Change over time is a fundamental concept in the social, behavioural, and health sciences. For some areas such as human development or areas involving program evaluation, change is a central aspect of study. In other areas, change may not be the central aspect of study, but it can still be of concern. Research in educational, cognitive, school, clinical, counselling, or social psychology examines change whenever treatments are compared to a control group or to a base rate. Researchers are frequently interested in answering questions requiring the use of pre- and post-measures and longitudinal (multi-wave) data. This course will cover the conceptual measurement, design and data analysis issues surrounding change and growth. Where possible, practical applications will be brought to class and be the focus of discussion.
Objectives: This course focuses on: (i) issues in the use of change or difference scores in two-wave data, (ii) HLM and other multi-level models for the trajectories resulting from multi-wave data that collected over more than two time points. Although the focus of the multi-wave analyses will be on HLM or multilevel models, if time permits, the use of structural equation models for change and growth modeling will be briefly described. Another way of describing this course is that you are learning about HLM modeling in the context of growth and change.
Prerequisites: Successful completion of a graduate course in statistics, data analysis, and research design (in the social, educational, and health sciences). It would be an asset for you to have completed a course covering basic topics in measurement (e.g., reliability and validity) and having covered regression and ANOVA. It would be a real asset for you if have covered MANOVA, repeated measures, and factor analysis, but these are not necessary. Students without measurement and regression will struggle with most of the material.