Universität Wien FIND

Due to the COVID-19 pandemic, changes to courses and exams may be necessary at short notice (e.g. cancellation of on-site teaching and conversion to online exams). Register for courses/exams via u:space, find out about the current status on u:find and on the moodle learning platform.

Further information about on-site teaching can be found at https://studieren.univie.ac.at/en/info.

040501 KU Data Analysis for Marketing Decisions (MA) (2019W)

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 on October 2nd, 2019 (Introduction/Vorbesprechung) as failure to do so will result in their exclusion from the course.

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 8th, 2019.

https://international-marketing.univie.ac.at/teaching/master-bwibw/courses-ws-1920/#c652807

Registration/Deregistration

Details

max. 30 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Friday 13.12. 13:15-14:45 Hörsaal 8 Oskar-Morgenstern-Platz 1 1.Obergeschoß

Wednesday 02.10. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 09.10. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 16.10. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 23.10. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 30.10. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Monday 04.11. 11:30 - 13:00 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß
Wednesday 06.11. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 13.11. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 20.11. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 27.11. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 04.12. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 08.01. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Monday 20.01. 11:30 - 13:00 Hörsaal 4 Oskar-Morgenstern-Platz 1 Erdgeschoß

Information

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 Data Analysis for Markting Decisions (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 that are accompanied by practical examples and hands-on exercises. Lectures primarily provide background knowledge in understanding the theory and logic behind the analytical techniques and finally introduce the SPSS environment in order to illustrate how to perform and interpret the statistical analysis. Please note that successful completion of DAMD depends greatly on whether students systematically review the class material and perform the suggested homework throughout the semester.

Assessment and permitted materials

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

No material other than a dictonary may be used in the final exam.

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 group project is an assignment conducted by teams of 3 to 5 students and involves the analysis of a dataset as well as the interpretation and presentation of the relevant results. The grade of the group project takes into account both group and individual performance and is determined by the overall quality of the assignment weighted by the individual contribution of each member to the group project (as determined by peer-evaluation). Thus, a different grade might be 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 (physically and mentally) present in the lectures!

Association in the course directory

Last modified: Mo 07.09.2020 15:19