Research, Measurement & EvaluationPh.D.
The RME Ph.D. Program is guided by the following vision and mission statements:
The vision of the RME program is to be a central part of “putting methods into practice” in the social sciences and/or education.
The mission of the RME Ph.D. program is to to provide students with the requisite training in the application of statistical and measurement methodologies to conduct original research in the fields of research and measurement methodology, and to serve as an expert in the areas of research design, measurement, statistics, and data analytics.
The Ph.D. in Research, Measurement, and Evaluation (RME) program provides in-depth knowledge of intermediate and advanced statistical and measurement methodologies and prepares scholars to make original and innovative contributions to the fields of research methodology in education, psychology, and social sciences.
The Ph.D. program in RME is designed to provide students with expertise in one or more specialized areas of measurement or statistical modeling and in-depth training in the intermediate and advanced statistical and measurement methodologies, as well as preparing individuals to make original contributions to the fields of measurement and pscyhometrics. The program aims to provide students with the necessary mentoring and experiences to produce innovative research that advances methodology used in education and social sciences. Students are encouraged to present research work in regional, national, and international conferences, and to publish their work in peer-reviewed journals. In this respect, students are being prepared to pursue influential positions in the field of research methodology, such as faculty positions at research-intensive universities and research scientist positions in research and testing organizations.
The Ph.D. program places an emphasis on gaining hands-on skills with respect to the design and analysis of quantitative research studies and conducting research that advances our knowledge and application of research methodology. Beginning as early as the second year of training, students are involved in applied field experiences and mentored apprenticeships in which they actively conduct research using statistical and measurement methodology under the supervision of RME faculty. Beginning in their third year of the program, students are encouraged to present original research at regional and national conferences and to publish in peer-reviewed journals.
Individuals completing the Ph.D. in RME have the skills needed to pursue faculty positions in quantitative methodology, to serve as consultants in large-scale research projects and evaluations, and to act as data analysts or research scientists in research or state agencies, private corporations, school districts, and non-profit organizations. Students focusing on measurement are particularly well-suited to work in the development and analysis of large-scale testing programs administered by testing companies (e.g., ETS, ACT, College Board, Pearson, etc.) and state educational accountability agencies.
Below is a list of positions our alumni have held upon their graduation in the past 10 years:
- Associate Professor of Research and Evaluation Methodology, College of Education, University of Florida
- Associate Professor, Department of Psychology, Middle Tennessee State University
- Assistant Professor, Department of Educational Professions, Frostburg State University
- Research Associate Professor at the School of Nursing and Health Studies at the University of Miami
- Assistant Director of Assessment, School of Pharmacy and Pharmaceutical Sciences, University of Buffalo
Requirements for Admission
All students entering the Ph.D. program are intended to be full-time students. A Master’s degree is not required for admission, but some students who have completed a relevant Master’s degree prior to entering the program are preferred. Qualified students with adequate training in applied mathematics, statistics, or quantitative research methodology may enter the Ph.D. program directly with a Bachelor’s degree. Although extensive experience with statistics is not required for admission, students must have successfully completed an introductory statistics (or equivalent) course to be considered for admission.
Admission to the RME doctoral program is based on the following:
- Official transcripts showing undergraduate and graduate GPAs. An undergraduate and graduate GPA of 3.0 or higher.
- GRE Verbal, Quantitative, and Writing Scores. The target minimum is a score of 300 combined on verbal and quantitative.
- Three letters of recommendation. At least two of the three letters of recommendation should include specific evaluation of the candidate’s potential to engage in scholarly research required for this program.
- A personal statement describing
- the personal characteristics relevant to training in educational and psychological research, measurement, and statistics,
- reasons for applying the program,
- previous research experience and ideas for developing a research program,
- fit with at least one RME faculty members’ research program and interest in working with in terms of background and interests,
- current resume,
- previous experience in educational and psychological research,
- and career goals upon graduation.
Applications are accepted for fall admission only. The number of admissions to the doctoral program depends on available student space in the program and faculty resources.
Completion of the Ph.D. requires a minimum of 66 graduate semester credit hours (72 graduate semester credit hours for students who do not hold a master degree), divided between a core set of required courses, a set of elective courses, and dissertation hours.
- Core Courses (36 credits, 12 courses of 3 credits each)
- EPS 700 Quantitative Methods I
- EPS 701 Introduction to Research Methods
- EPS 702 Quantitative Methods II
- EPS 703 Applied Multivariate Statistics
- EPS 704 Computer Applications in Educational and Behavioral Science Research
- EPS 705 Measurement and Psychometric Theory
- EPS 706 Categorical Data Analysis
- EPS 707 Item Response Theory
- EPS 708 An Introduction to Structural Equation Modeling for Multivariable Data
- EPS 709 Introduction to Multilevel Modeling
- EPS 710 Meta-analytic methods for research synthesis.
- EPS 711 Advanced Topics in Research, Measurement, and Evaluation
- Research Apprenticeship (6 credits)
For a minimum of 6 research apprenticeship credits, students work under the mentorship of RME faculty members (or approved faculty members outside of RME) on original studies pertinent to research, measurement, and evaluation. It is expected that the work completed during the apprenticeship culminates in a manuscript that is suitable for publication in an academic journal. The 6 credits of apprenticeship are documented as two 3-credit blocks of EPS799 and the research apprenticeship must be completed prior to the commencement of dissertation hours (EPS830).
