What Courses will I take for the RMME Online Master’s Degree?

Within UConn's Research Methods, Measurement, & Evaluation (RMME) Online Master's Degree, you'll concentrate on many of today's most pivotal research competencies and skills within a number of online and interactive courses. While all courses are 100% online and asynchronous, there are many opportunities to interact with your classmates and faculty via presentations, written response to one another, online meeting sessions and virtual office hours.

RMME Online Master's Degree students will earn their degree with 18 credits of required core courses and 12 credits of elective courses (30 credits in all).

Research Methods, Measurement, & Evaluation (RMME) Master's Degree Online Core Course Calendar

RMME CORE COURSES (Take all 6 Courses) FALL SPRING SUMMER
EPSY 5601: Principles and Methods in Educational Research X X
EPSY 5602: Educational Tests and Measurements X X
EPSY 5605: Quantitative Methods in Research I X X X
EPSY 5607: Quantitative Methods in Research II X X
EPSY 6601: Methods and Techniques of Educational Research X
EPSY 6621: Program Evaluation X
EPSY ELECTIVE COURSES (Select 4 Elective Courses) FALL SPRING SUMMER
GRAD 5950: Master’s Thesis Research X X X
EPSY 5610: Applied Regression X X
EPSY 6623: Advanced Program Evaluation X
EPSY 5195: Evaluation Practicum X
EPSY 5510: Learning: Its Implications for Education X

OTHER ELECTIVE COURSE OPTIONS
PP 5377: Qualitative Methods in Public Policy Offered at the discretion of UConn's School of Public Policy
PP 5379: Principles and Methods of Survey Research Offered at the discretion of UConn's School of Public Policy
GEOG 5150: Visualization in Geographic Information Systems Offered at the discretion of UConn's Department of Geography
GEOG 5500: Fundamentals of Geographic Information Science Offered at the discretion of UConn's Department of Geography
GEOG 5510: Applications of Geographic Information Systems Offered at the discretion of UConn's Department of Geography
GEOG 5512: Introduction to Spatial Data Science Offered at the discretion of UConn's Department of Geography

Note: Other elective classes approved by the student's academic advisor are acceptable.

Research Methods, Measurement, & Evaluation (RMME) Master's Degree Course Descriptions

RMME CORE COURSES (18 Credits - Take all 6 Courses)

EPSY 5601: Principles and Methods in Educational Research

EPSY 5601 provides introductory-level coverage of the theory and practice of research with primary application to K-12 settings. The goal of the course is to help students understand, evaluate, and make use of educational research and literature. Therefore, students will learn the basic concepts, procedures, and habits of mind for conducting and evaluating educational research and will become better producers and consumers of research. Students will learn to distinguish between spot good and bad science, helpful and unhelpful theory, strong and weak instruments, etc. Students successfully completing this course will be able to: (1) Describe and recognize the major types of quantitative and qualitative research; (2) Recognize the connection between research questions, research design and analysis (3) Explain measurement concepts in quantitative and qualitative research; (4) Understand descriptive and inferential statistical concepts and techniques used with quantitative data, and analysis concepts and techniques used with qualitative data; and (5) Locate, classify, synthesize, and evaluate published research. Topics for this course may include: basics of the educational research process, identification of research problems, formulation of research questions, research ethics, literature reviews, qualitative research methods, quantitative research methods, mixed methods, qualitative data collection and analysis, quantitative data collection and analysis, special topics in research methods, etc.

EPSY 5602: Educational Tests and Measurements

EPSY 5602 provides graduate students in education and other related fields with an overview of the concepts, procedures, and issues involved in testing, measurement, and assessment. Emphasis is on current developments in the field of measurement. Students successfully completing this course will be able to: (1) Apply the professional jargon of measurement, assessment, and testing; (2) Critically evaluate the quality of educational and psychological instruments; (3) Analyze reliability and validity evidence from test manuals, test reviews, and academic research; (4) Explain different measurement theories, including their underlying assumptions; (5) Explain which types of validity evidence are required for different test score interpretations; (6) Describe the process of test design and validation; and (7) Accurately interpret test scores and other psychometric indicators and estimates. Topics for this course may include: assessment purposes & measure types & formats; the process of test construction; cognitive items & performance assessments; non-cognitive/affective items, including item scaling and item response sets; Classical Test Theory; standard error of measurement; forms & types of reliability; generalizability theory; traditional forms of validity; current conceptualization of validity; types of validity evidence & applications; classification accuracy, sensitivity, & specificity; standard setting; standardized scores & score interpretations; norming tests; linking & equating; item difficulty & discrimination; bias & fairness in testing; ethical guidelines for test users; questionable measurement practices, etc.

