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2017/2018  KAN-CCMVV2032U  Applied Statistics

English Title
Applied Statistics

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Quarter
Start time of the course First Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 70
Study board
Study Board for MSc in Economics and Business Administration
Course coordinator
  • Torben Hansen - Department of Marketing (Marketing)
Main academic disciplines
  • Marketing
  • Statistics and quantitative methods
Last updated on 22-02-2017

Relevant links
Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors: After completing this course, the students should be able to:
  • Understand the interplay between the design of theoretical models and applied statistics
  • Understand advanced statistical techniques such as structural equation modelling and other multivariate data analysis techniques
  • Understand the use of SPSS and AMOS
  • Understand how complex real-world problems can be measured and solved
  • Understand the validity and reliability of data and statistical results
Course prerequisites
Basic knowledge of quantitative research methods (i.e. applied statistics) is required.
Examination
Applied Statistics:
Exam ECTS 7,5
Examination form Written sit-in exam on CBS' computers
Individual or group exam Individual exam
Assignment type Written assignment
Duration 4 hours
Grading scale 7-step scale
Examiner(s) One internal examiner
Exam period Autumn
Aids Open book: all written and electronic aids, including internet access
Make-up exam/re-exam
Same examination form as the ordinary exam
If the number of registered candidates for the make-up examination/re-take examination warrants that it may most appropriately be held as an oral examination, the programme office will inform the students that the make-up examination/re-take examination will be held as an oral examination instead.
Course content and structure

This course provides substantial insights into quantitative research methods that are relevant for dealing with statistically based research-problems.Students are introduced to advanced quantitative research methods such as Structural Equation Modelling and MANOVA/MANCOVA. This course equips students with the competencies necessary to better understand the usability of quantitative research methods in order to identify and handle complex market-oriented problems. Hence, this course enables students to further develop their quantitative practical problem-solving and analytical skills. Based on real-world consumer trends and challenges students will learn how to identify quantitative research problems, how to develop appropriate problem-focused quantitative research frameworks, how to apply these, and how to provide suggestions, limitations and implications for management.

 

 
Teaching methods
The course is given in lecture form with class work and with emphasis on teacher-student and student-student dialogues. Also, special emphasis is given on the interplay between consumer behaviour models and advanced applied statistics. In order to investigate identified research questions, the SPSS and AMOS statistical packages are integrated into the lectures. The students are expected to actively participate in class discussions concerning the usability of advanced quantitative techniques when dealing with complex market-related problems.
Feedback during the teaching period
During the entire course students will receive feedback on their performance and progress when working with the course assignments and when participating in dialogues and discussions in class.
Student workload
Forberedelse 123 hours
Undervisning 33 hours
Eksamen 50 hours
Further Information

 

 
Expected literature

Expected literature:

 

Joseph F. Hair, William C. Black, Barry J. Babin, and Rolph E. Anderson (2010), Multivariate Data Analysis, Pearson Prentice Hall, 7th edition.​

 

Anderson, J.C. & Gerbing, D.W. (1988). Structural Equation Modelling in Practice: A Review and

Recommended Two-Step Approach. Psychological Bulletin 103(November), 411-423.

 

Bagozzi, R.P. & Yi, Y. (1988). On the Evaluation of Structural Equation Models. Journal of the Academy of Marketing Science, 16(1), 74-94.

 

Baron, R., & Kenny, D. (1986). The moderator–mediator variable distinction in social psychological research. Journal of Personality and Social Psychology, 51, 1173–1182.

 

Baumgartner, H. & Hamburg, C. (1996). Applications of structural equation modeling in marketing and consumer research: A review. International Journal of Research in Marketing, 13, 139-161.

 

da Silva, A.S., Farina, M.C., Gouvêa, M.A., & Donaire,. D. (2015), A Model of Antecedents for the Co-Creation of Value in Health Care: An Application of Structural Equation Modeling. Brazilian Business Review, Nov/Dec., 121-149.

 

Hansen, T. (2012). Understanding Trust in Financial Services: The Influence of Financial Healthiness, Knowledge, and Satisfaction. Journal of Service Research 15(3), 280-295.

 

Hansen, T. & Thomsen, T.U. (2013). I Know what I Know, but I'll Probably Fail Anyway: How Learned Helplessness Moderates the Knowledge Calibration - Dietary Choice Quality Relationship. Psychology & Marketing, 30(11), 1008-1028.

 

Joshi, P, Suman, S.K. & Sharma M. (2015). The Effect of Emotional Intelligence on JobS atisfaction of Faculty: A Structural Equation Modeling Approach. Journal of Organizational Behavior, XIV(3), 58-70.

 

Little, T.D., Bovaird, J.A., & Widaman, K.F. (2006). On the merits of orthogonalizing powered and interaction terms: Implications for modeling interactions among latent variables. Structural Equation Modeling: A Multidisciplinary Journal 13(4), 497-519.

 

Sakkthivel A. M. & Balasubramaniyan S (2015), Influence of Social Network Websites over Women Consumers from Islamic Religion: A Structural Equation Modelling Approach. Journal of Internet Banking & Commerce, 20(2), 7 pages.
Last updated on 22-02-2017