Data Analysis
A.Y. 2024/2025
Learning objectives
The main objective of the course is to guide the student towards the autonomy of data analysis in the social sciences, showing how quantitative social research techniques are able to answer research questions relating to the relationship between social phenomena. The different theoretical topics will also be addressed operationally, using real data and through the use of IT tools, both generic such as Microsoft Excel and specific to statistical analysis such as Stata.
Expected learning outcomes
The course aims to provide a good knowledge of classical data analysis techniques. At the end of the course, the student will be able to carry out an analysis of real data, starting from the choice of the data source and the choice of the most appropriate analysis techniques, up to an informed interpretation of the results obtained.
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
0. Introduction to the statistical software R
1. Review of descriptive statistics
2. Introductory concepts of probability and random variables: the Bernoulli random variable and the Normal random variable
3. The law of large numbers and the central limit theorem.
4. Statistical inference:
4.1 Point estimation
4.2 Confidence intervals
4.3 Hypothesis testing
5. Bivariate analysis:
5.1 Contingency tables, statistical independence, the chi-square test,
5.2 The correlation index, simple and multiple linear regression
1. Review of descriptive statistics
2. Introductory concepts of probability and random variables: the Bernoulli random variable and the Normal random variable
3. The law of large numbers and the central limit theorem.
4. Statistical inference:
4.1 Point estimation
4.2 Confidence intervals
4.3 Hypothesis testing
5. Bivariate analysis:
5.1 Contingency tables, statistical independence, the chi-square test,
5.2 The correlation index, simple and multiple linear regression
Prerequisites for admission
General knowledge of social research methodology is required (formulation of research questions, formulation and testing of hypotheses, conception of the research design, etc.).
Knowledge of the basic notions of using a PC is required.
Knowledge of the basic elements of descriptive statistics is required: the classification of phenomena, the synthesis of data in frequency tables, the graphic representation of quantitative and qualitative phenomena, the arithmetic mean, the mode, the median, the quantiles, the interquartile difference, the variance and the covariance and correlation.
Knowledge of the basic notions of using a PC is required.
Knowledge of the basic elements of descriptive statistics is required: the classification of phenomena, the synthesis of data in frequency tables, the graphic representation of quantitative and qualitative phenomena, the arithmetic mean, the mode, the median, the quantiles, the interquartile difference, the variance and the covariance and correlation.
Teaching methods
The course consists in frontal lessons, some of a more theoretical nature (taken on the blackboard without the use of slides) followed by examples of data analysis developed through the statistical software R, to introduce the student to the autonomous use of the software itself and to the correct choice of the statistical tools to be applied.
Teaching Resources
Some notes, written by the Professor, regarding the descriptive statistics prerequisites are available on the MyAriel page of the course.
The course will be held in Italian, foreign students may use the English version of the reference book:
Sheldon M. Ross. Introduction to probability and statistics for engineers and scientists, Elsevier.
Unfortunately, there is not the English version of the second book:
Ieva, F., Masci, C., Paganoni, A.M. Laboratorio di Statistica con R, Pearson
but any alternative text in English covering the same topics can be used.
The R code used to carry out the data analyzes will be uploaded by the professor on the MyAriel page of the course.
The course will be held in Italian, foreign students may use the English version of the reference book:
Sheldon M. Ross. Introduction to probability and statistics for engineers and scientists, Elsevier.
Unfortunately, there is not the English version of the second book:
Ieva, F., Masci, C., Paganoni, A.M. Laboratorio di Statistica con R, Pearson
but any alternative text in English covering the same topics can be used.
The R code used to carry out the data analyzes will be uploaded by the professor on the MyAriel page of the course.
Assessment methods and Criteria
The exam is the same for attending and non-attending students.
It is a written exam lasting one hour and consisting of 10 questions: multiple choice questions and exercises to be carried out with the statistical software R, concerning data analysis.
This examination method is aimed at ascertaining the practical skills acquired and the autonomy of judgment in the interpretation of the results, as well as the knowledge of the theoretical foundations supporting the analyzes.
It is a written exam lasting one hour and consisting of 10 questions: multiple choice questions and exercises to be carried out with the statistical software R, concerning data analysis.
This examination method is aimed at ascertaining the practical skills acquired and the autonomy of judgment in the interpretation of the results, as well as the knowledge of the theoretical foundations supporting the analyzes.
Educational website(s)
Professor(s)
Reception:
Wednesday from 9:00 to 12:00
Via Conservatorio, III floor, Room n. 35