11.015 - PDC06: Hierarchical Linear Modeling With Large-Scale International Databases

Session Type: Professional Development Course

Abstract

Data from large-scale international studies reflect the nested structure of education systems and are, therefore, very well suited for hierarchical linear modeling (HLM). However, because these data come from complex cluster samples, there are methodological aspects that a researcher needs to understand when doing HLM (e.g., the need for using sampling weights and multiple achievement values for parameter estimation). This course will teach participants how to do HLM with data from large-scale studies, such as PIRLS, TIMSS, and PISA. The content of the course will include a presentation on the design of large-scale international studies and databases and implications for HLM analysis. Participants will learn how to specify simple two- and three-level models using the HLM 7 software package, incorporating students, classes or schools, and countries as hierarchical levels. There will be hands-on demonstrations about how to prepare an international dataset (using SPSS) for analysis with HLM 7 software and how to perform various HLM analyses. Participants will have the chance to work on practice exercises with several instructors available to answer questions. Participants should have a solid understanding of OLS regression and a basic understanding of hierarchical/multi-level models. Prior experience using a statistical software package, such as Stata or SPSS, is helpful. Prior knowledge about large-scale international studies or prior experience using the respective databases or HLM software is not required. To participate in the hands-on demonstrations and example analyses, participants should bring their own laptops with HLM software (a free student version is available).

Sub Unit

• Professional Development and Training Committee

Instructors

• Francis Howard Lim Huang, University of Missouri

• Sakiko Ikoma, American Institutes for Research

• Sabine Meinck, IEA Hamburg

• David C. Miller, American Institutes for Research

• Bitnara Jasmine Park, American Institutes for Research

• Yuan Zhang, American Institutes for Research