This module is designed to give you a hands-on perspective on the fundamentals of quantitative analysis. We start with the basics of quantitative research design, critically engaging with various approaches used by quantitative analysts. We progress by actively engaging with ‘data management’ (data collection, organization, and measurement), before focusing on data analysis(using data to describe trends and even explain and predict outcomes). You will perform primary data analysis and interpret and visualise the results of key econometrics analyses: multivariate regression, impact at the margins, analysis of outliers. You will learn how to use Stata, a powerful and widely used statistical software tool. Skills acquired in Stata are transferable to other statistical software packages.

Course content

Week 1: Fundamentals of Quantitative Methods

This week will introduce you to the fundamentals of quantitative methods. We will critically assess several topics such as: quantitative analysis, assessing and creating concepts, exploring data, introduction to Stata and Stata basics.

Week 2: Describing and Comparing Data

Continuing with Stata, Week 2 we’ll introduce you to ‘summary statistics’, statistical techniques used to describe and compare large datasets. Topics include essential Stata commands and using graphs and figures to visualize summary statistics and bivariate analyses in useful and meaningful ways.

Week 3: Using Data to Explain and Predict Outcomes

In this final week, you will learn how to use statistics to explain and predict outcomes. This covers an introduction to bivariate and multivariate regression analysis, when to use regression analysis, Stata regression and diagnostic commands and interpreting and graphing regression analysis.

Learning outcomes

Upon completion of this module, students will be able to:

• How to generate and organise new databases as well as how to navigate very large“ off-the-shelf” databases.
• The fundamentals of statistical analysis, from measures of central tendency to probability, to regression.
• How to use a widely-used statistical software programme, Stata.
• How to interpret and visualise the results of statistical analyses.

This short course is for mid-career professionals. Standard entry requirements are a 2:1 degree plus 3 years of relevant work experience. Applicants without a 2:1 or higher degree are welcome to apply and typically require 5+ years of relevant work experience.

Assessment

One written assignment, plus participation in webinars and discussion forums.

Our modules offer high levels of interaction with regular points of assessment and feedback. Each four week module is worth five Master's level academic credits and includes three webinars with a King's lecturer and peer group of global professionals.