quantitative phd program

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Quantitative methods, doctor of philosophy (ph.d.), you are here, a doctoral program focused on measurement and evaluation that trains students to create new research methodologies and design empirical data analyses. .

The Quantitative Methods Ph.D. program is designed to prepare future professors at research universities and principal investigators at research and assessment organizations in education, psychology, and related human services fields.

What Sets Us Apart

About the program.

Rigorous coursework across the field of education will prepare students with the tools needed to conduct cutting-edge research and assessment.

Fall: 4 courses; Spring: 4 courses

Research apprenticeship Yes

Culminating experience Dissertation

The Ph.D. program in Quantitative Methods is designed to prepare students for faculty positions at universities as well as important responsibilities at research and assessment organizations. Graduates will be prepared to design first-rate empirical research and data analyses and to contribute to the development of new research methodologies. Students who apply directly to the doctoral-level study program following a baccalaureate degree will enroll in the core courses described for the M.S.Ed. degree in Statistics, Measurement, Assessment, and Technology (SMART) and the more advanced courses for the Ph.D. degree. This will include the development of independent empirical research projects.

Doctoral degree studies include advanced graduate coursework, a research apprenticeship, a Ph.D. Candidacy Examination, and the completion of a doctoral dissertation that represents an independent and significant contribution to knowledge. The research apprenticeship provides students with an opportunity to collaborate with a faculty sponsor on an ongoing basis and to participate in field research leading to a dissertation.

For information about courses and requirements, visit the Quantitative Methods Ph.D. program in the University Catalog .

Our Faculty

Affiliated Faculty

Eric T. Bradlow K.P. Chao Professor, The Wharton School Ph.D., Harvard University

Timothy Victor Adjunct Associate Professor, Penn GSE

"Penn GSE’s Quantitative Methods Ph.D. program equipped me with the methodological skills to do impactful applied education research as soon as I graduated."

Anna Rhoad-Drogalis

Our graduates.

Graduates go on to careers as university professors, researchers and psyshometricians for government agencies, foundations, nonprofits organizations, and corporations.

Alumni Careers

Assistant Professor, Texas A&M University-Corpus Christi
Associate Director, Bristol-Myers Squibb
Lead Psychometrician, American Institute of Certified Public Accountants
Research Analyst, Penn Child Research Center, University of Pennsylvania
Senior Director, Educational Testing Service
Senior Researcher, Mathematica

Admissions & Financial Aid

Please visit our Admissions and Financial Aid pages for specific information on the application requirements , as well as information on tuition, fees, financial aid, scholarships, and fellowships.

Graduate School of Education University of Pennsylvania 3700 Walnut Street Philadelphia, PA 19104 (215) 898-6415 [email protected] [email protected]

Christine P. Lee Program Manager (215) 898-0505 [email protected]

Please view information from our Admissions and Financial Aid Office for specific information on the cost of this program.

All Ph.D. students are guaranteed a full scholarship for their first four years of study, as well as a stipend and student health insurance. Penn GSE is committed to making your graduate education affordable, and we offer generous scholarships, fellowships, and assistantships.

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Graduate School

Quantitative and Computational Biology

General information, program offerings:, department for program:, director of graduate studies:, graduate program administrator:.

The Program in Quantitative and Computational Biology (QCB) is intended to facilitate graduate education at Princeton at the interface of biology and the more quantitative sciences and computation. Administered from The Lewis-Sigler Institute for Integrative Genomics, QCB is a collaboration in multidisciplinary graduate education among faculty in the Institute and the Departments of Chemistry, Computer Science, Ecology and Evolutionary Biology, Molecular Biology, and Physics. The program covers the fields of genomics, computational biology, systems biology, evolutionary and population genomics, statistical genetics, and metabolomics and proteomics.

Program Highlights

An Outstanding Tradition: Chartered in 1746, Princeton University has long been considered among the world’s most outstanding institutions of higher education, with particular strength in mathematics and the quantitative sciences. Building upon the legacies of greats such as Turing, von Neumann, Tukey, Compton, Feynman, and Einstein, Princeton established the Lewis-Sigler Institute of Integrative Genomics in 1999 to carry this tradition of quantitative science into the realm of biology.

