The Quantitative Method Master’s in Education program in the Department of Educational Psychology entails 30 credit hours and can typically be completed in two years over four consecutive long (fall and spring) semesters. The program is designed to provide the knowledge and technical skills in the areas of applied statistics, psychometrics, and program evaluation. This training prepares graduates for doctoral study in Quantitative Methods and other social science fields as well as for professional positions, such as statistical analyst or social science researcher in academic institutions, testing companies, state and federal agencies and school districts.
Semester Start: Fall
Deadline to Apply: January 10
GRE Required? No
Location: On Campus
Schedule: Part-time allowed
Length of Program: 6 semesters, 65 hours
Core Goals
Goal 1: Learn to plan and execute quantitative research, as well as analyze and evaluate the research carried out by others.
Goal 2: Acquire expertise in a variety of statistical techniques, including the computing skills needed to carry out analyses.
Goal 3: Learn to develop research designs and analysis strategies that are tailored to and appropriate for specific quantitative research questions, based on an understanding of the relationship between the design, the measures used and the relevant data analysis techniques.
Goal 4: Develop and understand the proper use and assessment of use of measurement instruments (surveys, questionnaires, etc.) that are appropriate for specific educational, psychological and social science research and evaluation purposes.
Required Coursework
Please note required coursework may vary from year to year. Current students should always defer to their Program of Work for course requirements and consult with their faculty advisor / Graduate Advisor for any needed clarifications.
Students in the Quantitative Methods master’s (MEd) program are required to complete:
- QM Program Courses,
- QM Program Electives, and
- Out-of-Specialization courses.
Student coursework may vary depending on prior graduate coursework and waivers. All required courses must be completed with a grade of at least B-.
Note: the first digit in a Course Number denotes the number of credit hours of the course. Example: EDP 480C Correlation & Regression Methods = 4 credit hours.
Foundation Courses (23 credit hours)
- Prerequisite Course: EDP 380C.2 Fundamental Statistics.
- EDP 480C.6 Statistical Analysis for Experimental Data
- EDP 380D.4 Psychometric Theory and Methods
- EDP 480C.4 Correlation and Regression Methods
- EDP 381C.2 Research Design and Methods for Psychology and Education
- EDP 380D.6 Program Evaluation Models and Techniques
- EDP 380C Data Exploration and Visualization in R
Program Electives (6 credit hours)
Two courses from the following (may include alternative QM program elective approved by Area Chair):
- EDP 380C.12 Survey of Multivariate Methods
- EDP 380C.14 Structural Equation Modeling
- EDP 380C.16 Hierarchical Linear Modeling
- EDP 380D.8 Item Response Theory
- EDP 380D.14 Applied Psychometrics
- EDP 381C.12 Meta-Analysis
- EDP 381C.14 Causal Inference
Out-of-Specialization Courses (6 credit hours)
In addition to foundation and program area requirements, students must complete the following additional coursework outside of their program area. These courses are an opportunity to enhance interests and form relationships with out-of-area faculty; course choices must be approved by the Quantitative Methods Area Chair.
- 1 course taken outside of the EDP department (3 hours)
- 1 course taken either outside of the EDP department or in another program area within EDP (Counseling Psychology, School Psychology, or Human Development, Culture, and Learning Science) (3 hours)
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.
Interests include the development and dissemination of computerized adaptive testing applications in educational and psychological testing and patient-reported outcome measurements.
Research interests focus on using Bayesian statistical methods to employ hierarchical linear modeling, specifically working with longitudinal and mediation data.
Statistical methods related to psychometrics, such as uni- and multi-dimensional item response theory, response time modeling, cognitively diagnostic assessment, and stochastic test design.
Focuses on mediation analysis, causal inference, and longitudinal data analysis.
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...
Additional Resources
Area Chair
Seung Choi
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