Syllabus

LEARNING OUTCOMES

Credits: 6

Schedule: 28.10.2019 - 25.11.2019

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

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

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

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:

  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):
  

  • 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. can be accessed from: https://onlinelibrary.wiley.com/doi/full/10.1002/job.1868
  • Umphress, E. E., Bingham, J. B., & Mitchell, M. S. (2010). Unethical behavior in the name of the company: The moderating effect of organizational identification and positive reciprocity beliefs on unethical pro-organizational behavior. Journal of Applied Psychology, 95(4), 769-780. can be accessed from: https://pdfs.semanticscholar.org/3d6e/d4caa8053c0f4e66dcbf531ead2d8610f4b6.pdf
  • Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50(3), 906-914, can be accessed from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.456.1101&rep=rep1&type=pdf

  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

• Valid 01.08.2020-31.07.2022:

  via WebOodi

• 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
  

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.

  
  

Description

Registration and further information