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Instructor Class Description

Time Schedule:

Robert D Abbott
Seattle Campus

Hierarchical Linear Models

Theory and data analysis for research models where random factors are nested, such as multi-level data, growth curve analysis, and meta-analysis. Prerequisite: EDPSY 593 or equivalent.

Class description

Theory and data analysis for hierarchically structured data such as multilevel data (children in classrooms), longitudinal data, and metaanalysis. Students will use HLM software for projects.

Student learning goals

General method of instruction

Lecture, small group discussion; student presentations.

Recommended preparation

Courses in Correlational Methods and Analysis of Variance.

Class assignments and grading

Student projects and presentation in class.

Course graded credit/no credit based on student completion of projects.

The information above is intended to be helpful in choosing courses. Because the instructor may further develop his/her plans for this course, its characteristics are subject to change without notice. In most cases, the official course syllabus will be distributed on the first day of class.
Last Update by Jennifer L. Davis
Date: 09/23/1999