Quantitative Methods
Department of Educational Psychology
Designed For
This program is designed for students who enjoy working with data and want to understand how research informs decisions in education and the social sciences. It is a strong fit for those interested in statistics, measurement and evaluation—whether you’re coming from education, psychology or a related field.
Career Objective
Graduates are prepared for roles such as statistical analyst, program evaluator or social science researcher in higher education, testing organizations, government agencies and school districts. The program also provides excellent preparation for students planning to pursue a doctorate in quantitative methods or related social science disciplines.
At a Glance
Program Starts: Fall
Deadline to Apply:
January 10
Length of Program: 24 months
Schedule: Part-time allowed
Program Location: On campus
GRE Required? No
Build Quantitative Expertise for Research and Impact
The Quantitative Methods Master’s in Education curriculum focuses on applied statistics, psychometrics and program evaluation, giving students both the theoretical foundation and hands-on experience needed to work confidently with data.
Throughout the program, students learn how to design and conduct quantitative research, analyze complex datasets and critically evaluate research findings. Coursework emphasizes choosing the right research designs, measures and statistical techniques to answer real-world questions in education and the social sciences.
Students also gain experience developing and evaluating measurement tools such as surveys and questionnaires, along with the computing skills needed to carry out advanced analyses. Graduates go on to work in colleges and universities, professional testing organizations, research and evaluation agencies, state departments of education and large school districts.
Area Chair
Hyeon-Ah Kang
Program Details
Program Requirements
Course requirements may vary from year to year. The courses listed below reflect typical degree requirements.
Quantitative Methods (QM) master’s students are required to complete:
- Quantitative Methods core courses
- Quantitative Methods electives
- Out-of-Specialization electives
Core Courses (20 Credit Hours)
Students take the following QM core courses:
- EDP 380C.2 Fundamental Statistics
- EDP 480C.6 Statistical Analysis for Experimental Data
- EDP 380D.4 Psychometric Theory & Methods
- EDP 480C.4 Correlation & Regression Methods
- EDP 381C.2 Research Design & Methods for PSY & ED
- EDP 380C Data Exploration and Visualization in R
Program Electives (9 Credit Hours)
Students select three courses from the following:
- EDP 380C Python for Social Science
- EDP 380C.12 Survey of Multivariate Methods
- EDP 380C.14 Structural Equation Modeling
- EDP 380C.16 Hierarchical Linear Modeling
- EDP 380C.18 Applied Bayesian Analysis
- EDP 380C.22 Analysis of Categorical Data
- EDP 380C.23 Missing Data Analysis
- EDP 380D.6 Program Evaluation Models & Techniques
- EDP 380D.8 Item Response Theory
- EDP 380D.11 Computer Adaptive Testing
- EDP 380D.14 Applied Psychometrics
- EDP 380N.11 Machine Learning for Applied Research
- EDP 381C.12 Meta-Analysis
- EDP 381C.14 Causal Inference
- EDP 381E Advanced Item Response Theory
- EDP 381D Advanced Statistical Modeling
Out-of-Specialization Courses (6 Credit Hours)
Out-of-specialization courses give you the chance to explore your interests beyond the QM program and connect with faculty across campus. You’ll choose two courses in consultation with the QM program advisor.
- One course from another EDP program area or another department at UT Austin.
- One course from another department at UT Austin.
Faculty
Interested in statistical models with a focus on deriving and evaluating multilevel model extensions and meta-analysis models for educational, behavioral, social and medical science data.
Statistical methods and techniques for meta-analysis with a focus on selective reporting and publication bias, effect size calculations, multilevel modeling
Interests include the development and dissemination of computerized adaptive testing applications in educational and psychological testing and patient-reported outcome measurements.
Accepting new students
Studies statistical and computational methods for analyzing psychometric data. Research areas include item response theory, response time modeling, cognitively diagnostic models, computer-adaptive testing, and multimodal analytics of cognitive and be...
Statistical methods for causal mediation analysis, causal inference with machine learning methods, longitudinal or clustered data analysis, study design (e.g., statistical power analysis)
Accepting new students
My principal methodological research interest deals with the various facets of model specification, including, but not limited to, model comparison/selection and model modification methods. With the use of simulation techniques, I examine the perform...
Accepting new students
Additional Information
