Quantitative Data Analyses
- The main objective of the course is to 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.
- 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
- Advanced analysis with linear regressionInteraction 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 regressionVariables 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 regressionVariables with multiple outcomes, the difference between ordered and unordered responses. Contrasts in outcome variables. Interpretation of β-coefficients in multinomial models.
- Ordered logistic regressionCumulative probability. Latent variable interpretation, thresholds. Odds-ratio and the interpretation of the coefficients. Model selection and assumptions.
- Models for count dataPoisson regression. The difference between count and ordered responses. Poisson distribution. Interpretation of the coefficients, incident ratios. Overdispersion, offsets, excessive zeroes.
- Introduction to multilevel modelingHierarchical 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 regressionsDifferent 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.
- Test 1
- Test 2
- In-class assignmentsAn unweighted average of grades for in-class assignments.
- Final examThe exam is held online (in Skype) in the form of a test covering all topics.
- Interim assessment (3 module)0.2 * Final exam + 0.4 * In-class assignments + 0.2 * Test 1 + 0.2 * Test 2
- 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
- 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