EPSY 5605: Quantitative Methods in Research I

EPSY 5605 is the first course in the RMME quantitative methods course sequence. EPSY 5605 introduces foundational concepts/skills in quantitative methods, with illustrations and examples from educational research. Students are routinely encouraged to share applications of course content from their “home” disciplines or personal research experiences to illustrate the cross-disciplinary nature of quantitative methods. Students successfully completing this course will be able to: (1) Use statistical terminology appropriately to describe general principles of statistical analysis and inference; (2) Construct tabular and graphical displays to summarize a given set of data; (3) Identify and calculate appropriate descriptive statistics for variables in a given data set; (4) Construct scatter plots, calculate measures of bivariate relationship, and perform simple linear regressions for variables in a given data set; (5) Identify the parameters of interest in a given research context and select an appropriate statistical procedure for answering the research questions; (6) Construct and correctly interpret confidence intervals for the mean, proportion, correlation, difference between means, and difference between proportions; (7) State and test hypotheses about the mean, proportion, correlation, difference between means, and difference between proportions; (8) State and test hypotheses about bivariate relationships among variables using simple linear regression procedures and chi-square tests of association; and (9) Draw clear and correctly stated conclusions with respect to research questions of interest based on statistical analyses of a given set of data. Topics for this course may include: methods for displaying and summarizing data (frequency distributions, graphical displays, measures of central tendency and variability, measures of relative standing / percentiles, correlation, linear regression); probability and statistical inference (probability distributions, the normal distribution, sampling distributions for the mean and proportion); inference about a single population parameter (confidence interval for the mean and proportion, testing hypotheses about the mean and proportion); inference about the difference between two population parameters (inference about the difference between means / t-test for dependent and independent samples, inference about the difference between proportions / z-test for dependent and independent samples); inference about relationships (inference about correlations and regression slopes, chi-squared test of association); one-way analysis of variance, etc.

EPSY 5607:  Quantitative Methods in Research II

EPSY 5607 is the second course in the RMME quantitative methods course sequence. EPSY5607 provides students with an understanding of the models and analysis procedures necessary for carrying out quantitative research projects, with restriction to univariate procedures. Students successfully completing this course will be able to: (1) Select appropriate analysis of variance (ANOVA) and linear regression models and data analysis procedures for data from a given research design; (2) Perform univariate one-way, factorial, randomized-blocks, and repeated-measures ANOVAs; (3) Interpret the results of univariate one-way, factorial, randomized-blocks, and repeated-measures ANOVAs; (4) Test specified contrasts using Scheffé, Bonferroni, and Tukey multiple-comparison procedures; (5) Fit linear regression models with continuous predictors; (6) Perform a regression analysis with appropriately constructed dummy variables for categorical predictors; (7) Interpret the results of linear regression analyses with continuous and/or categorical predictors, including subset tests, regression coefficient values, t-statistics, tolerance values, and part and partial correlations for predictors in the model; (8) Perform and interpret the results of an ANCOVA analysis including checks of test assumptions, coefficient interpretations, and post-hoc tests; and (9) Understand how to run regression diagnostics to test model assumptions and identify outliers and other unusual observations. Topics for this course may include: one-way ANOVA; multiple comparisons; two-way ANOVA; randomized-blocks and repeated-measures designs; linear regression with continuous predictors; linear regression with categorical predictors; regression diagnostics, etc.

EPSY 6601: Methods and Techniques of Educational Research

EPSY 6601 offers an advanced survey of the principal methods employed in the investigation of educational problems, including problem formulation, stating hypotheses, sampling, instrument design, types of research methods and design principles. Students successfully completing this course will be able to: (1) Identify theory, concepts, and terminology pertinent to conducting quantitative educational research; (2) Describe a variety of experimental and quasi-experimental research designs; (3) Identify threats to validity for each of these research designs and propose strategies to minimize those threats; (4) Define a research problem of interest and generate appropriate research questions/hypotheses; (5) Select a quantitative research design to examine specific research questions/hypotheses and evaluate the adequacy of the chosen design; (6) Apply guidelines required for the protection of human subjects in research with identification of the role of the IRB in the protection of human subjects; (7) Evaluate and critique the results of research studies conducted by other researchers within the field of education; (8) Describe the principles of open science. Topics for this course may include: research questions and ethics, introduction to causality and the validity typology, statistical conclusion validity (including confidence intervals, effect sizes, and power), internal validity and randomized experiments, external validity, construct validity, quasi-experimental research designs, matching and propensity scores, regression discontinuity and interrupted time series research designs, mediation and moderation, practical issues in research (open science), etc.

