Time Schedule:
Theodore P Beauchaine
PSYCH 525
Seattle Campus
Analysis of data in the behavioral sciences. Required of all first-year graduate majors. Prerequisite: PSYCH 522, PSYCH 524; concurrent registration in PSYCH 523, or permission of instructor. Offered: W.
Class description
This course provides moderately detailed coverage of multiple linear regression and related data-analytic techniques. I assume that you are familiar with logic of analysis of variance, that you have at least some exposure to correlation, and that you have used SPSS or a similar statistics package in the past. We will begin with a brief overview of simple linear regression before moving on to multiple regression applications. A clear understanding of multiple linear regression provides the foundation for almost all statistical applications in psychology and related behavioral sciences. In this sense, this may be the most important methods class you take in graduate school. I strongly recommend that you do not fall behind in this class, as topics build on one another. Although I expect everyone to do well, if you find yourself struggling, contact me or one of the class TAs immediately. You will complete assignments every week, both by hand and using the Statistical Package for the Social Sciences (SPSS). Although SPSS is not the most sophisticated statistics package available, it is by far the most widely used. Learning it is therefore likely to be more practical for students doing applied data analysis. It should be noted, however, that we live in an age where knowledge of a single statistics package is usually not enough for methodological proficiency in a given sub-discipline. Nevertheless, you should emerge from this course with a foundation on which to build an effective data-analytic skill set.
Student learning goals
General method of instruction
Lecture with associated lab (PSYCH 523).
Recommended preparation
PSYCH 522, 524.
Class assignments and grading
Weekly analyses of data sets from students' labs.
Weekly assignments and in class exams (2).
