The topic of this year's Summer Program in Data Analysis is Structural Equation Models, “SEMs” for short. The first three days of the Program, May 12 - 14, provide a complete introduction to SEMs, and will be taught by Professor David Flora of York University’s Department of Psychology. The Program for May 16 - 18 will address some advanced topics in Structural Equation Modeling, and will be taught by Professor Ken Bollen of the University of North Carolina, a pioneer in the application of SEMs in the social sciences.

In the past 35 years, SEMs have become the statistical tool of choice for analysis of causal relations based on measures with error. SEMs combine ideas of “path analysis,” developed by Sewall Wright in the 1920s to describe the relationships in a causal chain, and factor analysis, developed in the 1930s to conceptualize “traits”. In the late 1960s, K. G. Jöreskog and D. Sörbom combined and generalized these ideas, developing a framework for simultaneous estimation of the causal relations among conceptual variables (usually called “latent” or “unmeasured” variables) and between the conceptual variables and their empirical measures (called “manifest”, “measured” or “observed” variables), using maximum likelihood estimation. Since that time, the application of SEMs has been broadened to cover binary and ordinal variables, longitudinal data and multiple groups. SEMs have found their widest application in psychology and sociology, but are used routinely in almost every area of social science.

SEMs thus extend familiar regression models, path modeling, factor analysis models, analysis of variance and covariance models, simultaneous equation models and some forms of longitudinal growth models into a general linear model framework based on the analysis of covariance structures. Structural equation models are especially useful in the analysis of longitudinal data.

For the computer lab sessions in the Program, which take place every day in the afternoon, we will use SAS (Statistical Analysis System) and AMOS (an SPSS product), with some references to other software. We will provide a complete introduction to SAS for the Program, because of its flexibility and the availability of support in most social science research environments. For non-SAS users, the first day of SPIDA (May 11) is a one-day SAS Workshop taught by Dr. Hugh McCague, Statistical Consultant at the Institute for Social Research.

Coordinated by Professors Bryn Greer-Wootten and Michael Ornstein of the Institute for Social Research, the 2011 SPIDA organizing committee includes Professors Robert Cribbie, David Flora and Michael Friendly of York’s Department of Psychology, Professor Georges Monette of York’s Department of Mathematics and Statistics, Professor John Fox (Sociology) of McMaster University, and Dr. Hugh McCague, ISR Statistical Consultant.