The goal of the course is to enable students to build and evaluate statistical models for the analysis and interpretation of data in the behavioural sciences. The focus is on methods of statistical modelling of data and practical decision-making, rather than on statistical theory per se.
Simple linear and nonlinear regression, multiple regression, and logistic regression are the main topics. IBM SPSS software is used. Regression is a highly general and very flexible data analytic framework in which to examine phenomena in the behavioural sciences. It can be used to predict or to explain relationships between an outcome variable and predictors or explanatory variables of interest. Both continuous and categorical variables of the kind typically studied in psychology and education can be accommodated.
Students successfully completing this course should be able to comprehend the assumptions, limitations, and uses of correlational and regression analysis; compute and interpret regression solutions for non-experimental and experimental designs; conceptualize, analyze, and interpret path models including mediators and moderators; evaluate publications and compose research reports incorporating correlational and regression analyses.
Prerequisites: Successful completion of EPSE 482 is required. Successful completion of EPSE 592 is highly recommended.