This paper investigated the effect of teacher quality, represented by teacher level characteristics, on mathematics gain scores employing a three-level hierarchical linear model (HLM) through value-added model (VAM) approach. The analysis investigated significant predictors at student, teacher, and school levels for predicting students' gain scores and also estimated d-type effect sizes at teacher and school levels. We found the significant effects of teacher's mathematics content certification, teacher experience, and the interaction effects of mathematics content certification with student level predictors. Although school poverty significantly predicted students' gain scores, the school level effect was relatively small.

This study explored significant predictors of student's Grade Point Average (GPA) and truancy (days absent), and also determined teacher effectiveness based on proportion of variance explained at teacher level model. We employed a two-level hierarchical linear model (HLM) with student and teacher data at level-1 and level-2 models, respectively. Using the data from one of the largest urban school districts in the United States, the analysis identified several significant intervening and demographic predictors at the student level and two significant predictors at the teacher level. The percentages of variance explained at the teacher level ranged from 12% to 15% with 'small' to 'medium' effect sizes.

In this article, we predicted students' mathematics gain scores employing two-level hierarchical linear models (HLM) through value-added approach using data from one of the largest urban school districts in the United States of America. Effects of teacher quality or teacher effectiveness, characterized by teacher's certification in mathematics content area and teacher experience, were measured on students' gain scores. The results showed significant impact on mathematics gain scores due to teacher's content certification and teacher experience at teacher level and pretest scores as well as free and reduced lunch status at student level including cross-level interaction effects of teacher content certification with

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For low achieving (at-risk) high school graduates, this article identified significant student and school level predictors of college readiness in reading and mathematics. This study employed a two-level hierarchical generalized linear model (HGLM) to explore the fixed and random effects. The study included 36 high schools where 3,784 students in reading and 2,903 students in mathematics with achievement levels 1 and 2 in both subjects were selected from one of the largest school districts in the United States. At the student level, grade point average (GPA), exceptional student education (ESE), English language learner (ELL), and Hispanic status of students were significant. At the school level, percentage of teachers with National Board certification, percentage of teacher effectiveness and advance degrees as well as average years of teaching experience were significant in predicting college readiness. The effect sizes, which ranged from .29 to .37, were determined to be small.