DOCTORAL

Doctor of Philosophy in Educational Studies and Master of Arts in Statistics (Dual Degree)

Overview & Requirements
Applying
Careers & Internships

The interdisciplinary culture of the University of Michigan has generated strong connections between statisticians and quantitative social scientists in economics, education, psychology, and sociology. This provides an ideal training ground for educational statisticians.

Through the dual-degree Doctor of Philosophy in Educational Studies and Master of Arts in Statistics program you will have the opportunity to: 

  • Engage with the best thinking in applied statistics across a range of departments while applying your technical interests to challenging applied problems and methods in education. 
  • Develop an area of substantive interest in education (e.g., educational technology, literacy education, mathematics education), and explore research literatures in that field with an emphasis on uncovering the key methodological challenges that face researchers in it.
  • Gain groundwork in the mathematical foundations of applied statistics, including probability, statistical inference, linear models, and multivariate statistics; as well as applications to assessment, program evaluation, and survey research in education.
  • Work on high-quality research projects within the School of Education, which is conducting a number of well-funded large-scale evaluations of educational interventions as well as secondary analyses of survey data.

Candidates complete both the doctoral program requirements of the Educational Studies program and the dual-degree master's requirements of the Department of Statistics in the College of Literature, Science, and the Arts.

In addition, you will be encouraged to draw on multidisciplinary courses, colloquia, and seminars available at the University through programs including the Survey Methodology Program, the Quantitative Methodology Program, and the Social Statistics and Methodology Program.

A doctoral guidance committee formed in accord with the policies of the Educational Studies program and including at least one member of the Department of Statistics will provide information and advice to guide you in selecting among the vast resources of the university to advance your knowledge and research interests.

Students complete a minimum of 60 credits for the PhD and a minimum of 24 credits for the MA.

The Doctor of Philosophy in Educational Studies program includes core requirements in educational studies and qualitative research coupled with extensive coursework in quantitative research methods. Additional electives and cognates allow students to focus their studies.

STATS courses have pre-requisites. For a listing of STATS courses and their pre-requisites, please visit the Department of Statistics course listings.

Additional Certificate and Endorsement Opportunities
Learning Experience Design Certificate

Requirements

Total Credit Hours Required
84

PhD core credits

19

Students complete the following courses:

  • EDUC 790 – Foundations of Schooling (3 credits)
  • EDUC 791 – Foundations of Teaching & Learning (3 credits)
  • EDUC 792 – Methods of Educational Research: Qualitative (3 credits)
  • EDUC 793 – Introduction to Quantitative Methods in Educational Research (3 credits)
  • EDUC 898 – Professional Development Seminar (1 credit); need 4 credits total
  • An advanced research methods course (3 credits)

PhD concentration credits

12

Students choose from the following courses:

  • BIOSTAT 601 – Probability and Distribution Theory (4 credits)
  • BIOSTAT 602 – Biostatistical Inference (3 credits)
  • BIOSTAT 617 / SOC 717 / STATS 580 / SURVMETH 617 – Methods and Theory of Sample Design (3 credits)
  • BIOSTAT 652 – Design of Experiments (3 credits)
  • BIOSTAT 695 – Analysis of Categorical Data (3 credits)
  • BIOSTAT 851 / STATS 642 – Linear Statistical Models (3 credits)
  • BIOSTAT 890 / STATS 640 – Multivariate Statistical Models (3 credits)
  • EDUC 707 – Psychometric Theory: Classical and Latent Trait Models (3 credits)
  • EDUC 795 – Quantitative Methods for Non-Experimental Research (3 credits)
  • EDUC 803 / PSYCH 804 – Structural Equation Modeling (3 credits)
  • EDUC 890 / SURVMETH – 790 Multilevel Methods in Social Research (3 credits)
  • IOE 560 / OMS 603 / STATS 550 – Bayesian Decision Analysis (3 credits)
  • PSYCH 726 / SOC 726 – Multivariate Analysis (3 credits)
  • PSYCH 406 – Computational Methods in Statistics and Data Science
  • PUBPOL 712 / EDUC 712 – Causal Inference in Education Policy Research, K-12 (3 credits)
  • PUBPOL 713 / EDUC 714 – Causal Inference in Education Policy Research, Post-Secondary (3 credits)
  • STATS 425 / MATH 425 – Introduction to Probability (3 credits)
  • STATS 426 – Introduction to Theoretical Statistics (3 credits)
  • STATS 451 – Introduction to Bayesian Data Analysis (3 credits)
  • STATS 500 – Statistical Learning I: Regression (3 credits)
  • STATS 503 – Statistical Learning II: Multivariate Analysis (3 credits)

Any course that is an elective in the Master of Arts in Statistics program can count toward the concentration requirement.

