040501 KU Data Analysis for Marketing Decisions (MA) (2021W)
Continuous assessment of course work
Labels
REMOTE
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 10th, 2021.https://international-marketing.univie.ac.at/studies/master-bwibw/courses-ws-202122/
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 13.09.2021 09:00 to Th 23.09.2021 12:00
- Registration is open from Mo 27.09.2021 09:00 to We 29.09.2021 12:00
- Deregistration possible until Th 07.10.2021 23:59
Details
max. 60 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Friday
01.10.
11:30 - 13:00
Digital
Friday
08.10.
11:30 - 13:00
Digital
Thursday
14.10.
11:30 - 13:00
Digital
Monday
18.10.
09:45 - 11:15
Digital
Friday
22.10.
11:30 - 13:00
Digital
Thursday
28.10.
11:30 - 12:30
Digital
Friday
29.10.
11:30 - 13:00
Digital
Friday
05.11.
13:15 - 14:45
Digital
Friday
12.11.
13:15 - 14:45
Digital
Friday
19.11.
11:30 - 13:00
Digital
Friday
26.11.
11:30 - 13:00
Digital
Friday
03.12.
11:30 - 13:00
Digital
Monday
06.12.
09:45 - 11:15
Digital
Friday
10.12.
11:30 - 12:30
Digital
Information
Aims, contents and method of the course
Assessment and permitted materials
Students’ performance in the course is assessed as follows:
Midterm exam: 30%
Group assignment: 25%
Final exam: 45%No material other than a dictionary may be used in the exams/quiz.
Midterm exam: 30%
Group assignment: 25%
Final exam: 45%No material other than a dictionary 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.
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 assignment is conducted by teams of ~ 6 students and involves answering a series of research questions by conducting and reporting the appropriate analytical techniques. The assignment reflects collective effort, so all members are expected to contribute and receive the same grade based on the evaluation of the assignment. 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, A. 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 MoodleSystematically 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: Fr 12.05.2023 00:12
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 analysesThe 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.