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040501 KU Data Analysis for Marketing Decisions (MA) (2020W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Continuous assessment of course work

It is absolutely essential that all registered students attend the first session (Introduction/Vorbesprechung) as failure to do so will result in their exclusion from the course.

By registering for this course you agree that the automated plagiarism software Turnitin processes and stores your data (i.e. project work, seminar papers, etc.).

Exchange students must have successfully completed at least a basic/introductory marketing course at their home university. To be able to attend the course they must hand in a relevant transcript/certificate by October 11th, 2020.



Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first serve).


max. 30 participants
Language: English


Classes (iCal) - next class is marked with N

Midterm Exam: 05.11.2020, 11:30, digital
Final Exam: 08.01.2021, 11:30, digital

Friday 02.10. 11:30 - 13:00 Digital
Friday 09.10. 11:30 - 13:00 Digital
Friday 16.10. 11:30 - 13:00 Digital
Friday 23.10. 11:30 - 13:00 Digital
Friday 30.10. 11:30 - 13:00 Digital
Thursday 05.11. 11:30 - 13:00 Digital
Friday 06.11. 11:30 - 13:00 Digital
Friday 13.11. 11:30 - 13:00 Digital
Friday 20.11. 11:30 - 13:00 Digital
Friday 27.11. 11:30 - 13:00 Digital
Friday 04.12. 11:30 - 13:00 Digital
Friday 11.12. 11:30 - 13:00 Digital
Friday 18.12. 11:30 - 13:00 Digital
Friday 08.01. 11:30 - 13:00 Digital
Friday 15.01. 11:30 - 13:00 Digital


Aims, contents and method of the course

Sound knowledge of statistics and data analysis is an essential requirement for marketing research and managerial decision-making – more than one would expect! The present course discusses (a) key concepts of statistics and statistical inference (e.g., NHST, Type I and II error, confidence intervals) and (b) different methods of data analysis (e.g., t-test, χ2 test, ANOVA, regression analysis) in a series of lectures that combine theory, illustrative examples, and hands-on exercises. Although IBM SPSS software is used in the context of DAMD, the course is not a tutorial on the SPSS software package. Rather it focuses on the logic, the implementation, and the interpretation of statistical data analysis in general. Students who successfully complete DAMD will be equipped with the ability to effectively carry out data analysis projects in their later academic and professional career.

The major topics covered in the course are:
Theoretical introduction to basic research terms: data, variables, models, research process, sample, population, measurement scales, etc.
Introduction and familiarization with the statistical software SPSS
Clearing and preparing data for further analysis
Descriptive statistics: central tendency, variability, skewness, kurtosis
Testing statistical assumptions: normality, homogeneity of variance, homoscedasticity
Inferential statistics and hypothesis testing: parameter estimates, sampling error, confidence intervals, Type I and Type II errors, p-values, t-values
Performing comparisons: chi-square test, independent samples t-test, paired-sample t-tests, analysis of variance
Investigating relationships: bivariate correlation, partial correlation
Regression models: simple linear regression, multiple linear regression, logistic regression
Finding structures using Factor Analysis
Presenting, reporting and interpreting results
Identifying practical and theoretical implications drawn from statistical analyses

The sessions involve theory discussions accompanied by practical examples and hands-on exercises. Lectures primarily provide background knowledge in understanding the theory and logic behind the statistical techniques and finally to illustrate how to perform and interpret the data analysis using the SPSS environment as an example. Note that successful completion of DAMD depends greatly on whether students systematically review the class material.

Assessment and permitted materials

Students’ performance in the course is assessed as follows:
Midterm exam: 30%
Group Project: 25%
Final exam: 45%

No material other than a dictonary may be used in the exams/quiz.

Minimum requirements and assessment criteria

The course has “prüfungsimmanenten Charakter”, therefore attendance is mandatory throughout the semester – more than three absences automatically results in a grade of 5 (“fail”).

In total, a minimum of 50 percent is needed to pass the course. The grading system is the following: 0 to 49% - grade 5, 50 to 59% - grade 4, 60 to 69% - grade 3, 70 to 79% - grade 2, 80 to 100% - grade 1.
Students who fail must repeat the entire course (and must register in the usual way next time the course is offered). No opportunities for make-ups will be offered.

Examination topics

The midterm exam is based on the topics covered in sessions 1 to 5 and the corresponding book chapters. The exam typically (but not necessarily) involves a combination of single-choice/true-false questions.

The midterm exam is based on the topics covered in sessions 1 to 5 and the corresponding book chapters. The exam typically (but not necessarily) involves a combination of single-choice/true-false questions.

The group project is an assignment conducted by teams of approx. 5 students and involves the analysis of a dataset as well as the interpretation and presentation of the relevant results. The group project is a collective effort and all members are expected to contribute as the same grade is awarded to students belonging to the same team. Detailed instructions will be provided in class.

The final exam covers all topics discussed in the lectures and corresponding book chapters. The exam typically includes questions of different formats (e.g., multiple-choice questions and mini cases with open-ended questions).

Reading list

Required textbook: Field, A. (2018), Discovering Statistics Using IBM SPSS Statistics (5th edition), Sage Publications: London [ISBN: 9781526445780] OR (old 4th edition): Field, A. (2013), Discovering Statistics Using SPSS (4th edition), Sage Publications: London [ISBN: 978-1-4462-4918-5].
Recommended additional textbook: Diamantopoulos, D. and Schlegelmilch, B. (2000), Taking the Fear out of Data Analysis (2nd edition), South-Western CENGAGE Learning: London [ISBN: 978-1-86152-430-0].
Complementary material: Marshall, E. (2016), The Statistics Tutor’s Quick Guide to Commonly Used Statistical Tests, University of Shefield - Statstutor Community Project, [Retrieved from www.statstutor.ac.uk]. → will be available on Moodle

Systematically reviewing the course material (slides, book chapters, and exercises) is as essential as being (mentally) present in the online lectures!

Association in the course directory

Last modified: Mo 05.10.2020 10:08