SRCLD Presentation Details
  Title  
       
    Introduction to HLM: Applications in the Social Sciences  
Author(s)
Jee-Seon Kim - University of Wisconsin-Madison

SRCLD Info
SRCLD Year: 2005
Presentation Type: Tutorial
Presentation Time: (na)
Abstract
Hierarchical linear modeling (HLM) provides a flexible set of analytic tools to study a wide range of social and developmental processes. Whereas traditional regression models assume independence among observations, HLM explicitly accounts for dependency in nested or clustered data. The purpose of this course is to provide researchers who want to apply hierarchical modeling techniques in their research with the fundamental ideas of HLM.

The first part of the course provides a general introduction to HLM with examples, and the second part demonstrates the usage of the HLM software and interpretation of results. Examples include two types of applications. The first application focuses on contextual effects in nested settings where siblings are nested within families or students are nested within schools. The second application is concerned with individual growth or change over time where longitudinal repeated measures are clustered within individuals.

A basic understanding of statistical inference and some experience with regression analysis are expected.
Author Biosketch(es)