field A. (2018) 5th ed. Chapter 4 & 8

    or 

    Field A. (2009) 3rd ed. Chapter 3 & 6

     

    3.

    30.10.2019

    9.15-12.00

    Measurement
    scales reliability and validity assessment & Exploratory Factor Analysis (EFA).


    Field A. (2018) 5th ed. Chapter 18

    or

    Field A. (2009) 3rd ed. Chapter 17

    Returning Assignment 1* by the end of the day 23:59.

    4.

    04.11.2019

    9.15-12.00

    Regression
    analysis: fundamental assumptions for multivariate analysis, simple and
    multiple regression.


    Field A. (2018) 5th ed. Chapter 9

    or

    Field A. (2009) 3rd ed. Chapter 7


     

    5.

    04.11.2019

    13.15-16.00

    Regression analysis cont.: hypotheses testing practice class.

    Field A. (2018) 5th ed. Chapter 9

    or

    Field A. (2009) 3rd ed. Chapter 7

     

    6.

    06.11.2019

    9.15-12.00

    Regression
    analysis cont.: indirect effects (moderation,
    mediation), logistic regression, and curvilinear modeling.

    For mediation:

    Field A. (2018) 5th ed. Chapter 11

    For moderation:

    Field A. (2018) 5th ed. Chapter 11

    also covered in

    Hair et al.'s Chapter 4 on "Multiple regression analysis"

    For logistic regression:

    Field A. (2018) 5th ed. Chapter 20

    or 

    Field A. (2009) 3rd ed. Chapter 8

    also covered in

    Hair et al.'s Chapter 5 on "Multiple discriminant analysis and logistic regression"

    For curvilinear modeling:

    Hair et al.'s Chapter 4 on "Multiple regression analysis"

    Returning Assignment 2* by the end of the day 23:59

    7.

    11.11.2019

    9.15-12.00

     

    Exercise Session: Results and recap of assignment 1 & Working
    on your research project

     

     In class recap on Assignment 1.

    8.

    18.11.2019

    9.15-12.00

     

    Structural Equation Modeling

    Hair et al.'s

    Chapter 10 "Structural equation modeling: An introduction," 

    Chapter 11 "SEM: Confirmatory factor analysis," and 

    Chapter 12 "SEM: Testing a structural model." 

     

    9.

    18.11.2019

    13.15-16.00

    In-class assignment.

     

    Assignment 3 as classroom exercise.

    10.

    20.11.2019

    9.15-12.00

     

    Exercise
    Session: Results and recap of assignment 2 &
    Continue working on your research project.

     

     In class recap on Assignment 2

    11.

    25.11.2019

    9.15-16.00

    Course
    project day: group
    presentations and advanced quant method insights.

     

    Presenting Assignment 4*

    There is a possibility to improve assignment 4
    based on the feedback from the presentations session.

    The updated version of Assignment 4 is due in
    written form by 9.12.2019

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