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Апрель

# Data Analysis in Sociology

2020/2021
Учебный год
ENG
Обучение ведется на английском языке
4
Кредиты

#### Автор программы

Тенишева Ксения Алексеевна
Статус:
Курс обязательный
Когда читается:
3-й курс, 3, 4 модуль

### Course Syllabus

#### Abstract

This course lasts for three years. The 2nd year provide an intermediate-advanced statistical analysis for quantitative research in sociology. In the 2nd year, the course covers two main topics: factor analysis and statistical prediction, including linear regression and structural equation modelling. We also discuss key issues in statistical analysis, such as creating indices and identifying causality based on the results of the analysis. The course covers the building blocks of quantitative data analysis with the goal of training students to be informed consumers and producers of quantitative research. This course is also the starting point for students interested in pursuing advanced methods training or planning to use quantitative methods in their own research.

#### Learning Objectives

• develop skills necessary to solve typical problems in analysing social data in R software environment

#### Expected Learning Outcomes

• Conduct statistical analyses in RStudio
• Choose appropriate methods and techniques for certain types of variables and certain aims of the analysis
• Give meaningful interpretation of statistical results: regression coefficients, tables, plots and diagrams (produced in R)
• Perform data transformations
• Represent graphically the results of the statistical analyses

#### Course Contents

• Introduction to GLM
Covariance and correlation. Basic concepts and logics of linear regression and GLM.
• Linear regression: OLS. Diagnostics
OLS estimator of linear regression, interpretation and statistic test of OLS estimators, fitted values and residuals, R-squared, addressing nonlinearity in linear regression framework, standardized coefficients, drawing plots, practice in R.
• Linear regression: Interaction effects
Main and multiplicative effects in regression models. Interaction effects, additive effects. Interpreting results. Choosing best model. Practice in R.
• Exploratory factor analysis
Dimensionality reduction. Manifest and latent variables. Factors, graphical representation of factors. Exploratory factor analysis. Factor scores, factor space, types of rotation. Optimal number of factors. Interpretation of the results. Creating indices based on factor analysis. Practice in R.
• Confirmatory factor analysis
Difference between exploratory and confirmatory factor analyses. Factor structure. Testing your (or somebody else’s) scales. Types of latent variables. Constructing factor model in lavaan package. Calculation of degrees of freedom, minimal number of cases. Non-correlated and correlated latent factors. Interpreting results. Model diagnostics. Cronbach’s alpha. Practice in R.

#### Assessment Elements

After each seminar, students are assigned a practical task which should be completed until Friday, 12 p.m.
• Project1
Project. There are three basic features assessed: correct calculations and correct code (syntax); correct interpretations – students must describe trends properly, assess significance of the results, and predict values of dependent variable correctly; and produce correct graphics, with proper types of plots and formatting applied.
• Exam
• DataCamp
• Project2
There are three basic features assessed: correct calculations and correct code (syntax); correct interpretations – students must describe trends properly, assess significance of the results, and predict values of dependent variable correctly; and produce correct graphics, with proper types of plots and formatting applied.
• Project 3
A project dedicated to the topics of causal modeling (SEM). One week will be given to prepare and submit your paper.

#### Interim Assessment

• Interim assessment (4 module)
0.2 * DataCamp + 0.1 * Exam + 0.15 * Practical tasks + 0.15 * Project 3 + 0.2 * Project1 + 0.2 * Project2