- EPS 799 Advanced Individual Study
- Field Experience in Educational Research (6 credits)
Students must complete a minimum of 6 credits in field experience related to educational research. The field experience involves providing methodological assistance to a research or evaluation project at the University of Miami or other approved organization (e.g., the Assessment, Research, and Data Analysis division of Miami-Dade County Public Schools). The nature of the field experience must be approved by the student’s advisor prior to commencing the credit hours.
- EPS 712 Field Experience in Educational Research
- Elective courses (6 credits)
Students can take any course of their interest from other departments and schools across the University after they discuss with their academic advisor. University of Miami provides a wide range of courses that is potentially interesting for RME students. The following list includes some of these courses:
- EPS 714 Qualitative Methods I
- EPS 715 Qualitative Methods II: Case Studies and Grounded Theory
- EPS 716 Qualitative Methods II: Interviews and Content Analysis
- MAS 648: Machine Learning for Data Analytics I
- MAS 651: Machine Learning for Data Analytics II
- MAS 681: Statistical Machine Learning
- MAS 640: Applied time Series Analysis and Forecasting
- MAS 635: Design of Experiments
- BST 640: Modern Numerical Multivariate Methods
- BST 670: Bayes Data Analysis: Theory and Computing
- BST 675: Intermediate Probability
- BST 680: Advanced Statistical Theory
- Doctoral Qualifying Exam
Students must successfully pass the doctoral qualifying exam prior to the commencement of the doctoral dissertation.
- Dissertation Hours (12 credits
EPS 730 Pre-Candidacy Dissertation Research (6 credits)
EPS 740 Post-Candidacy Dissertation Research (6 credits)
All applicants who meet the admission requirement are automatically considered for a 100% tuition scholarship which covers up to 60 credits of course work plus an additional 12 credits of independent-research work. At the rate of 2019-20 rate of $2,100 per credit, this scholarship is worth at least $151,200. The number of tuition scholarships is limited every year and very competitive.
In addition to the tuition scholarship, the program makes every effort to provide funding for doctoral students through graduate and research assistantships. Graduate assistants receive stipend for 20 hours of work weekly during an academic year (i.e., equivalent to 9 months). Graduate and research assistantships are awarded to students who demonstrate good progress toward graduation and are subject to availability. Continuation of funding is not automatic; it is contingent on student performance in the assistantship. Inadequate performance can lead to the withdrawal of financial support.
A complete list of resources for obtaining other types of fellowships, scholarships, and loans can be found on the Graduate School website under Financial Aid and Funding Opportunities
Dr. A. Corinne Huggins-Manley graduated from the Research, Measurement, and Evaluation (RME) program at the University of Miami (UM) in 2012 with her Doctor of Philosophy degree. She has many fond memories of her studies at UM.
“The RME program consisted of a tight-knit group of faculty and graduate students, and the result was that I always felt supported and I always had research partners with whom I could collaborate and learn.”
Upon graduation, she was offered a position as Assistant Professor at the University of Florida, and she currently works as an Associate Professor at the University of Florida in the Research and Evaluation Methodology program in the College of Education. She continues to work and grow as a scholar in educational measurement. She has published over thirty peer reviewed articles, has secured large external grant funding in the education sciences, has taught a host of quantitative courses at the graduate level, and has worked as program coordinator for both graduate and undergraduate level major, minor, and specialization degrees.
“I thank all the RME faculty at UM for providing the foundations needed for my successful junior faculty career, and my continuing mid-level faculty career. Go Canes!”
After receiving my bachelor’s degree in Sociology, I decided to pursue a master’s degree in Research, Measurement, and Evaluation (RME). Near the end of the master’s program I made the decision to continue and pursue a PhD.
I was fortunate to have excellent mentoring throughout my graduate career. I was able to work on a number of grant projects and learned all about the entire grant process. I also had opportunities to consult on various projects which enhanced my analysis skills. I even had opportunities to teach and tutor other students.
I enjoyed the program due to the small cohort size and cohesive group- both students and faculty. I was part of a group that enjoyed working together in class and on projects, and also got along well outside of the classroom. I still keep in touch with many of my former classmates and professors from the program.
Today I am the Assistant Director of Assessment at the University at Buffalo School of Pharmacy and Pharmaceutical Sciences. I assist the School in collecting data and conducting evaluations in preparation for program accreditation. I enjoy my job as I am able to apply my measurement and evaluation skills to improve the learning outcomes of students I see every day. I have even assisted in obtaining grants to enhance and improve instruction.
Karina Gattamorta started her career as a Special Education teacher after completing her Bachelor’s Degree in Psychology and Special Education at the University of Miami. Shortly after, she decided to pursue graduate education. While teaching, she earned an EdS in School Psychology from Florida International University. While working as a school psychologist, she became interested in issues related to fairness in testing and decided to return to her Alma Matter for a PhD in Research, Measurement, and Evaluation.
Upon graduating, she was offered a faculty position at the School of Nursing and Health Studies (SONHS) at the University of Miami where she has been for the past 9 years. She is currently a Research Associate Professor where she teaches various courses in research methods, statistics, and measurement. At SONHS, she has developed expertise in health disparities and nursing education practices while continuing to examine issues related to measurement. In her current line of research, Dr. Gattamorta is examining the intersection of racial/ethinic minority and sexual minority status on mental and behavioral health among youth and young adults.