The first part of the course involves data handling, scale construction (mainly classical test theory, factor analysis), and the disciplined cautious use of syntax in statistical software (MERM: SPSS; SaSR: Stata). The second part of the course addresses statistical models, mostly from the class of generalized linear models. Models to be discussed:

(1) Linear regression modeling for continuous dependent variables (such as income), focusing on modeling mediation, interactions and nonlinear relations;
(2) Regression models for binary dependent variables such as whether or not people are employed: logistic and probit regression;
(3) Regression models for ordinal dependent variables (e.g., voting intentions measured by a Likert item): ordinal logit.
(4) Regression models for nominal dependent variables with subject-level and/or alternative-level predictors (e.g., the denomination of the school attended by children): multinomial logit and conditional logit models;
(5) Regression models for the time-until-the-occurrence-of-an-event (e.g., the birth of the first child, the divorce of a marriage, promotion in a career): discrete time survival analysis and Cox regression. The discussion on these regression-type models focuses on the substantive interpretation of these nonlineair models. In addition we discuss numerous general statistical issues such as statistical estimation methods; Wald testing versus likelihood ratio testing; Hausman tests for model specification; marginal effects, etc. Computer practical for MERM (using SPSS) and SaSR (using Stata) are separate.