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Regular version of the site

Data Analysis in Sociology

2023/2024
Academic Year
ENG
Instruction in English
4
ECTS credits
Course type:
Compulsory course
When:
2 year, 3, 4 module

Instructors


Tsigeman, Elina

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

Learning Objectives

  • develop skills necessary to solve typical data analysis problems on social data in the R software environment
Expected Learning Outcomes

Expected Learning Outcomes

  • Choose appropriate methods and techniques for certain types of variables and certain aims of the analysis
  • Conduct statistical analyses in RStudio
  • Create analytical reports describing all the stages of analysis and interpreting its results
  • 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

Course Contents

  • Central tendency measures
  • Chi-square
  • Two means comparison
  • One-way ANOVA
  • Linear regression
  • Linear regression with multiple predictors
  • Data quality. Main issues with data
  • Missings treatment
  • Decision trees
  • Cluster analysis
  • Advanced regression techniques
  • Multilevel regression
  • 3rd year topics
  • 4th year topics
Assessment Elements

Assessment Elements

  • non-blocking In-class participation and practice
  • non-blocking Mid-term test
  • blocking Final exam (blocking)
  • non-blocking Group projects
Interim Assessment

Interim Assessment

  • 2023/2024 4th module
    0.3 * Final exam (blocking) + 0.3 * Final exam (blocking) + 0.3 * Group projects + 0.3 * Group projects + 0.2 * In-class participation and practice + 0.2 * In-class participation and practice + 0.2 * Mid-term test + 0.2 * Mid-term test
  • 2024/2025 4th module
    1 * 2023/2024 4th module + 1 * 2023/2024 4th module
  • 2025/2026 3rd module
    1 * 2023/2024 4th module + 1 * 2023/2024 4th module
Bibliography

Bibliography

Recommended Core Bibliography

  • 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
  • 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

  • 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
  • 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, Volume 1 : Foundations. Oxford University Press.
  • Little, T. D. (2013). The Oxford Handbook of Quantitative Methods. Oxford University Press.