This workshop will provide a series of examples of how multilevel modeling can be applied to a variety of research problems and questions.  An introduction to multilevel modeling will be followed by several demonstrations of the use of multilevel modeling methods to address different research problems.  Many social and natural phenomena have a nested or clustered organization.  Hierarchical Linear Modeling (HLM) provides a method for correctly analyzing such data as well as a means to study relationships that occur across levels. Workshop participants will learn foundational principles and concepts in HLM including model testing, fixed and random effects, parameter reliability, and empirical Bayes estimation.  Participants will see a variety of applications of HLM to research problems including estimation of the intraclass correlation coefficient, separating individual from school effects, the study of development and growth, and the use of HLM for meta-analysis.

Please direct any questions to Joe Stevens at stevensj@uoregon.edu.

Before the workshop, participants can:

• Read the following:

Osborne, J. W. (2000). Advantages of Hierarchical Linear Modeling, PARE, available at:  http://pareonline.net/getvn.asp?v=7&n=1

Hox, J.  (1995).  Multilevel Analysis, Read chapters 1 and 2.

Raudenbush & Bryk, 1986, Sociology of Education, available at:  http://www.uoregon.edu/~stevensj/HLM/raudenbush.pdf

Stevens, J. J.  (2005). The study of school effectiveness as a problem in research design.  In R. Lissitz (Ed.), Value-added models in education:         Theory and applications.  Maple Grove, MN: JAM Press. http://www.uoregon.edu/~stevensj/workshops/Stevens.pdf

Willms & Raudenbush (1989). Longitudinal HLM study of school effects, available at:  http://www.uoregon.edu/%7Estevensj/AofC/willms&raudenbush.pdf

• Download a copy of the workshop PowerPoint slides for note taking in pdf format (one slide/page) or as multiple slides/page

• If you want to try HLM you can download and install the free student version of HLM