World Class Research: The Lewis-Sigler Institute and the QCB program focus on attacking problems of great fundamental significance using a mixture of theory, computation, and experimentation.

World Class Faculty: The research efforts are led by the QCB program’s 50+ faculty, who include a Nobel Laureate, members of the National Academy of Sciences, Howard Hughes Investigators, and numerous faculty who have received major national research awards (e.g., NIH Pioneer, NIH Innovator, Packard, NSF PECASE, NSF CAREER, etc.).

Personalized Education: A hallmark of any Princeton education is personal attention. The QCB program is no exception. Lab sizes are generally modest, typically 6 – 16 researchers, and all students have extensive direct contact with their faculty mentors. Many students choose to work at the interface of two different labs, enabling them to build close intellectual relationships with multiple principal investigators.

Stimulating Environment: The physical heart of the QCB program is the Carl Icahn Laboratory, an architectural landmark located adjacent to biology, chemistry, physics, and mathematics on Princeton’s main campus. Students have access to a wealth of resources, both intellectual and tangible, such as world-leading capabilities in DNA sequencing, mass spectrometry, and microscopy. They also benefit from the friendly atmosphere of the program, which includes tea and cookies every afternoon. When not busy doing science, students can partake in an active campus social scene and world class arts and theater events on campus.

Program Offerings

Program offering: ph.d..

Core courses, QCB515, QCB537, QCB538, and COS/QCB551, are required for all students, as is a Responsible Conduct in Research (RCR) course, QCB 501. Three elective courses must be taken from the list below, including at least one from the quantitative course list and one from the biological course list. Courses not on the approved lists may be taken as electives with approval from the DGS.

Quantitative Courses (must take at least one)

APC 524 /MAE 506/AST 506 Software Engineering for Scientific Computing
CBE 517 Soft Matter Mechanics: Fundamentals & Applications
CHM 503/CBE 524/MSE 514 Introduction to Statistical Mechanics
CHM 515 Biophysical Chemistry I
CHM 516 Biophysical Chemistry II
CHM 542 Principles of Macromolecular Structure: Protein Folding, Structure, Design
COS 513 Foundations of Probabilistic Modeling
COS 524/COS 424 Fundamentals of Machine Learning
COS 597F Advanced Topics in Computer Sci: Computational Biology of Single Cells
COS 597G Advanced Topics in Computer Sci: Understanding Large Language Models
COS 597O Advanced Topics in Computer Science: Deep Generative Models: Methods, Applications & Societal Considerations
ELE 535 Machine Learning and Pattern Recognition
MAE 567/CBE 568 Crowd Control: Understanding and Manipulating Collective Behaviors and Swarm Dynamics
MSE 504/CHM 560/PHY 512/CBE 520 Monte Carlo and Molecular Dynamics Simulation in Statistical Physics & Materials Science
NEU 437/537 Computational Neuroscience
NEU 501 Cellular and Circuits Neuroscience
COS 511 Theoretical Machine Learning
COS 557 Artificial Intelligence for Precision Health
COS 597D Advanced Topics in Computer Science: Advanced Computational Genomics
MAE 550/MSE 560 Lessons from Biology to Engineer Tiny Devices
MAT 586/APC 511/MOL 511/QCB 513 Comp Methods in Cryo-Electron Microscopy
MOL 518 Quantitative Methods in Cell and Molecular Biology
NEU 560 Statistical Modeling and Analysis of Neural Data
ORF 524 Statistical Theory and Methods
PHY 561/56 2 Biophysics
QCB 505/PHY 555 Topics in Biophysics and Quantitative Biology
QCB 508 Foundations of Statistical Genomics

Biological Courses (must take at least one)

CHM 403 Advanced Organic Chemistry
CHM/QCB 541 Chemical Biology II
EEB 504 Fundamental Concepts in Ecology, Evolution, and Behavior II
EEB 522 Colloquium on the Biology of Populations
MAE 566 Biomechanics and Biomaterials: From Cells to Organisms
MAE 567/CBE 568 Crowd Control: Understanding and Manipulating Collective Behaviors and Swarm Dynamics
MOL 504 Cellular Biochemistry
MOL 506 Cell Biology and Development
MOL 518 Quantitative Methods in Cell and Molecular Biology
MOL 521 Systems Microbiology and Immunology
MOL 523 Molecular Basis of Cancer
MOL 559 Viruses: Strategy & Tactics
QCB 490 Molecular Mechanisms of Longevity
QCB 535 Biological networks across scales: Open problems and research methods of systems biology
QCB 570 Biochemistry of Physiology and Disease

