BSc Global Development with International Study / Course details

Year of entry: 2024

Course unit details:
Intermediate Statistical Methods

Course unit fact file
Unit code MGDI20251
Credit rating 20
Unit level Level 2
Teaching period(s) Semester 1
Available as a free choice unit? Yes

Overview

This is an intermediate course in quantitative research methods for analysing both economic and social data. It will focus on applied intermediate statistics/econometric methods, and it is designed for students with strong quantitative background. It will cover theory, relevant applications, and practical exercises using statistical software. Emphasis will be placed on the need to understand and appreciate the interaction between theory testing and practical application using real-word data to answer pertinent development questions. Thus, the unit will have strong focus on theory, developing practical experience, and confident use of statistical software (Stata).

Aims

The unit aims to enable students, through development of theoretical 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.

Syllabus

  • Probability distributions
  • Statistical inference (estimation & significance tests)
  • Analysing association between categorical variables
  • Linear regression analysis
  • Multiple regression model building
  • Logistic regression (modelling categorical responses)

Teaching and learning methods

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 via 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.

Knowledge and understanding

  • Explain the theoretical foundation of multiple regression analysis and effectively address common violations of the standard assumptions of the classical regression model.
  • Use modern causal-effect theory to specify, fit both linear and binary logistic regression models, and undertake robust regression analysis.

Intellectual skills

  • 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.

Practical skills

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

Transferable skills and personal qualities

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

Assessment methods

Method Weight
Written exam 50%
Project output (not diss/n) 20%
Set exercise 30%

Feedback methods

Feedback on the assessments via Blackboard within SEED guidelines.

Recommended reading

Agresti A. (2018). Statistical Methods for the Social Sciences, 5th Edition. Pearson Education Ltd.

Soderbom M. & Teal F. (2015). Empirical Development Economics, Routledge.

Stock J.H. & Watson M.W. (2015). Introduction to Econometrics, Updated 3rd ed. Pearson Education

Study hours

Scheduled activity hours
Lectures 20
Practical classes & workshops 10
Tutorials 10
Independent study hours
Independent study 160

Teaching staff

Staff member Role
Lawrence Ado-Kofie Unit coordinator

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