Prerequisites:

Course Contents

1. Styles of causal thought and models of controlled experiment: meaning of statistical inference concepts of relative error and confidence interval; simple bi-variate approaches and unmatched differences; one way and factorial analysis of variance. 2. Causal analysis in non experimental data; conceptual framework as flowcharts. 3. Models and explanatory fit; modeling social phenomena; measures of life expectancy, disease burden and health inequality. 4. Analysis of frequencies chisquare, odds ratios and relative risk, Cramer's V and Kendall' tau. 5. Multivariate analysis un standardized and standardized regression coefficients; uses of dummy vaiables; role of proxies; structural equation models; analysing longitudinal and cohort data; tempo effects. 6. Standardized rates and ratios as dependent variable; ratios of ratios and rates such as ratio of male and female mortality rates; working with indices such as HDI and PCI.

Topic