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040501 KU Data Analysis for Marketing Decisions (MA) (2016W)

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 5th, 2016 (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 14th, 2016.

http://international-marketing.univie.ac.at/teaching/master-bwibw/courses-ws-1617/#c615745

Registration/Deregistration

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

Details

max. 30 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Final Exam: Tuesday(!), 31.01.2017, 09:45-10:45, HS 6

Wednesday 05.10. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 12.10. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 19.10. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 09.11. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 16.11. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 23.11. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 30.11. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 07.12. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 14.12. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 11.01. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 18.01. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Wednesday 25.01. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß

Information

Aims, contents and method of the course

In (international) marketing research, one needs profound knowhow on data analysis and statistics – more than one would expect! This course aims at familiarizing students with (a) the stages of the data analysis process, (b) key statistical methods, and (c) the software package IBM SPSS. A combination of lectures and hands-on exercises will prepare students for conducting their own projects in other marketing courses, their master thesis and/or their post-academic career.

The major topics covered in the course are:
Theoretical introduction to basic marketing research terms: data, variables, models, marketing research process, sample, population, sampling methods, 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, autocorrelation
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 (one-way, factorial, repeated-measures)
Investigating relationships: bivariate correlation, ordinal 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

Sessions include theoretical background knowledge of the relevant analytical techniques combined with direct hands-on application of the techniques on real-life datasets using SPSS.

The course involves a combination of formal lectures and lab sessions. Formal lectures will provide background knowledge on the nature of data, hypotheses formulation and the selection of an appropriate statistical technique. The lab sessions will provide the opportunity to get familiar with SPSS and gain hands-on experience in conducting and interpreting analysis techniques. To consolidate the gained knowledge, students will execute two projects.

Assessment and permitted materials

Performance in the course will be assessed as follows:
Individual Assignment: 20%
Team Assignment: 35%
Final Exam: 45%

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

Minimum requirements and assessment criteria

In total, a minimum of 50 percent needs to be attained 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 Individual Assignment is a SPSS homework conducted by each student individually.

The Team Assignment is a more complex homework conducted by teams of 3 students; the same grade will be awarded to students belonging to the same team. Detailed instructions will be provided in the course.

The final exam is in written form and will be in English. Examinable material includes all indicated topics treated in theory and practice sessions. The exam will include questions of multiple formats (single choice questions, open-ended questions, etc.).

Reading list

The required textbook is: Field, A. (2013), Discovering Statistics Using SPSS (4th edition), Sage Publications: London [ISBN: 978-1-4462-4918-5 (pbk)]. An accompanying website provides additional useful material (http://www.uk.sagepub.com/field4e/).

A recommended additional textbook is: 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].

Reading and consulting online resources is an essential part of the course (especially as preparation for the sessions!) and as important as attending lectures.

Association in the course directory

Last modified: Mo 07.09.2020 15:29