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Regular version of the site

Quantitative Data Analyses

Academic Year
Instruction in English
ECTS credits
Course type:
Elective course
1 year, 3 module


Course Syllabus


The course covers the topics of diagnostics for linear models, binary, multinomial, ordered, Poisson regression, along with multilevel data analysis. Students are assumed to have basic knowledge of statistics and be familiar with several conventional statistical methods, most importantly linear regression, and with the R programming environment.
Learning Objectives

Learning Objectives

  • Give an introduction to a variety of extensions of linear regression analysis widely used in modern social sciences, as well as implementations of the respective methods in R, a popular, free programming language for statistical computing. By the end of the course, students will be able to choose relevant methods of analysis, implement necessary techniques, and interpret the results of modeling.
Expected Learning Outcomes

Expected Learning Outcomes

  • Able to read and understand most academic social sciences articles that use quantitative approach
  • Able to design a quantitative social study
  • Able to use R programming language for complex statistical computations
  • Able to choose statistical methods appropriate to their data and substantive research problem
Course Contents

Course Contents

  • Advanced analysis with linear regression
    Interaction terms. Variable transformations. Quadratic terms, logarithms. Regression model diagnostics. The linearity of the data. Homogeneity of variance, non-constant error variance. variance inflation factors. Non-independence of Errors. Outliers, hat values, and high leverage points. Standardized residuals (studentized residuals), Cook’s distance, variance inflation factors, Durbin-Watson test.
  • Binary logistic regression
    Variables with binary outcomes. Standard logistic function. Bivariate and multiple logistic models. Logit and probit link functions. Latent variable interpretation. Interpretation of β-coefficients, odds-ratio. Model fit: pseudo R2, PCP, ePCP.
  • Multinomial logistic regression
    Variables with multiple outcomes, the difference between ordered and unordered responses. Contrasts in outcome variables. Interpretation of β-coefficients in multinomial models.
  • Ordered logistic regression
    Cumulative probability. Latent variable interpretation, thresholds. Odds-ratio and the interpretation of the coefficients. Model selection and assumptions.
  • Models for count data
    Poisson regression. The difference between count and ordered responses. Poisson distribution. Interpretation of the coefficients, incident ratios. Overdispersion, offsets, excessive zeroes.
  • Introduction to multilevel modeling
    Hierarchical linear models. Hierarchy in the data structure. Intraclass correlation coefficient. Fixed and random effects. Random intercepts and random slopes. Linear models for hierarchical data.
  • Multilevel logistic regressions
    Different types of generalized linear models with a hierarchical structure ICC in logistic models. Random intercepts and random slopes in logistic models. Multilevel models for binary, ordinal, and count responses.
Assessment Elements

Assessment Elements

  • non-blocking Test 1
    During the class, participants are presented with a set of practical tasks that aim to test their understanding of variable transformations, diagnostics, binary and multinomial logistic modeling. The results should be submitted via email by the end of the class.
  • non-blocking Test 2
    During the class, participants are presented with a set of practical tasks that aim to test their understanding of regression analysis of different types of data. The results should be submitted via email by the end of the class.
  • non-blocking In-class assignments
    During every class, the participants are presented with a task that tests their understanding of the topic. The results should be submitted via email by the end of the class.
  • non-blocking Final exam
    The participants are presented with the tasks that test their knowledge and understanding of all topics covered during the course.
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.2 * Final exam + 0.4 * In-class assignments + 0.2 * Test 1 + 0.2 * Test 2


Recommended Core 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
  • Field, A. V. (DE-588)128714581, (DE-627)378310763, (DE-576)186310501, aut. (2012). Discovering statistics using R Andy Field, Jeremy Miles, Zoë Field. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edswao&AN=edswao.363067604
  • Smith, R. B. (2011). Multilevel Modeling of Social Problems : A Causal Perspective. Dordrecht: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=371921

Recommended Additional Bibliography

  • Chatterjee, S., Hadi, A. S., & Ebooks Corporation. (2012). Regression Analysis by Example (Vol. Fifth edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=959808
  • Freund, R. J., Wilson, W. J., & Sa, P. (2006). Regression Analysis (Vol. 2nd ed). AMsterdam: Academic Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=320724
  • Jiang, J. (2007). Linear and Generalized Linear Mixed Models and Their Applications. New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=212826
  • 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