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040501 PR KFK iMAR: Intern.Data Analysis: Meth.& Interpr.(E) (2015W)
Prüfungsimmanente Lehrveranstaltung
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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/
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 14.09.2015 09:00 bis Do 24.09.2015 14:00
- Abmeldung bis Mi 14.10.2015 23:59
Details
max. 30 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Mittwoch 07.10. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Mittwoch 14.10. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Mittwoch 21.10. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Mittwoch 28.10. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Mittwoch 04.11. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Mittwoch 11.11. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Mittwoch 18.11. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Mittwoch 25.11. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Mittwoch 02.12. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Mittwoch 09.12. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Mittwoch 13.01. 09:45 - 11:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Mittwoch 20.01. 11:30 - 13:00 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Mittwoch 27.01. 11:30 - 13:00 Hörsaal 1 Oskar-Morgenstern-Platz 1 Erdgeschoß
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
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.
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 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).
Literatur
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.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: 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.