Predicting Reading Proficiency in Multilevel Models: An ANOVA-like approach of interpreting effects

Abstract

This study used an analysis of variance (ANOVA)-like approach to predict reading proficiency with student, teacher, and school-level predictors based on a 3-level hierarchical generalized linear model (HGLM) analysis. National Assessment of Educational Progress (NAEP) 2000 reading data for 4th graders sampled from 46 states of the United States of America were used. The study found that both the rich and poor minority students in a rich school benefited the most in reading performance, whereas rich and average socioeconomic status non-minority students in a rich school being taught in the non-crowded classroom achieved considerably high in reading proficiency. Based on the 3-level HGLM analysis, the ANOVA-like approach enabled the researchers to predict reading proficiency and interpret predictors' effects in a simple fashion.

Acknowledgements

I am thankful to Dr Richard Tate, former professor of the Florida State University, who contributed significantly to the development and completion of my doctoral dissertation, which provided the basis of this article. I am also thankful to three anonymous reviewers who provided valuable feedback for improving this article.

Notes

1. A conditional model is the model which includes potential predictor/s in the equation.

2. An unconditional model is the model that does not include any predictor in the equation.

3. In this study, SES has been classified as: high = 2.32, medium or average = 0, low = −2.32.

4. In this study, school mean SES has been classified as: high = 1.28, medium or average = 0, low = −1.28.