EPSY 6621: Program Evaluation

EPSY 6621 is the first course in the RMME program evaluation course sequence. This course provides students with a basic understanding of evaluation. Students learn about fundamental evaluation topics and concepts in the areas of practice, theory, methods, and profession. Students successfully completing this course will be able to: (1) Explain the history and influences of evaluation in society; (2) Compare and contrast evaluation’s purposes and evaluators’ roles and activities; (3) Discuss theory, concepts, and vocabulary used in program evaluation; (4) Compare and contrast different theories pertinent to conducting program evaluation; (5) Apply basic evaluation practice methods; (6) Debate recent trends influencing the practice of evaluation. Topics for this course may include: ethics and integrity, defining evaluation, evaluation exemplars, history of program evaluation, types of theories used in evaluation practice, evaluation practice in action, evaluation theory (methods theorists, valuing theorists, use theorists), evaluator competencies, current debates in evaluation, etc.

 

RMME SAMPLE ELECTIVE COURSES (12-Credits - Select 4 Courses)

Note: Other classes approved by the student's academic advisor are acceptable.

GRAD 5950: Master’s Thesis Research

Students may opt to complete the Master’s Thesis as the culminating capstone experience for the RMME Master’s degree. (This is most common for students planning to pursue higher education at the doctoral level.) UConn’s Graduate School requires each of these students to register for nine (9) credits of GRAD 5950 - Master’s Thesis Research coursework overseen by the student’s academic advisor as they prepare their thesis project. Students in the RMME Master’s program generally do not complete all nine credits in a single semester.

EPSY 5610: Applied Regression

EPSY 5610 presents in-depth study of linear regression with one or more predictors. Students successfully completing this course will be able to: (1) Estimate linear regression models and interpret model parameters; (2) Make appropriate inferences about population parameters based on linear models; (3) Evaluate model fit and the validity of inferences using diagnostic criteria; (4) Conduct analyses and graphically represent results; and (5) Communicate results of regression analyses for informed audiences. Topics for this course may include: purposes of regression, variance/covariance, plotting data, one-predictor regression models, least squares estimators, residuals, centering of predictors, ANOVA decomposition, categorical predictors, model diagnostics, residual plots, inference in regression, sampling distributions, inference for slopes / points on line / new observations, statistical power, overestimating effects, reproducible science, logistic regression, categorical outcomes, matrix representation of regression, multiple predictors, Venn diagrams for variance, general linear F-test, partial correlation, adjusted R-squared, hierarchical regression, multicollinearity, partial plots, hidden outliers, leverage, influential points and residuals, DFBETAS, interactions, polynomial regression, model building, stepwise regression, missing data, causal inference, mediation, suppression effects, etc.

EPSY 6623: Advanced Program Evaluation

EPSY 6623 is the second course in the RMME program evaluation course sequence. In this course, students will build upon the foundational knowledge and skills on ESPY 6621 and transition from learning about evaluation to planning evaluations. Students gain deeper understanding in four areas related to evaluation: program context, evaluators, evaluation methods, and research on evaluation. Students successfully completing this course will be able to: (1) Develop evaluations that maximize the likelihood of their use; (2) Choose appropriate evaluation designs for various programs, processes, systems, organizations, or products; (3) Develop a program logic model or program theory; (4) Choose the most appropriate data collection and analysis methods for specific evaluation studies; (5) Develop an evaluation plan outlining a major evaluation study; (6) Identify the political and contextual factors that affect the practice of evaluation; (7) Determine effective communication and reporting methods for disseminating evaluation information; and (8) Apply the standards and ethical practices of evaluators. Topics for this course may include: evaluator roles & competencies, politics & ethics of evaluation practice, focusing the evaluation & describing the evaluand, culture & evaluation, evaluation designs (choosing data collection methods), planning / implementing / managing / budgeting the evaluation, analyzing evaluation data, and communicating & reporting evaluation processes and findings, etc.

