Data Analysis

A.Y. 2021/2022
Overall hours
Learning objectives
The objective of the course is to acquire a solid foundation in applied statistical methodology for the social sciences. By the end of the course students will master the basic toolkit of quantitative research both from a theoretical and a practical/applied standpoint.
Expected learning outcomes
Reach proficiency in various types of univariate and bivariate analyses. Understand what it means to make inference in the social sciences and how to do it in different circumstances. Become competent in hypothesis testing with different types of variables. Be able to produce basic statistical analyses of quantitative data independently using Stata. Achieve basic competences in the understanding and production of time series analyses.
Course syllabus and organization

Single session

Lesson period
First trimester
More specific information on the delivery modes of training activities for academic year 2021/2022 will be provided over the coming months, based on the evolution of the public health situation.
Course syllabus
The course 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.
Prerequisites for admission
No previous background in statistics is required to take this course.
Teaching methods
The course includes both lectures and lab sessions. Students are given in-class and take-home assignments and are asked to work in groups and/or individually. Lab sessions include individual exercises with the software Stata.
Teaching Resources
Alan Agresti and Barbara Finlay (2014), Statistical Methods for the Social Sciences. Pearson, 4th Edition
Chapters: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15.

For Stata: syntax will be provided by the professor in ARIEL
Useful (not mandatory) textbooks for learning how to use Stata on your own:
Ulrich Kohler & Frauke Kreuter (2012). Data Analysis Using Stata. Stata Press, 3rd Edition
Alan Acock (2014). A Gentle Introduction to Stata. Stata Press. 4th Edition
Assessment methods and Criteria
Attending students are asked to participate in at least 80% of the classes. They will be evaluated for their participation in class and for doing and handing in homework as instructed in class. The final exam for attendees includes multiple-choice questions and exercises (similar to those assigned for the homework). Attendees will also write a short assignment, in groups or individually, based on the analysis of micro level data using the software Stata. Non-attendees will take a comprehensive final exam on all the material assigned in the textbook.
SPS/07 - GENERAL SOCIOLOGY - University credits: 9
Lessons: 60 hours
Educational website(s)
By appointment
On Microsoft Teams