Linear structural equation models (LSEMs) are used frequently in the social sciences for the analysis of observational data. Their main attractiveness derives from the fact that they allow the substantive theory (i) to contain both manifest and latent variables, (ii) to specify interdependence 'reciprocal causation' among variables, and (iii) to define structural relationships between latent variables. LSEMs include many well-known types of multivariate analysis, such as regression analysis, factor analysis, path analysis and simultaneous equations. The first objective of this course is to give students a graduate level introduction to LSEMs. Besides discussion of topics such as model specification, identification, estimation, etc., particular attention will be paid to the structural interpretation of LSEMs, i.e. their meaning as causal model, as opposed to their content as statistical model. The theory on LSEMs will be illustrated by examples which are estimated using the LISREL 8 statistical software (students need [3] in order to perform these analyses). The second objective of the course is to make clear the relationship between LSEMs and graphical models. The theory on graphical models will be presented so far as necessary to obtain the main result (consistency theorem) stating that a LSEM satisfies the Markov properties implied by its path diagram. Literature for this part of the course is in the form of Lecture notes [4] which will be distributed at the beginning of the course. 

Prerequisites: Students are assumed to be acquainted with introductory level linear algebra, and with the essentials of inferential statistics (including OLS regression and ML estimation).