Time Schedule:
Brian P Flaherty
PSYCH 548
Seattle Campus
Intensive readings from the current literature on an emerging topic or theoretical perspective in quantitative psychology. Student presentations and discussion. Prerequisite: graduate standing in Psychology, or permission of instructor.
Class description
There doesn't appear to be a way to distinguish the different courses I teach under the 548 number, so the description here changes. Fall 2011, I'm teaching Confirmatory Factor Analysis and Structural Equation Models.
In this course, we'll first quickly review regression and then discuss some ancillary data issues. The heart of the course will begin with manifest variable path analysis. From there, we'll move to confirmatory factor and structural equation models. As time allows, we'll introduce topics such as incorporating means, multiple groups analysis, invariance, etc.
We focus on applying these models. We'll focus on decisions and issues involved in using these models. Ideally, one should come out of this course able to understand empirical papers using these models, fit a range of models oneself, and know where to go to find out how to do something we didn't discuss.
Student learning goals
Understand why and when to use these models appropriately.
Understand how to approach model evaluation and selection.
Be able to run basic confirmatory factor and path models.
Be able to write an explanation of the analysis and results.
Evaluate other's use of these models.
General method of instruction
Doing your own analysis and write-up based upon your (or my) empirical data. Additionally, we'll have readings, lecture and discussion, worked data examples and some canned homework exercises to make sure you are running the software properly.
Recommended preparation
Comfortable with multiple regression. This course is planned to follow the two quarter psychology grad. stat. sequence.
Class assignments and grading
Ideally, bring your own data to use for the class. Will successively develop analytic plan, conduct analyses and write up three different analyses.
Primarily your data analysis and research papers; homework counts a little and class participation (in some form) helps.
