This module aims to equip students with expert skills to compile and analyse complex quantitative data, and apply it to their own research project.

(LO1) Students will be able to use Stata to prepare raw data for analysis.

(LO2) Students will be able to make sense of data and gain preliminary understanding of its characteristics.

(LO3) Students will be able to write codes and develop user-written commands in Stata to improve operational efficiency.

(LO4) Students will be able to evaluate, effectively communicate, and identify ways to improve the efficiency and effectiveness of empirical design choices.

(S1) Leadership
Students develop this skill by leading on a mini research project.

(S2) Numeracy
Students develop this skill by systematically organise data and information and analyse them using statistical software (STATA).

(S3) Organised and able to work under pressure
Students develop these skills by balancing the time and resources to study and to complete the coursework and prepare for the presentation.

(S4) Digital skills
Students develop this skill by conducting statistical analysis using statistical software (STATA).

(S5) Ethical awareness
Students develop these skills by transparently presenting how they code using statistical software, and how they equally report significant and not significant results.

(S6) Communication skills
Students will have opportunities to develop written and oral communication skills through group discussions, in-class presentations and coursework. This will be assessed by written assignment and an individual presentation.

(S7) International awareness
International issues will be covered in class work.

(S8) Lifelong learning (critical thinking, problem solving and decision-making skills)
Students will be challenged to think critically about methodological issues and dilemmas. They will do this by gathering and synthesising information, analysing alternative perspectives and options and presenting a considered opinion or course of action in their course assessment.

6 x 3-hour lectures (18 hours)
6 x 2-hour seminars (12 hours)
1 x 2-hour presentation session (2 hours)
168 hours self-directed learning

Session 1 Stata Basics:
Stata’s interface;
Menus and dialogs;
Getting help;
Stata learning resources;
Miscellaneous topics.

Session 2 Data Management:
Reading, importing, saving, and exporting data files;
Creating variables, labelling and combining datasets;
Processing observations across subgroups;
Managing the shape of datasets.

Session 3: Programming basics: Macros, arguments, and looping:
Entering and executing a program;
Macro names and program arguments;
Branching and looping.

Session 4: Advanced topics in programming:
Handling time and date variables;
Confirming assumptions;
Returned values and storing results;
Bootstrapping and Monte Carlo simulations.

Session 5: Ado-files:
Do-files, programs, and ado-files: differences;
Temporary variables, temporarily destroying data, and Temporary files;
An analysis-specific ado-file and General-purpose (GP) ado-files;
Stata synt ax and Version control.

Session 6: Stata and applied econometrics:
Introduction to Monte Carlo Simulation;
Additional examples.

Session 7: Causal Inference:
Conceptual foundations;
Practical examples on the challenges of causal inference from observational data.

Session 8: Matching Methods:
Causal Inference with Conditional Independence Assumption (CIA);
Propensity Score Matching (PSM);
Coarsened Exact Matching (CEM).

Session 9: Quasi Experimental Designs:
Instrumental Variables;
Natural Experiment (DiD) ;
Regression Discontinuity Designs.

Session 10: Communicate empirical designs convincingly:
Research design transparency;
Crucial information and credible result communication;
Reasoning for design choices.