Syllabus

Title
1677 Quantitative and Qualitative Methods II
Instructors
Univ.Prof. Dr. Thomas Plümper
Contact details
Type
PI
Weekly hours
4
Language of instruction
Englisch
Registration
09/17/19 to 10/01/19
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Dates
Day Date Time Room
Wednesday 10/02/19 01:00 PM - 03:00 PM TC.3.12
Thursday 10/03/19 05:00 PM - 07:00 PM TC.3.12
Wednesday 10/09/19 01:00 PM - 03:00 PM TC.3.09
Wednesday 10/16/19 01:00 PM - 03:00 PM TC.3.09
Thursday 10/17/19 05:00 PM - 07:00 PM TC.3.09
Wednesday 10/23/19 01:00 PM - 03:00 PM TC.3.09
Thursday 10/24/19 05:00 PM - 07:00 PM TC.3.09
Wednesday 10/30/19 01:00 PM - 03:00 PM TC.3.09
Thursday 10/31/19 05:00 PM - 07:00 PM TC.3.12
Wednesday 11/06/19 01:00 PM - 03:00 PM TC.3.09
Thursday 11/07/19 05:00 PM - 07:00 PM EA.5.044
Wednesday 11/13/19 01:00 PM - 03:00 PM TC.3.09
Thursday 11/14/19 05:00 PM - 07:00 PM TC.3.09
Wednesday 11/20/19 01:00 PM - 03:00 PM TC.3.09
Thursday 11/21/19 05:00 PM - 07:00 PM TC.3.09
Wednesday 11/27/19 01:00 PM - 03:00 PM TC.3.09
Thursday 11/28/19 05:00 PM - 07:00 PM TC.3.09
Wednesday 12/04/19 01:00 PM - 03:00 PM TC.3.09
Thursday 12/05/19 05:00 PM - 07:00 PM TC.3.09
Wednesday 12/11/19 01:00 PM - 03:00 PM TC.3.09
Thursday 12/12/19 05:00 PM - 07:00 PM TC.3.09
Wednesday 12/18/19 01:00 PM - 03:00 PM TC.3.09
Thursday 12/19/19 05:00 PM - 07:00 PM TC.3.09
Wednesday 01/08/20 01:00 PM - 03:00 PM TC.3.09
Thursday 01/09/20 05:00 PM - 07:00 PM TC.3.09
Wednesday 01/15/20 01:00 PM - 03:00 PM TC.3.09
Thursday 01/16/20 05:00 PM - 07:00 PM TC.3.09
Wednesday 01/22/20 01:00 PM - 03:00 PM TC.3.09
Thursday 01/23/20 05:00 PM - 07:00 PM TC.3.09
Wednesday 01/29/20 01:00 PM - 03:00 PM TC.3.09
Thursday 01/30/20 05:00 PM - 07:00 PM TC.3.09
Contents

In this second part of the course, the central focus shifts to research designs for causal inference, model specification, and robustness tests. Discussed techniques include regression discontinuity, matching, instrumental variable models, and experiments. Robustness tests offer one and perhaps the answer to model uncertainty – the un­certainty researchers face which model specification pro­vides the optimal trade-off between simplicity and genera­lity. In multiple di­men­­sions and in a quasi-infinite number of ways in each of these dimensions, a model requires choices to be made – specification choices that, even if well justified, could have plausibly been made dif­ferently.

Learning outcomes

In the second part of the course, students will learn how to use research designs to improve the validity of causal inferences.

Attendance requirements

The course uses standard rules for absence.

Teaching/learning method(s)

The course uses seminar techniques. Increasingly, participants will present own analyses and research ideas.

Assessment

Students are assessed based on a combination of

- participation in course discussions (30 percent)

- presentations (40 percent)

- essays (30 percent).

Recommended previous knowledge and skills

The course requires participation in the first part of the seminar.

Availability of lecturer(s)

Office hours upon request (email and personal conversation).

Last edited: 2019-06-12



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