Data Lab
A.Y. 2021/2022
Learning objectives
This course aims to provide students with a specific understanding of different technics of data analysis, allowing to analyze various issues related to social and political sciences. In particular, lessons will deal with useful conceptual, analytical and technical tools for assessing data on the topics related to global societal issues.
Expected learning outcomes
By the end of the course, students will be able to understand and interpret scientific contributions based on variety of data, considering both their potentials and limitations. Students will have knowledge about the main software packages for conducting research.
Lesson period: Third trimester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Lesson period
Third trimester
Course syllabus
Lab 1.
1. Quick revision of research design steps (topic choice, literature review, hypothesis formulation, methodology, data analysis)
2. Quick revision of quantitative analysis (sampling, types of variables)
3. Introduction into Stata and other software packages.
4. Introduction into the database (European Social Survey) to be used.
Lab 2.
Descriptive statistics (frequency distributions, data variability, bivariate analysis) in Stata
Probability distributions and Central limit theorem
Introduction to inferential statistics and significance tests in Stata.
Lab 3.
Introduction to multivariate analysis.
Ordinary least squares regression model.
Simple linear regression model.
Lab 4.
Multiple linear regression model
Labs 5.
Binomial logistic regression.
Labs 6-7.
Multinomial logistic regression
Labs 8-9.
Differences between cross-sectional and panel data.
Introduction into random, fixed-effects and mixed-effects models.
Lab 10.
Presentations and discussion of the projects
1. Quick revision of research design steps (topic choice, literature review, hypothesis formulation, methodology, data analysis)
2. Quick revision of quantitative analysis (sampling, types of variables)
3. Introduction into Stata and other software packages.
4. Introduction into the database (European Social Survey) to be used.
Lab 2.
Descriptive statistics (frequency distributions, data variability, bivariate analysis) in Stata
Probability distributions and Central limit theorem
Introduction to inferential statistics and significance tests in Stata.
Lab 3.
Introduction to multivariate analysis.
Ordinary least squares regression model.
Simple linear regression model.
Lab 4.
Multiple linear regression model
Labs 5.
Binomial logistic regression.
Labs 6-7.
Multinomial logistic regression
Labs 8-9.
Differences between cross-sectional and panel data.
Introduction into random, fixed-effects and mixed-effects models.
Lab 10.
Presentations and discussion of the projects
Prerequisites for admission
Basic knowledge of mathematics
9 cfu from previous courses in Statistics
9 cfu from previous courses in Statistics
Teaching methods
A mix of frontal lessons and lab work
Teaching Resources
Agresti, A. Statistical Methods for the Social Sciences, fifth edition, Pearson Global Edition, 2018. (Chapters 1 - 6, 8 - 12, 14)
Treiman, Donald J. Quantitative data analysis: Doing social research to test ideas. John Wiley & Sons, 2014. (Chapters 1 - 3, 5 - 7, 9 - 10, 14-15)
StataCorp. 2021. Stata: Release 17. Statistical Software. College Station, TX: StataCorp LLC.
Treiman, Donald J. Quantitative data analysis: Doing social research to test ideas. John Wiley & Sons, 2014. (Chapters 1 - 3, 5 - 7, 9 - 10, 14-15)
StataCorp. 2021. Stata: Release 17. Statistical Software. College Station, TX: StataCorp LLC.
Assessment methods and Criteria
Group project with a presentation during the last class for attending students.
Written exam for non-attending students.
Written exam for non-attending students.
SPS/04 - POLITICAL SCIENCE - University credits: 3
Laboratories: 20 hours
Professor:
Dormidontova Yulia