Prerequisites: 

At least one graduate-level course in statistics or quantitative methods, and experience with multivariate regression models, including both analysis of data and interpretation of results.

Description: 

Although many surveys gather data on multiple units of analysis (e.g., individuals, the groups or organizations in which individuals participate, the same measures taken over multiple time periods), most statistical procedures cannot make full use of data with this nested structure: individuals nested within groups, measures nested within individuals, and other nesting levels that may be of analytic interest. In this course, students are introduced to an increasingly common statistical technique, hierarchical linear modeling (HLM). Multi-level methods and the HLM software can (and should be) used to analyze nested data and multi-level research questions. Although the course demonstrates multiple uses of the HLM software, including growth-curve modeling, the major focus is on the investigation of organizational effects on individual-level outcomes. Although we use, for instructional purposes, data drawn from a nationally representative sample of U.S. elementary schools, students, and teachers, the multi-level analysis skills taught in this course are equally applicable in many social science fields: sociology, public health, psychology, demography, political science, and in the general field of organizational theory. Typically the course enrolls students from all these fields. Students will learn to conceptualize, conduct, interpret, and write up their own multi-level analyses, as well as to understand relevant statistical and practical issues.