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Regular version of the site
Master 2021/2022

Methodology and Research Methods in Sociology: Quantitative Research Methods

Category 'Best Course for Career Development'
Type: Compulsory course (Comparative Social Research)
Area of studies: Sociology
Delivered by: School of Sociology
When: 1 year, 2 module
Mode of studies: offline
Open to: students of one campus
Instructors: Anna Almakaeva
Master’s programme: Comparative Soсial Research
Language: English
ECTS credits: 6
Contact hours: 52

Course Syllabus

Abstract

The course aims to provide students with understanding of key concepts and methods of modern statistical data analysis. It gives an overview and practice of the skills necessary for conducting independent research with quantitative survey data, using R software. The course also puts these skills into the broader academic context by reviewing how statistics are used in published scientific journal articles.
Learning Objectives

Learning Objectives

  • Provide students with understanding of key concepts and methods of modern statistical data analysis
Expected Learning Outcomes

Expected Learning Outcomes

  • Have skills in interpreting results and writing research papers
  • Have skills in the evaluation of the quality of published research papers
  • Have skills in using R Studio for statistical data analysis
  • know the key concepts of statistics
  • know the main methods and techniques of statistical data analysis
  • know the main procedures of data transformation and data analysis using R
  • to be able to analyze secondary data at the level required for an MA thesis
  • to be able to choose correct statistical methods and procedures according to the research questions
  • to be able to interpret and present the results of data analysis in oral and written form
Course Contents

Course Contents

  • Statistical inference in social sciences
  • T-tests, One-way ANOVA
  • Non-parametric analogues of T-tests and one-way ANOVA
  • Pearson's chi-square for contingency tables
  • Correlations
  • Linear regression
  • Regression diagnostics: heteroscedasticity, multicollinearity, non-linearity
  • Categorical independent variables in linear regression and interaction effects
  • Binary logistic regression
Assessment Elements

Assessment Elements

  • non-blocking Reading published research
  • non-blocking Essay
  • non-blocking Home assignment
    Grading criteria: 1) The correct method of data analysis. 2) Correct R function. 3) Correct interpretation.
  • non-blocking Exam
    The task of the first reexam is similar to the first exam. Students will have 3 hours to do it. The second reexam is similar to the first one. The weight of the reexam in the final grade is 0.3.
Interim Assessment

Interim Assessment

  • 2021/2022 2nd module
    0.35 * Essay + 0.2 * Home assignment + 0.35 * Exam + 0.1 * Reading published research
Bibliography

Bibliography

Recommended Core Bibliography

  • Handbook of univariate and multivariate data analysis and interpretation whith SPSS, Ho, R., 2006

Recommended Additional Bibliography

  • Cramer D. Advanced Quantitative Data Analysis. 2003.
  • Gelman, A. B., & Hill, J. (2015). Data analysis using regression and multilevel/hierarchical models. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.4E4FBAE7
  • Mirkin, B. Core concepts in data analysis: summarization, correlation and visualization. – Springer Science & Business Media, 2011. – 388 pp.
  • 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
  • Wickham H. ggplot2: elegant graphics for data analysis. Second edition. Cham: Springer, 2016. 260 p.
  • Willem Mertens, Amedeo Pugliese, & Jan Recker. (2017). Quantitative Data Analysis. Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.spr.sprbok.978.3.319.42700.3