Quantitative and Mixed Methods Research Methodologies
The mission of the Quantitative and Mixed Methods Research Methodologies (QMRM) concentration of the Educational Studies Ph.D. program is to provide training with both breadth and depth in the understanding, application, and development of quantitative and/or mixed methods research methodologies. Simply stated, QMRM graduates aim to advance research approaches used for generating knowledge in the field of education and other social and behavioral sciences.
Quantitative and Mixed Methods Research Methodologies (QMRM):
- Is a concentration in the Educational Studies Ph.D. program
- Provides training in the understanding, application, and development of quantitative research (i.e., psychometrics, measurement, statistical procedures, and quantitative designs)
- Provides training in the understanding, application, and development of mixed methods research (i.e., the designs and procedures needed when researchers intentionally integrate quantitative and qualitative approaches)
The QMRM Concentration targets two types of students:
- Students who want to become research methodologists who specialize in the advancement of research methods
- Students who want to become applied researchers in a substantive area with a specialization in the application of a quantitative or mixed methods research approach
Graduate Students in the QMRM Concentration:
- Complete course work in study design, data collection, and data analysis as applied in research about real-world problems
- Learn about diverse approaches to research
- Aim to contribute to the fields of research methodology
- Pursue employment as a research expert (i.e., a research methodologist)
A research methodologist studies the advancement of research methods, develops and applies methods or methodologies to address complex research problems in different settings, and examines issues associated with the methods or methodologies of their expertise.
The QMRM Faculty are research methodologists that study issues such as:
- Causal inference and measurement methods within the context of multilevel and multidimensional settings, such as classrooms and schools (Dr. Benjamin M. Kelcey)
- Multivariate latent variable modeling, including Nonparametric and Bayesian approaches, in educational and psychological measurement and survey research (Dr. Youn Seon Lim)
- Designs, procedural issues, and contexts for the adoption and use of mixed methods research (Dr. Vicki L. Plano Clark)
- Implementing, evaluating and developing tools for data-driven decision making and research across a variety of disciplinary contexts (Dr. Christopher M. Swoboda)
QMRM Faculty Study Methodological Topics in the Areas of:
- Quantitative research design
- Measurement and psychometrics
- Statistical data analysis techniques
- Mixed methods research design
The Methods Used to Study Research Methods By QMRM Faculty and Students Include:
- Monte Carlo simulations to model the performance of statistical procedures
- Database reviews to examine how research methods are being applied in substantive research
- Empirical research on existing practices, such as how and why researchers use certain methods
- Empirical research on new tools to better understand the advantages and limitations of research tools
- Data analytics and large-scale database design to facilitate the collection, warehousing, organization, analysis, and dissemination of qualitative and quantitative data to facilitate institutional decision making
Research methodologists are generally very successful at obtaining employment after earning a Ph.D. degree.
The types of positions obtained by research methodologists include:
- Professors – Academic positions involve research, teaching, and service at colleges and universities
- Testing Professionals – Research positions at testing companies (such as ETS or College Board) involve the development, assessment, and refinement of tests and performance measures
- Evaluators – Evaluation positions within commercial and nonprofit agencies involve the design and implementation of needs, formative, and summative assessments
- Research Statisticians/Consultants – Research support statisticians/consultants within specialized research groups, such as a College of Nursing or a program of grant funding, focus on the development and implementation of research grants
- Business Statisticians/Consultants – Business statisticians/consultants within companies, such as Netflix or Facebook, focus on data mining to uncover important trends in consumption and productivity
- Institutional Researchers – Institutional researchers within higher education focus on empirically describing and analyzing the full spectrum of functions (educational, administrative, and support) at institutions to facilitate institutional decision making
- Assessment Coordinators – Assessment coordinators oversee the student and teacher assessment process within K-12 districts
Ben Kelcey
Professor of Quantitative Research Methodologies, CECH Educational Studies
3311B Teachers College
My research focuses on causal inference, machine learning, structural equation modeling (SEM) and (latent) measurement methods within the context of multilevel and multidimensional settings such as classrooms and schools.
With respect to causal inference, I develop and apply frameworks, principles and practices that support causal inferences while paying particular attention to how concomitant social and organizational structures further afford or constrain their feasibility and effectiveness. I develop methods for (quasi-) experimental design and statistical adjustments that enable researchers to mount focused, specific analogies of their observational studies to randomized experiments with emphasis on multilevel or clustered settings.
Within this scope, I also develop and apply machine learning and artificial intelligence methods for causal inference (e.g., targeted learning, double machine learning, variational autoencoders) and explainable AI (XAI). Methods that can supplement accurate prediction and pattern detection with robust causal explanations hold foundational value in developing theories and programs because they provide a systematic way to understand, build, test, accumulate and replicate evidence. My work in machine learning is generally situated within multilevel organizational structures where high dimensional, interactive, and nonlinear data and latent variables (e.g., through factor-based variational autoencoders) are common (e.g., classroom teaching).
