Universität Wien FIND
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220078 SE SE Advanced Data Analysis 3 (2018W)

Prüfungsimmanente Lehrveranstaltung

Details

max. 30 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

The class will take place in either seminar room 2H316 or the computer lab 2H363. Please see syllabus for details.

Montag 15.10. 08:00 - 11:00 Seminarraum 1 2H316 UZA II Rotunde
Montag 29.10. 08:00 - 11:00 Seminarraum 1 2H316 UZA II Rotunde
Montag 12.11. 08:00 - 11:00 Seminarraum 1 2H316 UZA II Rotunde
Montag 26.11. 08:00 - 11:00 Seminarraum 1 2H316 UZA II Rotunde
Montag 10.12. 08:00 - 11:00 Seminarraum 1 2H316 UZA II Rotunde
Montag 14.01. 08:00 - 11:00 Seminarraum 1 2H316 UZA II Rotunde
Montag 28.01. 08:00 - 11:00 Seminarraum 1 2H316 UZA II Rotunde

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

It is acknowledged among communication scientists, psychologists, and marketing researchers alike that empirical research focusing on ‘latent variables’ or ‘constructs’ (e.g., customer satisfaction, consumer brand engagement, brand identification) enhances our understanding of complex social/psychological phenomena. This course seeks to familiarize participants with opportunities to test hypotheses about causal relationships between latent variables with empirical data. By doing so, the course gives an introduction to ‘structural equations modelling’ (SEM) using the AMOS program. The discussion will include, amongst others, issues associated with the nature, theoretical specification and graphical representation of SEM models. Following the theoretical background, the course enables its participants to apply the AMOS program to estimate simple as well as more complex models and to evaluate the models’ fit along different criteria. Alternative strategies for model modification and cross-validation are also outlined along with detailed guidelines for setting up and interpreting the relevant input/output files of the AMOS program.

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 in the computer lab. While the first part will introduce students to SEM, the second part will provide some hands-on experiences by conducting a mini research project.

The course is designed for master students and assumes previous knowledge of empirical data analysis and statistics (including regression and correlation analysis). While the course includes some practical tutorials on the AMOS program in a computer lab, it is highly recommended that participants to install AMOS on their own computer (PC only). Go to https://zid.univie.ac.at/software-shop/ for reduced software prices.

Art der Leistungskontrolle und erlaubte Hilfsmittel

Course grading is based on in-class participation, an online quiz and a mini-project which deals with a research project requiring the evaluation of a conceptual model with empirical survey data and the application of AMOS. Further details will be provided in the first session.

Mindestanforderungen und Beurteilungsmaßstab

Students can earn 25% with in-class participation and the online quiz each and additional 50% with the mini-project for the final grading. For successfully passing the course, participants have 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 and on Moodle. Ongoing in-class participation and additional readings are required.

Prüfungsstoff

Required knowledge and practical skills will be conveyed in the workshop sessions and tutorials. In addition, participants are expected to read widely on the subject. Here, participants are required to consult the required basic reading and the additional literature in order to successfully complete the quiz and the mini-project.

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.

The required text for the course is:

Byrne, B. M. (2010): Structural Equation Modeling with AMOS, 2nd edition, Psychology Press.

Student should also read the relevant chapters on SEM in:
Hair, J. F., Black, W. C., Babin, B. J. and Anderson, R. E. (2010): Multivariate Data Analysis, 7th edition, Pearson.

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Fr 22.02.2019 13:28