| Course information | |
|---|---|
| Period | Block 1 |
| Timeline | November |
| Number of ECTS | 2,5 ECTS |
| Coordinator | Dr Paula Nagler |
| Methodology | Lectures, plenary sessions, Q&A sessions, in-class/online workshops, exercises, self-study |
Course description
An increasing number of quantitative data sources covering urban areas at different scales has become available in recent years. This trend requires urban professionals to have the knowledge to understand such data and make sense out of them. This course introduces students to quantitative data analysis with a focus on understanding and assessing quantitative results. The course starts by introducing students to probability, statistical estimation, and hypothesis testing to explain how distributions in random samples behave and how likely observed results are when based on representative survey data. Hypothesis testing provides the basis for regression analysis. The course then proceeds to ordinary-leastsquares (OLS), a method used to estimate linear regression models, starting with simple regressions and progressing to multiple regression models. The course concludes with an overview of regression validity and a brief outlook on policy implications. The aim of this course is to equip students with a basic toolkit for understanding quantitative results in support of evidence-based policymaking. Emphasis is placed on interpreting results and translating quantitative research outcomes into policy-relevant conclusions.
Learning objectives
After completing the course, students are able to:
- Explain how random sampling informs hypothesis testing.
- Interpret results from ordinary least squares (OLS) regression models.
- Evaluate quantitative data outputs for evidence-based policymaking.