EPSY 5195: Evaluation Practicum

EPSY 5195 is the third (and final) course in the RMME program evaluation course sequence. In this course, students transition from planning evaluations (as in EPSY 6623) to conducting evaluations. Typically, Evaluation Practicum students carry out the evaluation they developed and proposed after participating in EPSY 6623. Students successfully completing this course will be able to: (1) Apply program evaluation foundations (e.g., standards, guidelines, principles, approaches, and theories); (2) Implement technical aspects of an evaluation (e.g., framing questions, designing studies, collecting and analyzing data, reporting findings); (3) Describe the unique circumstances and settings of evaluations, including key stakeholders; (4) Develop and execute logistical components of an evaluation (e.g., developing and monitoring work plans and timelines); and (5) Exhibit interpersonal evaluation competencies. Students who dually enroll in (i.e., students who have applied and been accepted to) both the RMME Master’s degree and the Graduate Certificate Program in Program Evaluation will complete a capstone experience through the Evaluation Practicum course (EPSY 5195). For these students, the Evaluation Practicum may also serve as the capstone experience for the RMME Master’s Degree program. If you are interested in learning more about this option, please contact your RMME academic advisor and/or Dr. Sarah D. Newton (sarah.newton@uconn.edu) as soon as possible to ensure adequate time for course planning!

EPSY 5510: Learning: Its Implications for Education

EPSY5510 presents the nature and types of learning, transfer of training, motivation, and nature of instructional outcomes, with particular attention to individual differences among elementary and secondary school students.

PP 5377: Qualitative Methods in Public Policy

PP 5377 provides instruction on the development and design of qualitative research.

PP 5379: Principles and Methods of Survey Research

PP 5379 explores the theory and practice of survey research, including sampling, questionnaire design, analysis, and reporting of results.

GEOG 5150: Visualization in Geographic Information Systems

GEOG 5150 encourages students to build skills in the design of spatial data displays and computer-generated maps.

GEOG 5500: Fundamentals of Geographic Information Science

GEOG 5500 presents an introduction to the theory and methods for representing, acquiring, storing, manipulating, displaying, and analyzing geographic features in relation to the surface of the earth.

GEOG 5510: Applications of Geographic Information Systems

GEOG 5510 details operational and management issues of geographic information systems (GIS) with emphasis on understanding GIS through use of software. Topics include the principal functional components of GIS including general GIS design and management theory, spatial and attribute data creation, database design and management, spatial analysis, cartographic production, and application design and implementation. Practical work includes analytical exercises using GIS culminating in an application project. [Prerequisite: GEOG 5500]

GEOG 5512: Introduction to Spatial Data Science

GEOG 5512 introduces the fundamentals of spatial data science. Students also learn how to apply a high-level programming language, R, for spatial data analysis, visualization, and modeling. [Prerequisite: GEOG 5500]

Additional RMME Master's Degree Requirements

The Graduate School at the University of Connecticut offers both Thesis (Plan A) and non-Thesis (Plan B) options for the completion of the Master’s degree. Most 100% online students choose the Plan B option. Both are described below.

  • Plan A (Thesis): Students must form a committee of three professors to oversee the thesis. Students interested in the thesis option are encouraged to approach their major advisor early in their academic program to discuss thesis requirements. Students are required to register for nine credits of GRAD 5950 - Master’s Thesis Research. Additionally, students must register for three additional credits (one standard course) of elective coursework (see list of possible elective classes above).
  • Plan B (non-Thesis): There are two main ways to complete the Plan B Master’s degree:
    • Option 1: Students must complete additional credits (four standard courses) of elective coursework (see list of possible elective classes above) AND successfully complete the RMME Master’s Degree comprehensive exam after completion of the coursework. Arrangements for completing the Master’s exam are made with the program advisor. The comprehensive exam covers content from the six core courses listed above.
    • Option 2: Students must complete the following classes: EPSY 6623 and EPSY 5195 AND 6 additional credits (two standard courses) of elective coursework. Since completion of EPSY 6623 and EPSY 5195 involve a capstone experience, the requirement to complete the comprehensive exam is waived for students completing this option. Furthermore, students electing this option are eligible to receive a Program Evaluation Certificate designation on their UConn diploma, in addition to the RMME Master's degree credential.

Note: Master's students t the University of Connecticut must maintain registration continuously each semester (except summer/winter sessions) until all requirements for the degree have been completed. Failure to maintain continuous registration will automatically result in the student being discontinued from the academic program. For information regarding continuous registration requirements, please contact: gradschool@uconn.edu.