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Master 2018/2019

Methodology and Research Methods in Sociology: Quantitative Research Methods

Type: Compulsory course (Comparative Social Research)
Area of studies: Sociology
Delivered by: School of Sociology
When: 1 year, 2 module
Mode of studies: Full time
Master’s programme: Comparative Soсial Research
Language: English
ECTS credits: 4

Course Syllabus


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

  • 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 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
  • to be able to analyze secondary data at the level required for an MA thesis
  • Have skills in using R Studio for statistical data analysis
  • Have skills in interpreting results and writing research papers
  • Have skills in the evaluation of the quality of published research papers
Course Contents

Course Contents

  • Statistical inference in social sciences
    Descriptive and inferential statistics. Random and non-random sampling, the case of “Literary Digest”. Normal distribution and z scores. The logic of statistical inference and Central limit theorem. Confidence level and confidence intervals.
  • T-tests, One-way ANOVA
    Steps of hypothesis testing, Two-tailed/one tailed assumptions, Type I/Type II errors. One sample t-test. Independent samples test, paired samples test. T-distribution for small samples. ANOVA (Analysis of variance), F-distribution, post hoc tests. Equality of variance tests and tests for normal distribution.
  • Non-parametric analogues of T-tests and one-way ANOVA
    Differences between parametric and non-parametric methods. Signed-Rank Test, Mann-Whitney U-test, Wilcoxon signed rank test, Kruskal-Wallis test, post hoc tests for non-parametric methods.
  • Pearson's chi-square for contingency tables
    Properties of Pearson's chi-square, independence of two variables, expected and observed frequencies. Chi-square distribution, Yates correction for 2x2 tables. Limitations of the chi-square test.
  • Correlations
    Association between two variables, types of dependence. Pearson`s r for metric scales, Pearson`s R for metric scales and test of significance. Spearman`s rank correlation coefficient, Kendall`s rank correlation coefficient. Partial correlation.
  • Linear regression
    Bivariate regression, quality of the bivariate model. Multiple regression and quality of multiple regression model. Interpretation of regression coefficients, the significance of regression coefficients. Standardization and interpretation of standardized coefficients.
  • Regression diagnostics: heteroscedasticity, multicollinearity, non-linearity
    Testing regression assumptions: linearity, multicollinearity, heteroscedasticity, normal distribution of errors, autocorrelation, outliers.
  • Categorical independent variables in linear regression and interaction effects
    Regression with dummy variables, interpretation of coefficients. Regression with categorical and nominal independent variables. Interaction effects, interpretation of interaction effects. Limitations of traditional regression tables for interpretation of interactions effects
  • Binary logistic regression
    Key concept of binary logistic regression: probability, odds, odds ratio, logit. Interpretation of coefficients. Quality of a model.
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

  • Interim assessment (2 module)
    0.3 * Essay + 0.3 * Exam + 0.3 * Home assignment + 0.1 * Reading published research


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