Doing Research (2): Quantitative Methods

Course goals

The objective of this course is for students to acquire a working knowledge of quantitative methods, summarized in the following learning objectives:
 
•          Students have an understanding of essential statistical concepts such as probability distributions, bivariate and multivariate analysis, and statistical testing.
•          Students understand the linear (bivariate and multivariate) regression model, including the ordinary least squares estimator and its statistical properties, functional form and model misspecification, and testing hypotheses.
•          Students have coding proficiency in the software package STATA for data analysis. This includes performing simple data processing tasks to obtain a dataset suitable for empirical analysis.
•          Students can recognize and implement the different stages of a quantitative research project; and produce, interpret, and assess empirical research output.
•          Students can answer research questions by applying the aforementioned knowledge.
•          Students have strengthened their interdisciplinary skills (Common Ground, Perspective Taking, and / or Integrating Perspectives)
 

Content

For the lectures and tutorials, the course approximately contains the following week-by-week material:
 
Week 1: Aim of the course; Key statistical concepts; Inference: Causality and empirical identification using quantitative methods
Week 2: Mechanics of the OLS regression model: Bivariate and multivariate OLS estimation (mechanics of least-squares, fitted values, residuals, R2, standard errors), including OLS assumptions and statistical properties
Week 3: Hypothesis testing (t-test and F-test) for OLS estimates
Week 4: Model specification and functional form (logs vs levels, dummies for ordinal and categorical variables, quadratic terms)
Week 5: Omitted variable bias; Presentation of research project (15% of total grade)
Week 6: Heteroskedasticity; Multicollinearity
Week 7: The linear probability model (OLS with a dummy dependent variable); Causality revisited (including examples on the use of experiments & natural experiments); Course summary; Deadline to hand in research project paper (30% of total grade)
Week 8: Final exam (55% of total grade, with at least a grade of 5.0)
 
In the computer lab sessions, students will receive an introduction to STATA in the first week of the course, and from week 2 onwards work in small groups with a provided dataset of their choice to answer a research question under the guidance of an instructor. Each week, students will incorporate the newly learned material into their paper. Students will present their preliminary findings in week 5 (15% of the final grade) and arrive at the final specification to answer their research question in week 6. They will then produce a short research report on that specification (30% of the final grade). In the report, the STATA code used to arrive at the the final model specification is included as an appendix for scientific replicability. All reports are screened using plagiarism-detection software.