Research Practicum (SPS-RESGO-RES-23)
SPS-RESGO-RES-23
| Department |
SPS |
| Course category |
SPS Methods Seminar |
| Course type |
Seminar |
| Academic year |
2023-2024 |
| Term |
1ST TERM, 3RD TERM |
| Credits |
20 (EUI SPS Department) |
| Professors |
|
| Contact |
Rzemieniecka, Monika
|
| Course materials |
| Sessions |
12/09/2023 12:30-14:00 @ Seminar Room 2, Badia Fiesolana
22/09/2023 14:00-15:30 @ Refectory, Badia Fiesolana
02/10/2023 13:00-15:00 @ Seminar Room 2, Badia Fiesolana
21/03/2024 11:00-13:00 @ Seminar Room 2, Badia Fiesolana
04/04/2024 11:00-13:00 @ Seminar Room 2, Badia Fiesolana
18/04/2024 11:00-13:00 @ Seminar Room 2, Badia Fiesolana
02/05/2024 11:00-13:00 @ Seminar Room 2, Badia Fiesolana
09/05/2024 11:00-13:00 @ Seminar Room 2, Badia Fiesolana
16/05/2024 11:00-13:00 @ Seminar Room 2, Badia Fiesolana
30/05/2024 11:00-13:00 @ Seminar Room 2, Badia Fiesolana
|
Purpose
Course Description: This course walks you through all the steps involved in a complex collaborative reproducible research project, from thinking up a problem worth studying, to pre-registering your design, to collecting and analyzing your data, and writing up and posting your results. You will practice these steps using real data collected by the instructor. To master the skills involved, you will work in teams and hand in bi-weekly homework assignments.
Course Prerequisites: You will only be able to do the work in this course if you are familiar with statistical methods to analyze quantitative data. Thus, students are welcome in the course if they have taken at least one prior course in statistics (covering material through multiple regression) that used either Stata or R. All other skills required this term will be taught by the instructor.
Description
Course Objectives: At the completion of this course, you will:
1. Have experience working with a complex multilevel dataset.
2. Have practiced many activities required to complete a large-scale reproducible research project.
3. Have experience collaborating with others on a research project.
4. Have worked with tools such as LATEX, RMarkdown, and GitHub, and have developed familiarity with sites such as BITSS, OSF, EGAP, Dataverse, and others.
5. Have improved research practices and skills.
6. Be knowledgeable about the highest standards and practices associated with many aspects of reproducible research.
7. Have gained an overview of the entire research process that will give you a more realistic and complete idea of the timeframe, intellectual commitment, and skills involved in real research.
Register for this course
Page last updated on 05 September 2023