Selected undergraduate courses of interest (Note: these do not count towards course requirements)

APC 350 Introduction in Differential Equations
CBE 448 Introduction to Nonlinear Dynamics
COS 226 Algorithms and Data Structures
EEB 324 Theoretical Ecology
MOL/QCB 485 Mathematical Models in Biology
ORF/MAT 309/380 Probability and Stochastic Systems
ORF 406 Statistical Design of Experiments
QCB 302 Research Topics in QCB
QCB 311 Genomics

Additional pre-generals requirements

Research Colloquium: QCB Graduate Colloquium QCB Graduate Colloquium is a research colloquium that has been developed for QCB graduate students, held weekly on an afternoon during the fall and spring terms. First, second, and fourth-year graduate students have the opportunity to present their research to peers.

Rotations All students are required to complete a minimum of three research rotations during their first year of graduate study, with a maximum of four, to explore possible research advisers.

General exam

The general examination is usually taken in January of the second year, and consists of an 8-10 page written thesis proposal and a two-hour oral exam on the student’s thesis proposal.

Qualifying for the M.A.

The Master of Arts (M.A.) degree is normally an incidental degree on the way to a full Ph.D. and is earned after a student successfully passes the general examination. It may also be awarded to students who, for various reasons, leave the Ph.D. program, provided the student has completed all coursework, pre-generals requirements, and the written portion of the generals examination.

A student must teach a minimum of one full-time assignment or teach two part-time assignments. Students will typically teach in year 4 of the program.

Post-Generals requirements

Committee Meetings Research progress is overseen by a thesis committee selected by the student after passing the general exam. The committee consists of the thesis adviser(s) and two additional faculty members. At least one member must be QCB faculty. The thesis committee must be approved by the DGS. Annual thesis committee meetings are mandatory.

Dissertation and FPO

The dissertation and final public oral exam (FPO) are required for all Ph.D. students. All students must write and successfully defend their dissertation according to Graduate School rules and requirements.

Ned S. Wingreen

Director of Graduate Studies

Executive committee.

Brittany Adamson, Molecular Biology
Joshua Akey, Integrative Genomics
Julien F. Ayroles, Ecology & Evolutionary Biology
William Bialek, Physics
Michelle M. Chan, Molecular Biology
Thomas Gregor, Physics
Sarah D. Kocher, Ecology & Evolutionary Biology
Michael S. Levine, Molecular Biology
Coleen T. Murphy, Molecular Biology
Yuri Pritykin, Computer Science
Joshua D. Rabinowitz, Chemistry
Joshua W. Shaevitz, Physics
Stanislav Y. Shvartsman, Chemical and Biological Eng
Mona Singh, Computer Science
Michael A. Skinnider, Integrative Genomics
John D. Storey, Integrative Genomics
Olga G. Troyanskaya, Computer Science
Ned S. Wingreen, Molecular Biology
Martin Helmut Wühr, Molecular Biology

