Analyzing Data using Hierarchical Linear Models

On May 15-16 CRRC-Armenia organized a methodological training on "How to Analyze Data using Hierarchical Linear Models?" conducted by Katy Pearce.

Hierarchical linear models (HLM) are a type of model used for analyzing data in a clustered or nested structure. An example of such data is students who are nested within classrooms, which are nested within schools; in this situation, we would expect that students within a cluster, such as a classroom or school, would share some similarities due to their common environment. Hierarchical linear models are also known as multilevel models, random coefficient models, or random effects models. HLM can be used to analyze a variety of questions with either categorical or continuous dependent variables.

Ms. Katy Pearce is a Ph.D. student from the University of California at Santa Barbara and a Fulbright Scholar in Armenia. She has extensive experience in quantitative and qualitative research methods. Ms. Pearce is particularly interested in cross-cultural comparison, particularly looking at national cultural variables and attitudes towards adoption of technologies. She also is interested in social effects of technology adoption.