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Quantitative Methods in Education

Unpacking a paradox

Kyle Nickoderm inside the Education Sciences Building

Doctoral student Kyle Nickodem finds better ways to understand and communicate data about student achievement. Read more.

Solve problems in education through research

Discover solutions to issues in educational research, assessment, and program evaluation through measurement, evaluation, and statistics. Upon graduation, you'll be equipped to help inform educational policy, practice, and curriculum and—most importantly—help schools and students succeed.

Careers

  • Test publishing firms
  • Teaching and research at colleges and universities (Ph.D. only)
  • Research and evaluation centers
  • Public school systems
  • State departments of instruction
  • Private industry

See where recent QME students have found jobs

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How to apply

"Statistics is a really powerful tool for solving educational and social problems. A blend of theory-based and technical classes and ample opportunities for applied research were the main reasons I joined the QME program."

Youngsoon Kang headshot

Youngsoon KangPh.D. student | kangx373@umn.edu

Interests: Profile analysis, educational aspiration of American Indian students, multidimensional factors impacting school violence

Work: Youngsoon is working on a theoretical paper regarding profile analysis, which aims to show how the two statistics, level and pattern, serve as meaningful predictors under difficult conditions. In addition, she examines youth development construct scores and higher non-response rates, to develop Native American students’ profiles assets and predict their educational aspirations.

Degrees & coursework

Areas of emphasis

Your curriculum requirements will depend on the degree (M.A. or Ph.D.) and area of emphasis you choose.

Measurement - Theory and methods of measuring important variables in education, including: achievement, attitude, and specialized cognitive and noncognitive variables

Statistics education- Understanding of statistics education and skills needed to conduct research applied to the teaching and learning of statistics

Program evaluation -Effectiveness of educational programs and education-related human services using a variety of quantitative and qualitative techniques

Statistics -The development and study of statistical theories and methods of data analysis that support and enhance efforts to understand the complex interplay between educational processes and student, family, school and community factors with the goal of improving educational outcomes such as student achievement

Curriculum

M.A. curriculum (30-34 credits)

Ph.D. curriculum (72 credits)

Faculty & staff

Ernest Davenport headshot

Ernest DavenportDirector of graduate studies
lqr6576@umn.edu

  • Correlates of academic achievement (especially related to mathematics)
  • Mathematical artifacts of statistical procedures
Mark Davison headshot

Mark DavisonAmerican Guidance Service, Inc./John P. Yackel Professorship in Educational Assessment & Measurement
mld@umn.edu

  • Educational measurement
  • Scaling (particularly multidimensional scaling)
  • Assessment of growth in educational settings
Robert delMas headshot

Robert delMasCEHD Distinguished Teaching Award
delma001@umn.edu

  • Effects of activity-based approaches to teaching
Elizabeth Fry headshot

Elizabeth Fryfryxx069@umn.edu

  • Teaching and learning of statistics
  • Assessment development
  • Use of technology to teach concepts
  • Students' understanding of study design and scope of conclusions that can be made
Michael Harwell headshot

Michael HarwellActing program coordinator, CEHD Distinguished Teaching Award
harwe001@umn.edu

  • Meta-analytic effect sizes and tests that extract information from a pool of studies beyond traditional measures
  • The role and impact of conceptual and empirical models for socioeconomic status (SES)
  • Nonparametric estimators and tests for complex statistical models particularly linear (mixed) models that serve as competitors to normal-theory-based methods
Nidhi Kohli headshot

Nidhi Kohli kohli@umn.edu | Lab

  • Structural equation models
  • Finite mixture model
  • Longitudinal models, including latent growth curve models, mixed-effects models, and growth mixture models
  • Development and validation of new scales/instruments
Frances Lawrenz headshot

Frances LawrenzAssociate vice president for research, University of Minnesota
lawrenz@umn.edu

  • Science and mathematics program evaluation
  • Assessment instrument development
  • Quantitative and qualitative research methodologies
  • Mixed methods
Michael Rodriguez headshot

Michael RodriguezCampbell Leadership Chair in Education and Human Development, co-founding director of Educational Equity Resource Center
mcrdz9@umn.edu

  • Psychometric properties of tests
  • Effects of item formats
  • Use of constructed-response versus multiple choice
  • Applied measurement
  • Improving accessibility of assessment of students with disabilities and English language learners
Andy Zieffler headshot

Andy Zieffler zief0002@umn.edu

  • Teaching and learning of statistics
  • Measurement and assessment of statistics education research
  • Statistical computing and integration of computing into statistics curriculum