PhD cognate credits

6

Students choose from the following courses:

  • STATS 500 – Statistical Learning I: Regression (3 credits)
  • STATS 503 – Statistical Learning II: Multivariate Analysis (3 credits)
  • BIOSTAT – 601 Probability and Distribution Theory (4 credits)
  • BIOSTAT – 602 Biostatistical Inference (3 credits)

PhD apprenticeship

2–6

Students complete a minimum of one and up to three credits in each of the following apprenticeships:

  • EDUC 789 – Research Apprenticeship
    • Graduate Student Research Assistant (GSRA) Position or Apprenticeship to Faculty Member
  • EDUC 798 – Teaching Apprenticeship
    • Graduate Student Instructor (GSI) Position or Apprenticeship to Faculty Member

Preliminary exam credits

2–6

Students complete a minimum of one and up to three credits in each of the following courses:

  • EDUC 991 – Prelims Part A (1 credit minimum) (may be elected more than once) 
  • EDUC 992 – Prelims Part B (1 credit minimum) (may be elected more than once)

MA core credits

6–8

Students choose one option from each of the following sequences:

Sequence #1:

  • STATS 500 – Statistical Learning I: Regression (3 credits) and STATS 503 – Statistical Learning II: Multivariate Analysis (4 credits)
  • STATS 600 – Linear Models (4 credits) and STATS 601 – Analysis of Multivariate and Categorical Data (4 credits)

Sequence #2:

  • BIOSTAT 601 – Probability and Distribution Theory (4 credits) and BIOSTAT 602 – Biostatistical Inference (4 credits)
  • STATS 610 – Statistical Inference (3 credits) and STATS 611 – Large Sample Theory (3 credits)

MA elective credits

6

Students complete two additional elective STATS courses, totaling at least 6 credits. These can be from graduate-level courses offered by the Department of Statistics, or other approved courses. All 600-level or above STATS courses can be used as electives, with the exception that students cannot use STATS 600 or 601 as electives if they have taken STATS 500 or 503; and they cannot use STATS 610 and 611 as electives if they have taken BIOSTAT 601 and 602.

Students choose from the following courses:

  • STATS 406 – Computational Methods in Statistics and Data Science 
  • STATS 414 – Topics Course (examples of previous offerings: Applied Survival Analysis, Bayesian Analysis) 
  • STATS 430 – Applied Probability 
  • STATS 451 – Introduction to Bayesian Data Analysis 
  • STATS 506 – Computational Methods and Tools in Statistics (3 credits)
  • STATS 509 – Statistics for Financial Data (3 credits)
  • STATS 526 – Discrete State Stochastic Processes (3 credits)
  • STATS 531 – Analysis of Time Series (3 credits)
  • STATS 535 – Reliability (3 credits)
  • STATS 547 – Probabilistic Modeling in Bioinformatics (3 credits)
  • STAT 551 – Bayesian Modeling and Computation 
  • STATS 560 – Introduction to Nonparametric Statistics (3 credits)
  • STATS 570 – Design of Experiments (3 credits)
  • STATS 580 – Methods and Theory of Sample Design (3 credits)
  • STATS 607 – Statistical Computing (1.5 credits)
  • BIOSTAT 615 – Statistical Computing 
  • BIOSTAT 675 – Survival Analysis 
  • BIOSTAT 682 – Applied Bayesian Inference 
  • BIOSTAT 695 – Analysis of Categorical Data 
  • BIOSTAT 696 – Spatial Statistics 
  • Any approved STATS 600-level or above courses

MA cognate credits

4

Students complete two courses from another department, totaling at least 4 credits. Courses taken to satisfy the PhD in Educational Studies program requirements can be double-counted and used to satisfy this requirement.

MA writing component

Since the dual degree is primarily designed for students who do a significant amount of statistics for their thesis research, the students in the program are required to have a PhD thesis chapter, or a thesis-based research paper submitted for publication, which demonstrates mastery of statistical methods at the level of a master’s project.

