Type of Document	Dissertation
Author	Subedi, Bidya Raj
Author's Email Address	b.subedi@mailcity.com
URN	etd-04112005-205644
Title	A Demonstration of the Three-Level Hierarchical Generalized Linear Model Applied to Educational Research
Degree	Doctor of Philosophy
Department	Educational Psychology and Learning Systems, Department of
Advisory Committee	Advisor Name Title Dr. Richard Tate Committee Chair Dr. Akihito Kamata Committee Member Dr. Albert Oosterhof Committee Member Dr. Janice Flake Committee Member
Keywords	teacher and school effects interaction effect main effect odds and odds ratios unconditional and conditional models NAEP reading data three-level HLM and HGLM cross-level interactions simple effect description
Date of Defense	2004-12-08
Availability	unrestricted
Abstract This study demonstrates the three-level hierarchical generalized linear model (HGLM) applied to educational research. The sequential steps for developing, analyzing, evaluating, and applying the three-level HGLM are demonstrated in the study. In the study, the effects of predictors are interpreted using the simple effect and ANOVA-like approaches. In order to describe predictors’ effects, odds and odds ratios are computed and interpreted. This study used NAEP 2000 Reading data for fourth grade students. A sample of 7,175 students, 1,076 teachers, and 295 schools from 46 States was used in the study. Student, teacher, and school level data were used as level-1, level-2, and level-3 units respectively for analysis. Reading proficiency was considered as a dichotomous outcome. Socioeconomic status (SES) and minority were used as student level predictors; class size was used as a teacher level predictor; and school mean SES was used as a school level predictor. Positive effect of SES and school mean SES on reading proficiency was found. However, negative effect was found due to minority and class type on reading proficiency. Graphical methods are presented to interpret the effects for class type and minority on reading proficiency. Specifically, the effect of class type is presented graphically for minority and non-minority students associated with different levels of school mean SES. Similarly, the effect of minority is depicted for crowded and non-crowded class types associated with different levels of school mean SES. The research practitioners not only can replicate the procedural steps of demonstrating the three-level HGLM as presented in this study, but they also can interpret predictors’ effects using simple effect and ANOVA-like approaches described in this study. Despite the complexity of the process in computing effects using the simple effect approach, researchers can interpret effects with less complication using this approach compared to the traditional HGLM approach.
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