Further Statistical Methods for Global Development

MGDI30801 Further Statistical Methods for Global Development 20 Semester 1 Undergraduate Level 3 Lawrence Ado-Kofie Unit coordinator Global Development Institute 1 FHEQ level (Framework for Higher Education Qualifications) ' Last part of a Bachelors ' European Credit Transfer & Accumulation System Rating : 10.0 This course unit builds on the Second-Year Intermediate Statistical Methods course [MGDI20251] and develops advanced concepts and skills in quantitative analysis of economic and social data. It emphasises applied statistical/econometrics techniques, focusing on multiple regression, model building, and addressing violations of classical assumptions. Students will gain practical experience using real-world datasets and statistical software (Stata) to conduct empirical research and answer economic/social development-related questions. This course unit builds on the Second-Year Intermediate Statistical Methods course [MGDI20251] and develops advanced concepts and skills in quantitative analysis of economic and social data. It emphasises applied statistical/econometrics techniques, focusing on multiple regression, model building, and addressing violations of classical assumptions. Students will gain practical experience using real-world datasets and statistical software (Stata) to conduct empirical research and answer economic/social development-related questions. The unit aims to:Enable students through development of conceptual insights and practical skills to become:<ol><li>Critical and competent users of statistical methods in applied development studies and research.</li><li>Able and critical readers of academic and policy articles with quantitative empirical content. </li></ol> The course unit develops students’ statistical and econometric competencies, enabling them to work effectively in professional environments that rely on quantitative research and data analysis. These skills are highly relevant for careers in financial services, research and academic institutions, government agencies, international organisations such as UN bodies, and non‑governmental organisations. <ul><li>Apply advanced multiple regression techniques to economic and social data.</li><li>Diagnose and address common violations of classical regression assumptions.</li><li>Select appropriate econometric models for different research contexts.</li></ul> <ul><li>Assess model fit and validity using diagnostic tests and robustness checks.</li><li>Employ concepts in statistics/econometrics to analyse real-word data, ascertain findings, and provide tailored suggestions and recommendations.</li><li>Communicate quantitative research findings effectively in written and graphical formats.</li></ul> <ul><li>Collect secondary data from online databases: International Statistics & Data, such as World Data Bank - WDI, UN data, WHO data, World Economic Outlook database, etc. </li><li>Use Stata software package to manage data, estimate models, and perform hypothesis testing.  </li><li>Undertake diagnostic tests, analyse and interpret regression results.  </li></ul> <ul><li>Design and implement quantitative research to answer development-related questions.</li><li>Interpret and critically evaluate empirical research finding.</li><li>Critically appraise the use of statistical/econometric methods in published research.  </li></ul> Syllabus (indicative curriculum content):<ul><li>Multivariate relationships.</li><li>Multiple regression & Correlation.</li><li>Regression with Categorical Predictors: Analysis of Variance (ANOVA) Method.</li><li>Regression with Quantitative & Categorical Predictors.</li><li>Model Building with Multiple regression.</li><li>Logistic Regression: Modeling Categorical Responses.</li><li>An Empirical Research Project through which students demonstrate the concepts and skills acquired in the course.  </li></ul> The course unit employs a blended approach to teaching and learning, drawing on lectures, tutorials, and STATA‑based computer laboratory sessions, supported by independent study using Canvas e‑learning materials and the recommended texts.Lectures (2 hours) are interactive and encourage active student participation through questioning and discussion. To maximise learning, students are required to engage with the lecture slides and online resources on Canvas prior to attending lectures, tutorials, and STATA lab sessions.   0 Other 100% Individual Applied Empirical Research Project (4000 words)  The purpose of the research project is to evaluate the knowledge and skills acquired in the module by conducting an independent applied quantitative study that investigates the causal effects of key development economics variables.Students will apply appropriate empirical methods to address pertinent research questions in development economics and demonstrate competency in designing, implementing, and interpreting a rigorous causal analysis, and findings.   Written comments via Canvas within SEED's guidelines. MGDI20251 Intermediate Statistical Methods Pre-Requisite Compulsory MGDI 20251 Pre-requisite N 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.    Blaikie Norman (2003). Analysing Quantitative Data From Description to Explanation. Sage Publication Ltd., London   Lectures 20 Practical classes & workshops 10 Tutorials 10 0 160 The unit covers research topics and case studies related and applicable to BAME, all race, and sex. The delivery/teaching of the course unit is inclusive in the following ways:  a.    Unit content will be made accessible through Canvas.  b.    Lectures will be recorded so students can follow at their own pace through Canvas.  c.    Core e-textbook and all recommended materials will be embedded in Canvas. Individual Stata licenses will be issued to students to install on their laptops for 24/7 accessibility without the need to be in the University Computer Cluster for practice and completion of assignments.