Similarly, my work on structural equation and latent variable modeling develops methods for complex measurement and nesting structures, high dimensional settings, (approximate) measurement invariance, and nonlinear effects. This work has included, for example, machine learning with latent variables (e.g., variational autoencoders), n-level SEM, latent interactions, structural-after-measurement (SAM) and small sample (nonlinear) estimators and is also generally situated within multidimensional and multilevel contexts (e.g., complex cross-classified assessments of teaching and learning).
Statistically, these research areas unfold within the context of and development of machine learning methods, (quasi-) experimental design and analysis methods, and latent variable analysis methods. The confluence of these statistical foci take substantive root in understanding the extent to which teacher and school factors can intervene to promote more effective and contextually responsive classroom and school environments. This work has included the measurement and analysis of teachers' knowledge, instructional quality and strategies, teacher mental health and preparation, and student achievement to identify and explain profiles, pathways and practices (e.g., who teachers are, what teachers know, what teachers believe, perceive and experience, what teachers do) that produce student learning and how these profiles and practices vary across contexts.
With respect to causal inference, I develop and apply frameworks, principles and practices that support causal inferences while paying particular attention to how concomitant social and organizational structures further afford or constrain their feasibility and effectiveness. I develop methods for (quasi-) experimental design and statistical adjustments that enable researchers to mount focused, specific analogies of their observational studies to randomized experiments with emphasis on multilevel or clustered settings.
Within this scope, I also develop and apply machine learning and artificial intelligence methods for causal inference (e.g., targeted learning, double machine learning, variational autoencoders) and explainable AI (XAI). Methods that can supplement accurate prediction and pattern detection with robust causal explanations hold foundational value in developing theories and programs because they provide a systematic way to understand, build, test, accumulate and replicate evidence. My work in machine learning is generally situated within multilevel organizational structures where high dimensional, interactive, and nonlinear data and latent variables (e.g., through factor-based variational autoencoders) are common (e.g., classroom teaching).
Similarly, my work on structural equation and latent variable modeling develops methods for complex measurement and nesting structures, high dimensional settings, (approximate) measurement invariance, and nonlinear effects. This work has included, for example, machine learning with latent variables (e.g., variational autoencoders), n-level SEM, latent interactions, structural-after-measurement (SAM) and small sample (nonlinear) estimators and is also generally situated within multidimensional and multilevel contexts (e.g., complex cross-classified assessments of teaching and learning).
Statistically, these research areas unfold within the context of and development of machine learning methods, (quasi-) experimental design and analysis methods, and latent variable analysis methods. The confluence of these statistical foci take substantive root in understanding the extent to which teacher and school factors can intervene to promote more effective and contextually responsive classroom and school environments. This work has included the measurement and analysis of teachers' knowledge, instructional quality and strategies, teacher mental health and preparation, and student achievement to identify and explain profiles, pathways and practices (e.g., who teachers are, what teachers know, what teachers believe, perceive and experience, what teachers do) that produce student learning and how these profiles and practices vary across contexts.
Youn Seon Lim
Asst Professor (F2), CECH Educational Studies
Teachers College
Dr. Youn Seon Lim is an Associate Professor with Tenure in Measurement, Psychometrics, and Quantitative Research Methods in Educational Studies, School of Education at the Univeristy of Cincinnati. Youn Seon teaches courses in introductory and advanced statistics, measurement, and psychometrics. Her research concerns multivariate latent variable modeling, including Nonparametric and Bayesian approaches, in educational and psychological measurement and survey research. Youn Seon has published on algorithms for estimating the Q-matrix of cognitively diagnostic assessments, on Bayesian estimation routines for large multidimensional diagnostic classification models, on model-fit statistics, and on fitting nonparametric hierarchical diagnostic classification models to nested response patterns.
Prior to joining the faculty of the University of Cincinnati, Youn Seon was an Assistant Professor of Data Science and Biostatistics at the Donald and Barbara Zucker School of Medicine at Hofstra University/Northwell in Hempstead, NY. Youn Seon did postdocs in Prof. Fritz Drasgow’s Psychometric Laboratory at the University of Illinois, Urbana-Champaign, and at BIFIE in Salzburg, Austria. (BIFIE stands for "Bundesinstitut für Bildungsforschung, Innovation & Entwicklung des österreichischen Schulwesens" [Federal Agency for Educational Research, Innovation, and Development of the Austrian Schooling System]—BIFIE is kind of the Austrian equivalent to ETS.)
For Full Publications, please visit the Website.
Prior to joining the faculty of the University of Cincinnati, Youn Seon was an Assistant Professor of Data Science and Biostatistics at the Donald and Barbara Zucker School of Medicine at Hofstra University/Northwell in Hempstead, NY. Youn Seon did postdocs in Prof. Fritz Drasgow’s Psychometric Laboratory at the University of Illinois, Urbana-Champaign, and at BIFIE in Salzburg, Austria. (BIFIE stands for "Bundesinstitut für Bildungsforschung, Innovation & Entwicklung des österreichischen Schulwesens" [Federal Agency for Educational Research, Innovation, and Development of the Austrian Schooling System]—BIFIE is kind of the Austrian equivalent to ETS.)
For Full Publications, please visit the Website.
