Hierarchical linear modeling with large-scale international datasets.

Abstract

Data from international large-scale assessments (ILSAs) reflect the nested structure of education systems and is, therefore, very well suited for multilevel modeling (MLM). However, because these data come from complex cluster samples, there are methodological aspects that a researcher needs to understand when doing MLM, e.g., the need for using sampling weights and multiple achievement values for parameter estimation. This course will teach participants how to do MLM with data from ILSAs, such as PIRLS, TIMSS, and PISA. The content of the course will include an overview of the ILSAs and a presentation on the design of these studies and databases and implications for MLM analysis. Participants will learn how to specify two-level models using the HLM software program and also learn about model comparison, centering decisions and their consequences, and available resources for doing three-level models. Time will be allotted for participants to work on practice exercises, with several instructors available to mentor and answer questions. Participants should have a solid understanding of OLS regression and a basic understanding of MLM. Prior experience using a statistical software program, such as Stata or SPSS, is helpful. Prior knowledge about ILSAs or prior experience using the respective databases or HLM software is not required.