KSOD 2430 - Quantitative methods II

• Make students familiar with regression methods (theory and applications)
• Learn techniques and limitations of different methods of empirical analysis
• Treat large databases with representative probability samples
• Know statistical tools and computer programming (R)
• Present quantitative results in a synthetic way (oral and written)
• Reminder semester 1
• The concept: Correlation vs. causality
• Hypotheses and limits of the simple OLS model
• Methodological challenges (omitted variable bias, heteroscedasticity, autocorrelation, multicollinearity, endogeneity)
• Multiple linear and non-linear regression models and their interpretations (estimation equations, graphical presentations, squared terms and interaction terms)
• Logit and probit regressions for binary and categorical variables, margins
• Data types: Cross-section, time-series and panel data; macro and micro data
• Fixed-effect models; Multi-level models

Quantitative methods 1, R programming summer school

Integrated method: combination of sociological concepts, statistical categorisation and empirical analysis to describe and understand sociologic and demographic phenomena

• Apply a quantitative approach to study societal issues
• Use statistical tools to measure socio-demographic dynamics
• Use sociological concepts to make assumptions, to interpret and to relativize the quantitative results
• Critical evaluation of data quality

One individual written test in the middle of the semester, one analytical work to hand in at the end of the semester