Allison Atteberry is an assistant professor of in the Research and Evaluation Methodology program at the CU Boulder School of Education. Her academic interests center on policies and interventions that are intended to help provide effective teachers to the students who need them most. This has led to a focus on the identification, selection, development, and retention of teachers who have positive impacts on student achievement. Specific topics include the development of measures of teacher and school effectiveness, teacher preparation, high quality professional development, mentoring and peer collaboration, efforts to use measures of effectiveness formatively to improve practice, policies that target district responses to teachers and schools based on measures of effectiveness, and incentives for the strongest teachers to work with the most disadvantaged populations. Dr. Atteberry has expertise in both econometric and statistical approaches to education policy analysis.
keywords
education policy, development of teaching effectiveness, measuring school quality, educational equity, quantitative research methods, school interventions
EDUC 7326 - Quasi-Experimental Design in Causal Inference in Social Sciences
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Fall 2019
Focuses on experimental and quasi-experimental designs in educational research; applications of the general linear mode; power and statistical efficiency; randomization and control; multiple comparisons; factorial experiments and interaction with fixed-factor and mixed design; analysis of covariance; effects of assumption violations; and related computer programs for statistical analysis. Recommended prerequisite of a graduate-level introduction to stats course.
EDUC 7456 - Multilevel Modeling
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Fall 2018 / Fall 2020
Covers in depth two advanced multivariate models common to social science research: latent variable (structural equation) models and multi-level (hierarchical) models. Topics may be taught with a particular analytic context, such as measurement of change (longitudinal analysis) or experimental design. Recommended prerequisite of one year of graduate-level stats course.
EDUC 8240 - Applied Regression Analysis
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Spring 2020
Statistical analysis can be a powerful tool for understanding social, educational, psychological, and developmental processes. In this course, we will learn to answer such questions using multiple regression analysis, to develop an understanding of the strengths and limitations of this approach, and practice communicating results clearly and accurately. By the end of the semester, students in this course should be able to critically examine published research using regression and carefully perform their own regression analyses using empirical data. Recommended prerequisites of EDUC 8230 or another course in basic statistical methods.
EDUC 8270 - Intermediate and Advanced Application of Quantitative Methods for Behavioral Scie
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Spring 2021
This courses helps students develop the pragmatic skills needed to conduct quantitative analyses in their own research, in which they must apply concepts with novel data and in novel settings. It also provides a formal introduction to a variety of interstitial topics in quantitative analysis that may be assumed knowledge in more advanced methods courses. In addition, students will learn how to teach themselves new quantitative methods as they need them in their future careers.
