BSc Global Development

This is an intermediate course in quantitative research methods for analysing both economic and social data. It focuses on applied statistics/econometric methods: how methods are used and interpreted rather than their theoretical derivations, and it is designed for students with strong quantitative background. It covers concepts, relevant applications in social science research, and practical exercises using statistical software. Emphasis is placed on understanding concepts and applications, estimation, testing, model building, and practical application using real-word data to answer pertinent development questions. Thus, the unit has strong focus on concepts, developing practical experience, and confidence use of statistical software (Stata).

The unit aims to enable students, through development of conceptual insights and practical skills to become:

• Critical and competent users of statistics in applied development studies and research.
• Able and critical readers of academic and policy articles with an empirical content.

1. Probability distributions
2. Statistical inference (estimation & significance tests) 
3. Analysing association between categorical variables
4. Linear Regression and Correlation
5. Linear Multiple regression analysis
6. Multiple regression model 

The course unit will draw on a range of teaching and learning strategies; lectures, tutorials, computer based lab sessions (STATA) and independent learning by students using e-learning materials provided on the Blackboard and the recommended textbooks. During the 2-hour lecture sessions, student participation will be encouraged and welcomed through asking and answering questions. Students will be expected to have gone through teaching slides and e-learning materials provided in Blackboard before lectures/tutorials/Stata lab sessions.

• Undertake statistical inference: estimation and significance tests.
• Explain the conceptual foundation of multiple regression analysis.
• Use modern causal-effect theory to specify, fit regression models, and undertake robust regression analysis.

• Apply concepts in statistics/econometrics and critique statistical results presented in published articles and journals.
• Employ concepts in statistics/econometrics to analyse real-word data.

• Produce empirical work, using alternative forms of regression analysis.
• Analyse and interpret regression results.

• Use Stata software package for conducting multivariate regression analysis, undertake diagnostic tests, and interpret quantitative results.
• Examine published empirical work with statistical/econometric contents, and make analytical judgement.

Assessment 1: Mid-term examination (1 Hour) — 30%

Assessment 2: End of semester examination (2 Hours) — 70%

Feedback on the assessments via Blackboard within SEED guidelines.

Agresti A. (2024). Statistical Methods for the Social Sciences, 6th Edition. Pearson Education Ltd.
Soderbom M. & Teal F. (2015). Empirical Development Economics, Routledge.
Stock J.H. & Watson M.W. (2020). Introduction to Econometrics, Updated 4th ed. Pearson Education.