040501 KU Data Analysis for Marketing Decisions (MA) (2018S)
Prüfungsimmanente Lehrveranstaltung
Labels
It is absolutely essential that all registered students attend the first session on March 6th, 2018 (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 March 11th, 2018.http://international-marketing.univie.ac.at/teaching/master-bwibw/courses-ss-18/#c643049
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mi 14.02.2018 09:00 bis Mi 21.02.2018 12:00
- Abmeldung bis So 11.03.2018 23:59
Details
max. 30 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Dienstag
06.03.
09:45 - 11:30
Hörsaal 16 Oskar-Morgenstern-Platz 1 2.Stock
Dienstag
13.03.
09:45 - 11:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Dienstag
20.03.
09:45 - 11:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Dienstag
10.04.
09:45 - 11:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Dienstag
17.04.
09:45 - 11:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Montag
23.04.
15:00 - 16:30
Hörsaal 14 Oskar-Morgenstern-Platz 1 2.Stock
Dienstag
24.04.
09:45 - 11:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Mittwoch
02.05.
11:30 - 13:00
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Dienstag
08.05.
09:30 - 11:15
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Dienstag
15.05.
09:45 - 11:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Mittwoch
23.05.
11:30 - 13:00
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Dienstag
05.06.
09:45 - 11:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Dienstag
12.06.
09:45 - 11:30
PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Montag
18.06.
11:30 - 13:00
Hörsaal 6 Oskar-Morgenstern-Platz 1 1.Stock
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
Performance in the course will be assessed as follows:
Midterm exam: 25%
Group project: 30%
Final exam: 45%No material other than a dictonary may be used in the final exam.
Midterm exam: 25%
Group project: 30%
Final exam: 45%No material other than a dictonary may be used in the final exam.
Mindestanforderungen und Beurteilungsmaßstab
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.
Prüfungsstoff
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 multiple-choice/single-choice 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 the 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 examinable material of the final exam includes all topics covered in the lectures and the corresponding book chapters. The exam will include questions of different formats (e.g., multiple choice questions and mini cases with open-ended questions).
Literatur
Required textbook is: Field, A. (2013), Discovering Statistics Using SPSS (4th edition), Sage Publications: London [ISBN: 978-1-4462-4918-5].
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].
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].
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Mo 07.09.2020 15:29
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 classes involve theoretical discussions that are accompanied by several practical examples and hands-on exercises. Primarily, the lectures provide background knowledge on the statistical theory, the selection, and the understanding of various data analysis techniques. In addition, hands-on exercises introduce the SPSS environment and illustrate how to perform and interpret statistical data analyses. Note that successful completion of DAMD depends greatly on students’ effort to systematically review the material and suggested homework throughout the semester.