Vicki L. Plano Clark
Professor, CECH Educational Studies
638P Teachers College
Vicki L. Plano Clark is a professor in the Research Methods area of the School of Education. She advises students in the Quantitative and Mixed Methods Research Methodologies (QMRM) concentration of the Educational Studies doctoral program and the Applied Research Methods (ARM) track of the Educational Studies master's program.
As a methodologist specializing in mixed methods research, Dr. Plano Clark studies how researchers effectively integrate quantitative and qualitative approaches to address their research questions. Her scholarship focuses on delineating useful designs for conducting mixed methods research, examining procedural issues associated with these designs, and examining larger questions about the contexts for the adoption and use of mixed methods. She has written numerous books and articles in the field of mixed mehtods research. She was the founding Managing Editor for the Journal of Mixed Methods Research and currently serves as an Associate Editor. In 2011, she co-led the development of Best Practices for Mixed Methods in the Health Sciences for NIH's Office of Behavioral and Social Sciences Research. In 2012 she became a founding co-editor of the new Mixed Methods Research Series with Sage Publications. She recently served as Chair of the Mixed Methods Research Special Interest Group of the American Association of Educational Research (AERA).
As an applied research methodologist, Dr. Plano Clark also engages in research and evaluation projects on a wide array of topics such as the management of cancer pain, the identity development of STEM graduate students, the professional development of teachers of Chinese, and the well-being of rural low-income families.
Before joining the University of Cincinnati in 2012, Dr. Plano Clark was the director of the Office of Qualitative and Mixed Methods Research, a service and research unit that provided methodological support for proposal development and funded projects at the University of Nebraska–Lincoln (UNL). She also taught research methods courses in UNL's Educational Psychology department. Prior to that work, she spent 12 years developing innovative curricular materials for introductory physics as UNL's Physics Laboratory Manager.
As a methodologist specializing in mixed methods research, Dr. Plano Clark studies how researchers effectively integrate quantitative and qualitative approaches to address their research questions. Her scholarship focuses on delineating useful designs for conducting mixed methods research, examining procedural issues associated with these designs, and examining larger questions about the contexts for the adoption and use of mixed methods. She has written numerous books and articles in the field of mixed mehtods research. She was the founding Managing Editor for the Journal of Mixed Methods Research and currently serves as an Associate Editor. In 2011, she co-led the development of Best Practices for Mixed Methods in the Health Sciences for NIH's Office of Behavioral and Social Sciences Research. In 2012 she became a founding co-editor of the new Mixed Methods Research Series with Sage Publications. She recently served as Chair of the Mixed Methods Research Special Interest Group of the American Association of Educational Research (AERA).
As an applied research methodologist, Dr. Plano Clark also engages in research and evaluation projects on a wide array of topics such as the management of cancer pain, the identity development of STEM graduate students, the professional development of teachers of Chinese, and the well-being of rural low-income families.
Before joining the University of Cincinnati in 2012, Dr. Plano Clark was the director of the Office of Qualitative and Mixed Methods Research, a service and research unit that provided methodological support for proposal development and funded projects at the University of Nebraska–Lincoln (UNL). She also taught research methods courses in UNL's Educational Psychology department. Prior to that work, she spent 12 years developing innovative curricular materials for introductory physics as UNL's Physics Laboratory Manager.
Chris Swoboda
Professor of Research Methods, CECH Educational Studies
638Q Teachers College
As a highly collaborative applied quantitative research methodologist, I have focused my scholarship, teaching and leadership roles on implementing, evaluating and developing tools for data driven decision making and research across a variety of contexts (e.g., education, health, legal studies). I pride myself in both using rigorous methodological approaches to combat real problems and in making these tools accessible to all learners.
Lori A. Foote
Asst Professor - Educator (F2), CECH Educational Studies
610J Teachers College
As a public elementary teacher-turned-researcher, my scholarship centers on practical issues of students' access to instruction and support at the district and school level. Due to the building nature of mathematics, I am particularly interested in students' access to high-quality mathematics instruction in the middle-to-upper elementary grades. I define high-quality mathematics instruction as practices that bring together the multiple strands of mathematics proficiency alongside schools' designs to provide assistance to students who struggle. Relatedly, when students struggle in math - briefly or in a more ongoing basis, I investigate the processes used in schools to provide assistance. I am interested in cross-contextual avenues to success in mathematics, particularly the ways that schools in different settings must negotiate best practices to improve mathematics learning outcomes for their students.
Alongside my research related to equitable mathematics practices in schools, I am interested in the ways that researchers used methodological approaches to investigate their research questions. I have investigated the ways that mixed methods approaches to research may be leveraged to benefit the scope and depth of a project. I have used mixed methods case study in its comparative case form to investigate contextualized mathematics practices in my research. As a result, I have also published several methodological articles and chapters related to the use of mixed methods case study.
Alongside my research related to equitable mathematics practices in schools, I am interested in the ways that researchers used methodological approaches to investigate their research questions. I have investigated the ways that mixed methods approaches to research may be leveraged to benefit the scope and depth of a project. I have used mixed methods case study in its comparative case form to investigate contextualized mathematics practices in my research. As a result, I have also published several methodological articles and chapters related to the use of mixed methods case study.