Evaluation, Statistics, and Methodology, PhD
The PhD program in Evaluation, Statistics, and Methodology (ESM) has been carefully designed to provide students with an integrated, sequenced, and experientially based doctoral program leading to a meaningful professional career.
Program overview
The ESM program is intended for students with education, social science, psychology, economics, applied statistics, and/or related academic backgrounds seeking employment within the growing fields of applied evaluation, assessment, and statistics. These fields have grown substantially in response to the needs of the federal and state governments, non-profit organizations and foundations, and private sector entities to demonstrate and continually improve the effectiveness of their programs and services. The ESM doctoral program is based squarely upon the needs of students seeking to enter ESM fields, expand opportunities within their current professional situation, or prepare for an academic career and engage in research.
Why study Evaluation, Statistics, and Methodology?
Located within the Department of Educational Leadership & Policy Studies (ELPS), the ESM program integrates evaluation, statistics, and measurement theory, content knowledge, technical skills, and highly relevant and meaningful field experiences to enable graduates to function as esteemed professionals, productive scholars, and leaders in their sub-fields of interest.
What can you do with a PhD in Evaluation, Statistics, and Methodology?
Senior Evaluation Analyst
Statistical Analyst
Data Scientist
Survey Methodologist
Featured Courses
Narrative Methods
In-depth exploration of narrative research methodologies both as a type of design and as a type of analysis. Students will design, conduct, and analyze data through a narrative project in this course.
Visualizing Data Using R
Intended to support students in creating static visualizations (e.g., visualizations for inclusion in presentations and publications) and dynamic visualizations (e.g., those that can allow researchers and others to interact with the visualization).
Foundations of Educational Data Science
Introduces students to the data science software and programming language R. Course activities focus on preparing, using, and visualizing complex data sources for analysis using the tidyverse suite of R packages.
Multilevel Modeling
Techniques for analyzing hierarchical and longitudinal data structures in education and social sciences. Topics include random-intercept and random-slope models, centering strategies, model diagnostics, longitudinal analysis, and models for categorical outcomes.
Request Information
Interested in learning more? Contact Us!