Бакалавриат
2024/2025
Анализ данных в социологии
Статус:
Курс обязательный (Социология и социальная информатика)
Направление:
39.03.01. Социология
Кто читает:
Департамент социологии
Где читается:
Санкт-Петербургская школа социальных наук
Когда читается:
2-й курс, 3, 4 модуль
Формат изучения:
без онлайн-курса
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
5
Course Syllabus
Abstract
This course lasts for three years. The 1st year aims at beginners. This year starts from introductory topics (variable types, hypothesis testing, descriptive statistics) to working with some methods (chi-square, t-test, nonparametric statistics, one-way ANOVA, and linear regression). The course covers the building blocks of quantitative data analysis with the aim to train students to be informed producers and consumers of quantitative research. The applied part introduces working in R (RStudio) for calculations and reporting.This course is the starting point for social science and humanities students interested in pursuing training in advanced methods of data analysis or planning to use quantitative methods in their own research. The 2nd year aims at intermediate-level students. This year starts from introductory topics (data preparation, visualization, basic statistical tests) to working with more advanced methods of data analysis (interaction effects in linear regression, GLM, factor analysis). The course aims to develop quantitative data analysis skills required to understand and perform independent research. The applied part includes working in R (RStudio). The 3d year aims at upper-intermediate level students.
Learning Objectives
- Develop skills necessary to prepare and present social data. Develop skills necessary to perform data analysis using social data in the R software environment.
Expected Learning Outcomes
- Have skills to write R code for basic data analysis tasks
- Have skills in using R Studio for statistical data analysis
- Be able to apply data analysis tools to real-life problems.
- Able to use R programming language for statistical computations
- Be able to solve the problems of data analysis competitions
Course Contents
- Linear regression
- Introduction to GLM
- Exploratory factor analysis
- Central tendency measures
- Chi-square
- Two means comparison
- One-way ANOVA
- Linear regression
- Preliminary data analysis
- Linear regression with multiple predictors
- Introduction to GLM
- Linear regression: OLS. Diagnostics
- Linear regression: Interaction effects
- Exploratory factor analysis
- Confirmatory factor analysis
- Data quality. Main issues with data
- Resampling
- Missings treatment
- Decision trees
- Cluster analysis
- Advanced regression techniques
- Multilevel regression
Assessment Elements
- Group projects
- Final exam (blocking)
- Mid-term test
- In-class participation and practice
Interim Assessment
- 2024/2025 4th module0.3 * Final exam (blocking) + 0.3 * Group projects + 0.2 * In-class participation and practice + 0.2 * Mid-term test
- 2025/2026 4th module0.4 * Group projects + 0.2 * In-class participation and practice + 0.4 * Mid-term test
- 2026/2027 3rd module1 * Mid-term test
Bibliography
Recommended Core Bibliography
- Chatterjee, S., Hadi, A. S., & Ebooks Corporation. (2012). Regression Analysis by Example (Vol. Fifth edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=959808
- Discovering statistics using R, Field, A., 2012
- Upton, G. J. G. (2016). Categorical Data Analysis by Example. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1402878
Recommended Additional Bibliography
- Applied regression analysis and generalized linear models, Fox, J., 2008
- Regression analysis of count data, Cameron, A. C., 2013