040501 PR KFK iMAR: Interational Data Analysis: Methods & Interpretations (E) (2015W)
Continuous assessment of course work
Labels
It is absolutely essential that all registered students attend the first session on October 7th, 2015 (Introduction/Vorbesprechung) as failure to do so will result in their exclusion from the course.Exchange students must have successfully completed at least one 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, 2015.http://international-marketing.univie.ac.at/teaching/courses-ws1516/international-data-analysis/
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 14.09.2015 09:00 to Th 24.09.2015 14:00
- Deregistration possible until We 14.10.2015 23:59
Details
max. 30 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Wednesday 07.10. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Wednesday 14.10. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Wednesday 21.10. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Wednesday 28.10. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Wednesday 04.11. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Wednesday 11.11. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Wednesday 18.11. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Wednesday 25.11. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Wednesday 02.12. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Wednesday 09.12. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Wednesday 13.01. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Wednesday 20.01. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Wednesday 27.01. 11:30 - 13:00 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
Information
Aims, contents and method of the course
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.
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 2 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 comprise single choice questions (20 percent) and open-ended questions (80 percent).
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
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 analysesSessions 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.