This course will cover research design and methodology. Topics may include experimental, quasi-experimental, single subject and survey research; validity; reliability; confounds; measurement; sampling; inductive inference.
This course is part of the Day One Access program; you should not purchase any course materials denoted by the Day One Access logo as they are part of your course registration. In the event you drop this course from your schedule on or prior to the last day of DROP/ADD period, your account will be fully refunded automatically if you received a digital textbook. If you received a physical textbook you will need to return the textbook to the store and your account will be refunded at that time.
This course affords students the opportunity to earn course credit for their work in supporting instructors and engaging with their peers in an undergraduate level course. It is designed for motivated and academically inclined students who wish to contribute to the educational environment and gain valuable, practical experience. Students work with instructors to identify appropriate tasks to be completed.
This course is part of the Day One Access program; you should not purchase any course materials denoted by the Day One Access logo as they are part of your course registration. In the event you drop this course from your schedule on or prior to the last day of DROP/ADD period, your account will be fully refunded automatically if you received a digital textbook. If you received a physical textbook you will need to return the textbook to the store and your account will be refunded at that time.
The department offers selected topics that may not be offered on a regular basis. Prerequisite: permission of the instructor.
Social science data frequently have a hierarchical or multilevel structure as a consequence of sampling designs or repeated measures. The purpose of the course is to introduce students to the basic principles and applications of hierarchical linear modeling in social science research. Topics covered include an introduction to multilevel analyses, random intercept models, random slope models, hypotheses testing, hierarchical models for limited dependent variables, model fitting, three-level models, and repeated-measures applications. Prerequisites: PSYC 728 or PSYC 828 or equivalent.
This course is a pass/fail course doctoral students may take to maintain active status after successfully passing the candidacy examination. All doctoral students are required to be registered for at least one graduate credit hour every semester until their graduation.
The department offers selected topics that may not be offered on a regular basis. Prerequisite: permission of the instructor.
Student participation in a supervised, undergraduate research experience for which credit will not apply to the degree. Experience must be related to the student's major, minor or career area of interest. Prerequisites: permission of the instructor.
Review of the basic descriptive and inferential statistical procedures with a heavy emphasis on fundamental and advanced analysis of variance techniques. Topics include contrasts, factorial designs, within-subject and mixed designs, and analysis of covariance. Course materials are covered in the context of classical experimental and quasi-experimental design. Prerequisites: admission into the psychology M.S. or Ph.D. program or permission of the instructor.