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The purpose of the M.Ed. in Quantitative Analytics is to prepare students to use rigorous data analysis to inform and improve research, management, and policy decisions in education and the social sciences.
The 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. Students in the program will gain proficiency in: (1) managing and analyzing data, (2) constructing and interpreting research reports, (3) developing an understanding of measurement, survey, and research design, and (4) communicating analytic results and interpretations to broad audiences. Students will gain proficiency in these areas through course-based applications of these tools to actual research problems in education and the social sciences.
The Master of Education in Quantitative Analytics in Education and the Social Sciences is a 30-credit hour degree program. An independent capstone study or internship is a required component of the 30 credit hours.
The curriculum is designed to provide students with a core foundation in advanced quantitative research and analysis in education and the social sciences. Students who need to acquire prerequisite statistical knowledge will complete the foundational course EDLF 5330 – Quantitative Methods and Data Analysis I or similar, before beginning the program.
The curriculum covers the core components of quantitative research design, analysis and assessment, with a focus on large sets of education and demographic data. Required courses will train students to analyze and understand large sets of data through course-based application of key analytics tools and software to real-world research problems. Throughout the core curriculum, course materials and class discussions will situate research questions and designs in an educational context, preparing students to understand and incorporate the needs of districts, schools, and educators in their analyses and recommendations. Students will be exposed to a variety of theoretical and philosophical conceptions of research designs, as well as readings and examples of various designs – e.g. cross-lagged designs, randomized control trials, and collecting survey data. Students will learn to evaluate and apply suitable measurement, survey, and research designs, including tests and surveys. Coursework will educate students in the creation and management of large databases and prepare them to draft and interpret research reports and communicate analytics results and insights to broad audiences. The core curriculum will provide multiple opportunities for students to present their research findings in written and verbal forms, including presenting the results of their work in standard journal and popular press formats.
Restricted electives will provide students with additional breadth and depth in quantitative research and analysis, education policy, and assessment. A limited number of qualitative methods courses will be offered for students who want to incorporate these skills into their program of study. Students will select restricted electives in consultation with a faculty advisor, allowing them to pursue their interests and tailor the degree program to their personal career goals.
The required capstone will provide students with the opportunity to engage in research or an internship study, through which they will integrate and apply their knowledge and skill in quantitative research design, assessment and analysis, and present their findings in a final paper.
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)
Total: 30 credit hours
Description of Capstone Experience
Option 1: The student will work closely with a research mentor on original research or comprehensive literature review on a methodological topics that is of interest to the student. This option is similar to writing a thesis, but on a smaller scale (i.e., typically 30 pages in length). The final paper should be about the same length as a manuscript written for a scholarly journal. If conducting original research, the student must make a significant contribution to the work, as determined by their research mentor, though the student need not be the sole contributor (i.e., lead author).
Option 2: The student will obtain an internship with an organization in which they can obtain practical experience in data analytics. A written agreement between the student, the advisor, and the sponsoring organization must be in place prior to beginning the internship experience. This agreement will document the relevance of the experience to data analytics and must be approved by all parties.