Анализ данных в социальных науках

2024/2025

Статус: Маго-лего

Кто читает: Департамент социологии

Когда читается: 2, 3 модуль

Охват аудитории: для своего кампуса

Преподаватели: Пашков Станислав Георгиевич

Язык: английский

Кредиты: 6

Full Syllabus Ask Question

Abstract

The goal of this course is to introduce students the main principles of data analysis methods and procedures commonly used in social science, with an accent on social problems and corresponding scientific challenges. Students are to familiarize with a variety of data analysis methods, which should be useful in quantitative research. It is aimed at developing a data-driven, as well as theory-driven logic through understanding data fundamentals, main application areas. Students will be able to understand limitations, values added and heuristic mechanisms of different data analysis methods. During the course, students will acquire practical skills to be able to gather, generate, visualize and analyze quantitative data in social science research. All the learning process is based on R language and RStudio.

Learning Objectives

The main goal of the course is to familiarize students with a variety of data analysis methods which should be useful in quantitative research. The course is aimed at developing a datadriven mentality through understanding data fundamentals, as well as areas of application for different analytical methods and approaches. Students should be able to understand the limitations, value added and heuristic mechanisms of different data analysis methods.

Expected Learning Outcomes

A student orients he/she-self in variety of conceptual approaches to the phenomena of social memory, its connection with the problematics of values and identity, its dependence on the social and political changes
Can do statistical analysis in R
Be able to write a statistical analysis report
A student knows the main approaches in the field and can use the main methods in political science
Students are able to use basic methods of computational text mining.
Able to solve professional problems based on synthesis and analysis
Advance the skills of working with social and economic databases applicable to the student’s own research
Able to demonstrate knowledge on the principles for data visualization
Be familiar with contemporary text mining models and approaches
Get knowledge about how to use computer modeling in social and political sciences research and to apply agent-based modeling for data analysis.
Practice skills inprogramming with R for a research proposal.

Course Contents

SESSION 1: Introduction to Research Design and Research Methodology of Quantitative Study
SESSION 2: Macro- and Micro-Data. Open-source Datasets. Questionnaire design
SESSION 3: Dataset Description and Data visualization
SESSION 4: Statistical Analysis and principles of Descriptive Statistics
SESSION 5: Exploring Distributions and Statistical Inference
SESSION 7: Statistical Relationship. Causation
SESSION 6: Statistical Relationship. Correlation
SESSION 8: Linear Regression Models
SESSION 9: Nonlinear Regression Models
SESSION 10: Factor and Cluster Analysis
SESSION 11: Qualitative-Quantitative analysis of Textual Data using R (Appendix)

Assessment Elements

Laboratory assessment No. 3: Regression modeling
The laboratory work covers the material of sessions 7-9 and is a variant of a practical task in which the student needs to give a detailed answer or demonstrate writing code in R and interpreting (visualizing) the data received. As part of the laboratory work, the student is given a country and a set of variables that the student needs to process, build the appropriate statistical measurements, and interpret the process and results. The work is done individually.
Final project
Laboratory assessment No. 1: Descriptive statistics
The laboratory work covers the material of sessions 3-5 and is a variant of a practical task in which the student needs to give a detailed answer or demonstrate writing code in R and interpreting (visualizing) the data received. As part of the laboratory work, the student is given a country and a set of variables that the student needs to process, build the appropriate statistical measurements, and interpret the process and results. The work is done individually.
Class attendance
Laboratory assessment No. 2: Factor/Cluster Analysis
The laboratory work covers the material of sessions 6 & 10 and is a variant of a practical task in which the student needs to give a detailed answer or demonstrate writing code in R and interpreting (visualizing) the data received. As part of the laboratory work, the student is given a country and a set of variables that the student needs to process, build the appropriate statistical measurements, and interpret the process and results. The work is done individually.

Interim Assessment

2024/2025 3rd module
0.1 * Class attendance + 0.3 * Final project + 0.15 * Laboratory assessment No. 1: Descriptive statistics + 0.2 * Laboratory assessment No. 2: Factor/Cluster Analysis + 0.25 * Laboratory assessment No. 3: Regression modeling

Bibliography

Recommended Core Bibliography

C.R. Rao. (2005). Data Mining and Data Visualization: Vol. 1st ed. North Holland.
Churcher, C. (2012). Beginning Database Design : From Novice to Professional (Vol. 2nd ed). New York: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1173722
Creswell, J. W. . (DE-588)133331512, (DE-576)164944168. (1994). Research design : qualitative and quantitative approaches / John W. Creswell. Thousand Oaks, Calif. [u.a.]: Sage. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.040749258

Recommended Additional Bibliography

King, G., Keohane, R. O., & Verba, S. (1995). The Importance of Research Design in Political Science. American Political Science Review, (02), 475. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.cup.apsrev.v89y1995i02p475.481.09
Munzert, S. (2014). Automated Data Collection with R : A Practical Guide to Web Scraping and Text Mining. HobokenChichester, West Sussex, United Kingdom: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=878670
Rajaraman, A., & Ullman, J. D. (2012). Mining of Massive Datasets. New York, N.Y.: Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=408850

Authors

Desiatova Mariia Ivanovna
KRUCHINSKAIA EKATERINA VLADISLAVOVNA
PASHKOV STANISLAV GEORGIEVICH

Course Syllabus