module specification

Data Analysis module provides an introduction to research methods used in the social sciences and particularly in economics research. The focus of this module is on the use of statistical methods. Students will learn about:

1. To pose simple, clear research questions and structure a research project;

2. Collect, clean and organize data;

3. Describe data using simple statistics and graphs;

4. Regression analysis in cross-sections;

5. Critical interpretation and discussion of results from such analysis.

The main tool that students will learn to use for these purposes is Stata. Module can be adapted to use another software such as R depending on IT lab facilities and python.

In short, you will learn about different types of datasets, how to use the data, different ways to visualize the data. You will also gain skills in research design and how to apply data to answer questions that arise in economic, policy or other social science research. You will learn and gain understanding on how to interpret regression analysis and will be able to discuss and defend your analysis.

The aim of this module is to enable students with no prior exposure to rigorous empirical analysis to learn about data analysis and quantitative methods and their uses and limitations. Students will learn to clean, organize, and visualise and interpret data for the purpose of answering questions that are asked in economic research.

The module operates for 12 weeks during autumn term. Module is organized into weekly lectures (1 hour) and seminars at IT lab (2 hours).

The following is a guide of each week’s topics:

Week 1 – Introduction & Structuring Research (LO1)

Week 2 – Variation & Descriptive Statistics (LO1)

Week 3 – Distributions, LLN & CLT (LO1)

Week 4 – Using Graphs & Tables (LO1)

Week 5 – Experiments, Comparing Groups, Hypotheses (LO1, LO2)

Week 6 – Enhancement week ((LO1, LO1, LO3)

Week 7 – Testing Hypotheses – Applications (LO1, LO2, LO3)

Week 8 – Regression Analysis – Introduction (LO1, LO2, LO3)

Week 9 – Regression Analysis – Inference (LO1, LO2, LO3)

Week 10 – Regression Analysis – Functional Form (LO1, LO2, LO3)

Week 11 – Linking theory and empirics, developing hypotheses (LO1, LO2, LO3)

Week 12 – Review and preparation for coursework (LO1, LO2, LO3

Teaching is structured around a 1-hour lecture and a 2-hour seminar session per week. Lectures will be structured with the focus on data analysis and econometrics theoretical frameworks and present explanatory examples of real-world cases. The objective is to prepare students for independent data analysis and provide the skills to apply data analysis to conduct economic research.

The teaching and learning activities in the seminar sessions will be take place in the IT lab. Students will learn to apply software to read, structure, visualise and analyse data. Software can be adapted based on IT availability – e.g., Stata, R.

Lecturer will encourage teamwork and active participation in the lectures as well the seminars. Participation in debates and speaking in class- will help students build public speaking skill and expressing their own opinion in a concise manner. Lecture notes and other resources will be made available to students on the virtual learning platform (Weblearn).

Teaching, learning, and assessment activities are designed to develop and strengthen analytical skills of students and provide them with a good knowledge base to be able to develop and test hypothesis as well as learn to ask and answer questions in the context of economic research. Students will be provided with specific tasks in weekly seminar and apply this knowledge in their coursework.

Overall, students’ critical and independent thinking skills are developed in this module. Students will be equipped with skills to conduct data description and visualization, test hypothesis, conduct regression analysis and gain basic skills to design empirical research.

Upon completion of the module, students should be able to demonstrate their ability to:

1. Describe the key concepts related to the collection and analysis of quantitative data using appropriate statistical techniques (LO1).

2. Apply and effectively interpret the results of statistical analysis methods in economic research (LO2).

3. Design and conduct an empirical research project using quantitative data (LO3).

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