This workshop teaches the concepts and analysis of multilevel data through multilevel models (also known as hierarchical linear models or mixed models). With understanding of basic linear regression concepts as a prerequisite, the instructors will cover a wide range of topics including clustered data, longitudinal studies, and clustered longitudinal data. Participants will be introduced to the use of HLM 7.0 software. The workshop will consist of lively lectures and hands-on examples using HLM software.Many studies in social sciences (e.g., education, human development, public health, sociology) are multilevel, longitudinal, or both. Multilevel data arise when participants are clustered within social settings. The variation and covariation within and between such settings are often of interest substantively and should not be ignored when assessing relationships between explanatory variables and outcomes. In longitudinal research, we repeatedly observe subjects. These repeated measures for each participant will be correlated and explanatory variables may be time-varying or time-invariant. This workshop will consider the issues of analysis that arise in multilevel and longitudinal research settings.

We will first consider two-level cross-sectional studies in which persons (level-1) are nested within groups (level-2). The level-1 model specifies a process within each group, and the level-2 model explains how these processes are different between groups. Next, we will discuss two-level studies of individual growth and compare the structures of these studies to multilevel studies. We will also consider three-level models. We will focus on the case in which repeated measures (level-1) are nested within persons (level-2) who are themselves nested in organizations (level-3).

All of these studies will involve nearly continuous outcomes for which the normality distribution is at least plausible. They will also feature purely nested designs (e.g., persons nested within organizations). The workshop will provide participants with an overview of other types of applications where hierarchical linear models or generalized hierarchical linear models are appropriate (e.g., binary outcomes), and briefly discuss how the HLM software could be used to model such data.