Data Analysis in Sociology

Bachelor 2018/2019

Type: Compulsory course (Sociology and Social Informatics)

Area of studies: Sociology

Delivered by: Department of Sociology

Where: Saint-Petersburg School of Social Sciences

When: 3 year, 3, 4 module

Mode of studies: offline

Instructors: Ksenia Tenisheva, Vera Titkova

Language: English

ECTS credits: 4

Contact hours: 60

Full 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

Practical tasks
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
Экзамен проводится в письменной форме. Экзамен проводится на платформе MsTeams: через модуль "Задания" всем рассылается задание экзамена; выполненную работу следует прикрепить также в модуле "Задания" MsTeams. В случае сбоя в работе MsTeams, студент также может направить выполненную работу на корпоративную почту преподавателя со своей корпоративной почты. На выполнение экзамена выделяется 2 дня. Вы можете начать в любое время, но рассчитайте свои силы и возможности так, чтобы уложиться до дедлайна. Компьютер студента должен удовлетворять требованиям: подключение к интернету, предустановленный RStudio одной из последних версий. Во время экзамена студентам запрещено кооперироваться и коллективно выполнять задание. Во время экзамена студентам разрешено пользоваться любыми источниками - учебниками, интернетом. Долговременным нарушением связи во время экзамена считается отсутствие интернета в течение всего времени экзамена/ отсутствие доступа к компьютеру в течение всего срока экзамена. При долговременном нарушении связи студент не может продолжить участие в экзамене. Процедура пересдачи аналогична процедуре сдачи. О проблемах со связью или доступом к компьютеру студент должен сообщить преподавателю незамедлительно (как только появится такая возможность). При своевременном сообщении о проблеме каждый случай технических неполадок будет рассматриваться отдельно, решение о возможности и форме прохождения экзамена будет выноситься индивидуально.
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

Bibliography

Recommended Core Bibliography

Agresti, A. (2013). Categorical Data Analysis (Vol. Third edition). Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=769330
Agresti, A., & Finlay, B. (2014). Statistical Methods for the Social Sciences: Pearson New International Edition (Vol. Pearson new international ed., 4. ed). Harlow England: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1418314
Denis, D. J. (2016). Applied Univariate, Bivariate, and Multivariate Statistics. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1091881
Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling, Fourth Edition (Vol. Fourth edition). New York: The Guilford Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1078917
Stowell, S. (2014). Using R for Statistics. Berkeley, CA: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1174344
Tabachnick, B. G., & Fidell, L. S. (2014). Using Multivariate Statistics: Pearson New International Edition (Vol. 6th ed). Harlow, Essex: Pearson. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1418064

Recommended Additional Bibliography

Beh, E. J., & Lombardo, R. (2014). Correspondence Analysis : Theory, Practice and New Strategies. Chichester, West Sussex: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=842814
Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research, Second Edition (Vol. Second edition). New York: The Guilford Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=831411
Crawley, M. J. (2013). The R Book (Vol. Second Edition). Chichester, West Sussex: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=531630
Little, T. D. (2013). The Oxford Handbook of Quantitative Methods. Oxford: Oxford University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=603942

Course Syllabus