The course covers matrix algebra, multiple regression, classical path analysis, polynomial regression, standardized regression, stepwise solutions, analysis of variance, weighted least squares, and logistic regression. The general linear model used as a context for regression methods. Basics of Structural Equation Modeling. Quantitative techniques using manifest and latent variable approaches for the analysis of educational and social science data. Introduction to structural equation modeling approaches to multiple regression, factor analysis, and path modeling.
Emphasis is placed on developing, estimating, and interpreting structural equation models.
A gentle introduction . Students learn to use SAS statistical package to perform data management, data analysis and to write reports. Participants will develop and interpret HLM models, learn to use HLM software, and present small projects completed during the session. This course will cover a number of flexible inferential techniques outside the realm of normal-theory tests. These methods are appropriate for researchers working with qualitative data or data for which the normality assumption is suspect.