The writing component may focus on data collection (design of experiments, survey design), modeling and analysis of data, or both. It must be approved by a member of the student's PhD committee with an appointment in the Statistics department. This committee member will need to sign a form approving the statistical writing component, typically at the time of the defense. The student must also provide a two-page summary of the writing component, to be submitted with the signed approval form, describing the scientific problem under investigation, its importance, and statistical methods used.

If the student's thesis does not have a sufficient statistical component, this requirement may be replaced with two additional elective courses (at least 6 credit hours), making the total credit requirement equivalent to that of the master’s Program in Applied Statistics. Students are strongly advised to consult with the Statistics faculty member on their committee well in advance of the defense to determine the best course of action. Those who do not anticipate having a significant statistical component in their thesis should apply directly to the master’s Program in Applied Statistics.

Questions?

Questions?

Frequently Asked Questions

Prospective students

Prospective Students

2018 Ed Studies masters cohort poses in front of School of Education building
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Educational Studies

Associated Faculty

Marvin W. Peterson Collegiate Professor of Education; Professor of Public Policy, Gerald R Ford School of Public Policy; Evaluation Faculty Lead

(734) 647-1984

Professor, School of Education; Professor of Public Policy, Gerald R. Ford School of Public Policy; Professor, Department of Economics, College of Literature, Science, and the Arts

734-615-5113

Professor, School of Education; Walter H. Annenberg Professor of Education Policy, Gerald R. Ford School of Public Policy; Professor, Department of Economics, College of Literature, Science, and the Arts

734-615-6994

Professor of Education; Professor of Economics, College of Literature, Science, and the Arts; Professor of Public Policy, Gerald R Ford School of Public Policy

734-647-8366

Connect with ES

Contact

Phone: (734) 763-9497
Master's: edstudiesma.info@umich.edu 
Doctoral: edstudiesphd.info@umich.edu  

Location

610 E. University Avenue
Room 4218
Ann Arbor, Michigan 48109-1259

Office Hours

Monday–Friday
8:00 a.m.–5:00 p.m.

Application Deadline

Educational Studies is suspending admissions to its PhD program for the fall of 2021. We are doing this to ensure that we can continue to provide the support needed by our current students at this uncertain time. However, we are engaged in an exciting redesign of our PhD program and look forward to welcoming your applications in the fall of 2021 for the fall of 2022. Please check in (at this site) for updates regarding our redesign efforts.

Quick Facts

GRE general exam required

Yes

Financial aid available

Yes

Starting term

Fall term only

Connect with ES

Contact

Phone: (734) 763-9497
Master's: edstudiesma.info@umich.edu 
Doctoral: edstudiesphd.info@umich.edu  

Location

610 E. University Avenue
Room 4218
Ann Arbor, Michigan 48109-1259

Office Hours

Monday–Friday
8:00 a.m.–5:00 p.m.

Careers

Education researchers with strong backgrounds in statistics and quantitative methods are in enormous demand, in: 

  • Schools and colleges of education
  • Research organizations such as the Educational Testing Service, the Rand Corporation, the National Opinion Research Center
  • Government agencies

100%

of known graduates found full time employment in education

100%

of known graduates found employment within 12 months

$61K

average first year salary
Graduates typically go into these industries
  • Educational foundations and policy
  • Educational leadership
  • Mathematics education
  • Teaching and teacher education

 

Graduates often work as
  • Education Consultant
  • Director of Research
  • Professor
  • Research Design Specialist
  • Research Investigator
  • Research Specialist

Recent job titles include

  • Associate Director of Education Research
  • Chief of Research
  • Clinical Professor
  • Deputy Director
  • Director of Enrollment Research and Data Management
  • Director of Mathematics Learning Center
  • Instructional Consultant
  • Principal Researcher
  • Research Scientist

Internships

Although no internship is required, students must satisfy the apprenticeship requirement by completing a minimum of one and up to three credits in each of the following apprenticeships:

  • EDUC 789 – Research Apprenticeship
    • Graduate Student Instructor (GSI) Position or Apprenticeship to Faculty Member
  • EDUC 798 – Teaching Apprenticeship
    • Graduate Student Research Assistant (GSRA) Position or Apprenticeship to Faculty Member