Universität Wien

220078 SE SE Advanced Data Analysis 3 (2016W)

5.00 ECTS (2.00 SWS), SPL 22 - Publizistik- und Kommunikationswissenschaft
Prüfungsimmanente Lehrveranstaltung

This course is the third in the three-course methodology sequence required of all students enrolled in the M.A. programs in Communication Science. The course covers an introduction to the analysis of data using the general linear model and serves as a foundation for more advanced statistical methods courses offered throughout the university. Focus is on conceptual understanding rather than mathematical computation. Students will gain experience practicing their learning through various assignments using SPSS and R software. The course will take the form of interactive workshop sessions, placing particular emphasis on student participation. Theoretical discussion of key issues will be accompanied with examples taken from literature and practical exercises.
Moodle

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 30 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

Mittwoch 12.10. 09:15 - 10:45 Seminarraum H10, Rathausstraße 19, Stiege 2, Hochparterre
Mittwoch 19.10. 09:15 - 10:45 Seminarraum H10, Rathausstraße 19, Stiege 2, Hochparterre
Mittwoch 09.11. 09:15 - 10:45 Seminarraum H10, Rathausstraße 19, Stiege 2, Hochparterre
Mittwoch 16.11. 09:15 - 10:45 Seminarraum H10, Rathausstraße 19, Stiege 2, Hochparterre
Mittwoch 23.11. 09:15 - 10:45 Seminarraum H10, Rathausstraße 19, Stiege 2, Hochparterre
Mittwoch 30.11. 09:15 - 10:45 Seminarraum H10, Rathausstraße 19, Stiege 2, Hochparterre
Mittwoch 07.12. 09:15 - 10:45 Seminarraum H10, Rathausstraße 19, Stiege 2, Hochparterre
Mittwoch 14.12. 09:15 - 10:45 Seminarraum H10, Rathausstraße 19, Stiege 2, Hochparterre
Mittwoch 11.01. 09:15 - 10:45 Seminarraum H10, Rathausstraße 19, Stiege 2, Hochparterre
Mittwoch 18.01. 09:15 - 10:45 Seminarraum H10, Rathausstraße 19, Stiege 2, Hochparterre
Mittwoch 25.01. 09:15 - 10:45 Seminarraum H10, Rathausstraße 19, Stiege 2, Hochparterre

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

(1) To develop a clear conceptual understanding of simple and multiple regression, logistic regression, multinomial regression, and multilevel linear modeling. Other topics in linear models may be introduced as time allows.
(2) To acquire skills of developing and testing aforementioned statistical models. At the end of the course, you should be able to correctly formalize your theoretical hypotheses, apply
statistical tests, identify model assumptions, interpret model parameters, and perform model comparisons.
(3) To gain practical experience in using various statistical software such as SPSS or R to test the aforementioned statistical models.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Course grading is based on (a) two mini take-home assignments (worth 15% each) and (b) one final paper (worth 70%).

The two assignments deal with critically demonstrating your understandings of key concepts in linear modeling fundamentals and its extensions, while for the final paper you will be required to use one of the advanced linear modeling technique (not regular OLS regression) to estimate and evaluate a conceptual model with empirical data of your choice. Further details will be provided in the first session.

The due date of the assignments will be announced when the assignments are distributed (typically one week after the assignment is distributed). You may NOT work with other students when working through the assignments, and you must submit your own independently written answers for each assignment/paper.

Mindestanforderungen und Beurteilungsmaßstab

Both assignments will account for 15% of the final grade each (a total of 30%), and the final paper will account for 70% of your final grade. For successfully passing the course, participants are required to achieve at least 51% of the total points. Full details on the course grading (e.g., grading system) will be given in the first session. Although attendance is not mandatory to pass this class, ongoing in-class participation is expected.

Prüfungsstoff

Literatur

Details on the required readings will be provided in the first session. In addition, a literature list as well as accompanying texts will be available on Moodle.

Suggested readings:

Hayes, A. F. (2015). Statistical methods for communication science. Malwah, NJ: Lawrence Erlbaum.

Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach. New York: The Guilford Press

Hosmer, D. W., Jr., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression, 3rd edition. John Wiley & Sons. (ebook available through University library: http://onlinelibrary.wiley.com/book/10.1002/9781118548387)

Snijders, T. B. A. & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling, 2nd edition. Sage.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Fr 01.10.2021 00:22