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Quantitative Analytics in Education and the Social Sciences: M.Ed. - Master of Education 

Quantitative Analytics in Education and the Social Sciences: M.Ed. - Master of Education
 at 
University of Virginia 
Overview

Preparing students for research roles as applied data scientists in settings that involve working with large data sets to address questions of substantive importance.

Duration

12 months

Total fee

15.25 Lakh

Mode of learning

Online

Official Website

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Course Level

UG Certificate

Quantitative Analytics in Education and the Social Sciences: M.Ed. - Master of Education
 at 
University of Virginia 
Highlights

  • Earn a Degree after completion
  • Capstone Experience / Research Internship
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Quantitative Analytics in Education and the Social Sciences: M.Ed. - Master of Education
 at 
University of Virginia 
Course details

Skills you will learn
Who should do this course?
  • For Current undergraduates looking to pursue more advanced training through completion of a doctorate degree in research methodology
  • For Existing professionals looking to complement their existing work
What are the course deliverables?
  • Students in the program will gain proficiency in:
  • managing and analyzing data
  • constructing and interpreting research reports
  • developing an understanding of measurement, survey, and research design
  • communicating analytic results and interpretations to broad audiences
More about this course
  • The M.Ed. in Quantitative Analytics and the Social Sciences program prepares students for research roles as applied data scientists in settings that involve working with large data sets to address questions of substantive importance
  • Graduates have been employed in school systems, state education departments, testing companies, research centers, and community colleges; and have been accepted into competitive doctoral programs
  • Components of the M.Ed. program include courses that focus on the fundamentals of research design and data analytics
  • The program also includes elective courses that allow students to acquire a deeper understanding of, and expertise in, specialized methods of inquiry and analytic tools

Quantitative Analytics in Education and the Social Sciences: M.Ed. - Master of Education
 at 
University of Virginia 
Curriculum

Core Courses: 12 credit hours

EDLF 5310 - Data Management for Social Science Research (3 credits)

EDLF 7403 - Survey Design and Instrument Construction (3 credits)

EDLF 7420 - Quantitative Methods II: General Linear Models (3 credits)

EDLF 7402 - Program Evaluation (3 credits) OR EDLF 7300 - Foundations of Educational Research (3 credits)

Advanced Modeling: 6-9 credit hours

EDLF 8310 - Generalized Linear Models (3 credits)

EDLF 8360 - Multilevel Modeling in Education Research (3 credits)

EDLF 8361 - Structural Equation Modeling (3 credits)

Capstone Experience/Research Internship

EDLF 5993 - Independent Study (3 credits) OR EDLF 5985 - Internship (3 credits)

Methodological Electives: 6-9 credit hours

EDLF 6080 - Education Policy (3 credits)

EDLF 7330 - Single-Subject Research (3 credits)

EDLF 7402 - Program Evaluation (3 credits)

EDLF 7404 - Qualitative Analysis (3 credits)

EDLF 7410 - Mixed Methods Research Design (3 credits)

EDLF 8311 - Design and Analysis of Field Experiments (3 credits)

EDLF 8315 - Causal Inference in Educational Policy Research (3 credits)

Faculty Icon

Quantitative Analytics in Education and the Social Sciences: M.Ed. - Master of Education
 at 
University of Virginia 
Faculty details

Bethany A. Bell, Chair, Department of Education Leadership, Foundations and Policy Associate Professor
Bethany A. Bell's multidisciplinary research agenda has three primary goals: (1) to expand the methodological knowledgebase of complex statistical procedures to advance health and education equity research, (2) to reduce health-related inequities, with a primary focus on nutrition-related outcomes, among disenfranchised populations locally and nationally, and (3) to make complex analyses more accessible to applied researchers. To achieve these goals, she conducts Monte Carlo simulation studies, develops SAS macros and “how to” papers, and examines social determinants of various health outcomes, using both primary and secondary data sources. Her newest area of research, in collaboration with colleagues from education foundations and sociology, focuses on developing new survey instruments to measure people’s experiences of race and racism as well as their responses to those experiences. By developing new ways to understand the embodiment of one’s race, they look to advance their understanding of the mechanisms that link race and health inequities. Her research has been recognized with awards and honors by both the American Education Research Association (Division D Early Career Award in Measurement and Research Methodology – Quantitative Methods and Statistical Theory) and the National Institutes of Health (Loan Repayment Program Award in Health Disparities Research, National Institute on Minority Health and Health Disparities.
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Timothy R. Konold, Professor Research, Statistics, & Evaluation Program Director
Tim Konold is a professor and the director of the research, statistics, and evaluation program; and holds faculty affiliations with the Center for the Advanced Study of Teaching and Learning (CASTL), the Virginia Education Science Training (VEST) program, and the Youth Violence Project (YVP). He has taught introductory and advanced graduate-level courses in quantitative methods at the University of Virginia for the past 25 years and has served as the senior psychometric consultant for the Chartered Financial Analyst (CFA) international testing program for 20 years. He has authored more than 100 peer-reviewed articles, book chapters, published tests, and technical reports on topics that seek to infuse contemporary quantitative methods into work that has direct implications for policy and the education of children and youth.
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Jim Soland, Assistant Professor
Jim Soland's research is situated at the intersection of educational and psychological measurement, practice, and policy. Particular areas of emphasis include understanding how measurement decisions impact estimates of treatment effects and psychological/social-emotional growth, as well as detecting and quantifying test/survey disengagement. His work has been featured by the Collaborative for Academic, Social, and Emotional Learning (CASEL), the Brookings Institute, and the New York Times.

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Quantitative Analytics in Education and the Social Sciences: M.Ed. - Master of Education
 at 
University of Virginia 
 
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Quantitative Analytics in Education and the Social Sciences: M.Ed. - Master of Education
 at 
University of Virginia 
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P.O. Box 400160,Charlottesville,Virginia 22904-4160
Charlottesville ( Virginia)

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