Data Analysis

The "Data Analysis" module aims at providing students with a solid foundation in applied statistical methodology. Students who attend and successfully complete the course will master the basic toolkit of quantitative research (i.e. cases, types of variables, datasets, hypotheses testing); will achieve an understanding of why sampling is used, different sampling methods and how to make predictions (inference) in the social sciences; they will be proficient with the main tools for univariate and bivariate analyses. Students will also receive basic training for the use of the statistical software Stata and, by the end of the course, they will be able to produce basic statistical analyses of quantitative data independently.
The topic covered are: Introduction, variables and samples; Descriptive statistics, Introduction to Stata, setting up the workspace, descriptive statistics, Probabilities and distributions, Generating and modifying variables in Stata, Inference and estimation, Significance tests; Point and interval estimation with Stata; Comparing two groups and associations between categorical variables, Cross-tabulation in Stata, Linear regression and correlation, ANOVA, Linear regression and ANOVA in Stata, Introduction to logistic regression and to multivariate relationships; Setting up and executing a quantitative research analysis in Stata.

The goal of the "Introduction to R" module is to provide students with a working knowledge of the software R for statistical data analysis and reporting. Students who complete the course will be shown how to use R for a variety of operations, including: data exploration and management, basic and advanced statistical operations (frequencies, hypothesis testing, linear regression), data visualization, and development of data products such as reports and slides.