Universität Wien
Warning! The directory is not yet complete and will be amended until the beginning of the term.

400002 SE SE Methods for Doctoral Candidates (2012S)

Advanced Quantitative Methods: Research Applications

4.00 ECTS (2.00 SWS), SPL 40 - Doktoratsstudium Sozialwissenschaften
Continuous assessment of course work

DI 17.04.2012 11.00-13.00 C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien

MI 18.04.2012 13.00-15.00 C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien

DO 19.04.2012, 11.00-13.00 Uhr PC-Raum
Ort: NIG, Kursraum B, Erdgeschoss, Stiege III, links, Universitätstraße 7, 1010 Wien

MI 02.05.2012 12.00-14.00 C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien

DO 03.05.2012 15.00-17.00 Uhr PC-Raum
Ort NIG, Kursraum A, Erdgeschoss, Stiege I, rechts, Universitätsstraße 7,
1010 Wien

DI 15.05.2012 11.00-13.00 C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien

MI 16.05.2012 11.00-13.00, Sitzungszimmer im 4. Stock, Raum C 424
Universitätsstraße 7, 1010 Wien

MI 16.05.2012 17.30-19.00 Uhr PC-Raum
Ort NIG, Kursraum A, Erdgeschoss, Stiege I, rechts, Universitätsstraße 7,
1010 Wien

MI 30.05.2012 09.00-11.00 C0628A Besprechung SoWi, NIG Universitätsstraße 7/Stg. III/6. Stock, 1010 Wien

DO 31.05. 2012, 11.00-13.00 Uhr PC-Raum
Ort: NIG, Kursraum B, Erdgeschoss, Stiege III, links, Universitätstraße 7, 1010 Wien

Moodle

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).

Details

max. 15 participants
Language: English

Lecturers

Classes

Currently no class schedule is known.

Information

Aims, contents and method of the course

The course, for advanced graduate students, offers a treatment of quantitative methods in social science research, by focusing on regression analysis and beyond. Once we have a mastery of classical regression, we will use that as a base for more advanced techniques, such as simultaneous equation estimation, multinomial logit, panel analysis and time series analysis. Examples from real research will be freely drawn upon in class.

Assessment and permitted materials

The course will require that the student carry out their own data analysis. The emphasis is on learning by doing. Therefore, the student should find a satisfactory data-set to work with for regular homework assignments. Performance on these homework assignments will determine the student's grade. The student should select an econometrics text to read in conjunction with the class, to reinforce the class topics. Guidance will be provided here, appropriate to availability and level of difficulty.

Minimum requirements and assessment criteria

Examination topics

Reading list

Association in the course directory

Last modified: Mo 07.09.2020 15:46