Data Analysis
A.Y. 2023/2024
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.
Lesson period: First trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First trimester
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. SPSS and Excel will also be introduced.
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 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, as well as SPSS and Excel.
Teaching Resources
Alan Agresti and Barbara Finlay (2014), Statistical Methods for the Social Sciences. Pearson, 4th Edition or above. Available in the university library. Chapters; 1, 2, 3, 4, 5, 6, 7, 8 (up to 8.4 included), 9, 10, 11, 12 (up to 12.3 included).
The slides of the course and other teaching material will be provided during the course by the instructor on the Ariel website of the course.
For Stata: the syntax will be provided by the teacher in the classroom and on ARIEL
Useful material to learn Stata (not mandatory):
Ulrich Kohler & Frauke Kreuter (2012). Data analysis using Stata. Stata Press, 3rd edition
Alan Acock (2014). A sweet introduction to Stata. Stata Press. 4th edition
The slides of the course and other teaching material will be provided during the course by the instructor on the Ariel website of the course.
For Stata: the syntax will be provided by the teacher in the classroom and on ARIEL
Useful material to learn Stata (not mandatory):
Ulrich Kohler & Frauke Kreuter (2012). Data analysis using Stata. Stata Press, 3rd edition
Alan Acock (2014). A sweet introduction to Stata. Stata Press. 4th edition
Assessment methods and Criteria
Attending students are required to be present at least 80% of the lessons and will have to do the homework assigned by the teacher according to the methods indicated during the course. Active participation in class and homework will be taken into account for the final assessment. The final exam for attending students includes a short written test in presence consisting of multiple-choice questions and exercises similar to homework. Students are also required to present a short group paper based on an analysis of the data with the Stata software.
Non-attending students can choose whether to take the same exam as attending students or take only a written test on the material assigned in the reference volume. In both cases, students are invited to contact the teacher to communicate the chosen method and to ask any doubts or questions about the course and the final exam.
Non-attending students can choose whether to take the same exam as attending students or take only a written test on the material assigned in the reference volume. In both cases, students are invited to contact the teacher to communicate the chosen method and to ask any doubts or questions about the course and the final exam.
SPS/07 - GENERAL SOCIOLOGY - University credits: 9
Lessons: 60 hours
Professor:
Dotti Sani Giulia Maria
Educational website(s)
Professor(s)