Associated Faculty

Mohamed S. Abou Donia, Molecular Biology
Robert H. Austin, Physics
Bonnie L. Bassler, Molecular Biology
Clifford P. Brangwynne, Chemical and Biological Eng
Mark P. Brynildsen, Chemical and Biological Eng
Daniel J. Cohen, Mechanical & Aerospace Eng
Ileana M. Cristea, Molecular Biology
Danelle Devenport, Molecular Biology
Adji Bousso Dieng, Computer Science
Tatiana Engel, Princeton Neuroscience Inst
Jianqing Fan, Oper Res and Financial Eng
Elizabeth R. Gavis, Molecular Biology
Zemer Gitai, Molecular Biology
Frederick M. Hughson, Molecular Biology
Martin C. Jonikas, Molecular Biology
Yibin Kang, Molecular Biology
Andrej Kosmrlj, Mechanical & Aerospace Eng
Fenna Krienen, Princeton Neuroscience Inst
Andrew M. Leifer, Physics
Simon A. Levin, Ecology & Evolutionary Biology
Jonathan M. Levine, Ecology & Evolutionary Biology
Lindy McBride, Ecology & Evolutionary Biology
Tom Muir, Chemistry
Mala Murthy, Princeton Neuroscience Inst
Cameron A. Myhrvold, Molecular Biology
Celeste M. Nelson, Chemical and Biological Eng
Sabine Petry, Molecular Biology
Catherine Jensen Peña, Princeton Neuroscience Inst
Eszter Posfai, Molecular Biology
Ben Raphael, Computer Science
Mohammad R. Seyedsayamdost, Chemistry
Corina E. Tarnita, Ecology & Evolutionary Biology
Jared E. Toettcher, Molecular Biology
Samuel S. Wang, Princeton Neuroscience Inst
Haw Yang, Chemistry
Ellen Zhong, Computer Science

For a full list of faculty members and fellows please visit the department or program website.

Permanent Courses

Courses listed below are graduate-level courses that have been approved by the program’s faculty as well as the Curriculum Subcommittee of the Faculty Committee on the Graduate School as permanent course offerings. Permanent courses may be offered by the department or program on an ongoing basis, depending on curricular needs, scheduling requirements, and student interest. Not listed below are undergraduate courses and one-time-only graduate courses, which may be found for a specific term through the Registrar’s website. Also not listed are graduate-level independent reading and research courses, which may be approved by the Graduate School for individual students.

CHM 541 - Chemical Biology II (also QCB 541)

Cos 551 - introduction to genomics and computational molecular biology (also mol 551/qcb 551), cos 557 - artificial intelligence for precision health (also qcb 557), mat 586 - computational methods in cryo-electron microscopy (also apc 511/mol 511/qcb 513), qcb 501 - topics in ethics in science (half-term), qcb 505 - topics in biophysics and quantitative biology (also phy 555), qcb 508 - foundations of statistical genomics, qcb 515 - method and logic in quantitative biology (also chm 517/eeb 517/mol 515/phy 570), qcb 570 - biochemistry of physiology and disease, qcb 590 - extramural research internship in quantitative and computational biology.

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Quantitative Methods, PhD

The Ph.D. program in Quantitative Methods is designed to prepare students for faculty positions at universities and important responsibilities at research and assessment organizations. Graduates will be prepared to design first rate empirical research and data analyses and to contribute to development of new research methodologies.

Students who apply directly to the doctoral-level study program following a baccalaureate degree will enroll in the core courses described for M.S.Ed. degree in SMART and the more advanced courses for the Ph.D. degree. This will include the development of independent empirical research projects.

For more information: http://www.gse.upenn.edu/qm/phd

View the University’s Academic Rules for PhD Programs .

The Ph.D. degree program in Quantitative Methods requires a minimum of 20 course units or relevant courses and advanced degree accomplishments. A maximum of eight (8) credits from other institutions may be taken into account in reducing this basic requirement where appropriate.

Course List
Code	Title	Course Units
Required Courses
	Data Processing and Analysis (Fall)	1
	Evaluation of Policies, Programs and Projects	1
	Survey Methods & Design (Spring)	1
	Measurement & Assessment (Fall)	1
	Regression and Analysis of Variance (Fall or Spring)	1
	Measurement Theory and Test Construction (Spring)	1
	Factor Analysis and Scale Development (Fall)	1
	Structural Equations Modeling (Spring)	1
	Policy Research (Spring)	1
	Randomized Trials and Experiments (Spring)	1
	Complex, Multilevel, and Longitudinal Research Models (Fall)	1
	Classifications, Profiles, and Latent Growth Mixture Models (Spring)	1
Electives
Select eight electives		8
Total Course Units		20

Required Milestones

Qualifications evaluation (also known as program candidacy).

A Qualifications Evaluation of each student is conducted after the completion of 6 but not more than 8 course units. The evaluation is designed by the specialization faculty and may be based on an examination or on a review of a student’s overall academic progress.

Preliminary Examination (Also known as Doctoral Candidacy)

A Candidacy Examination on the major subject area is required. The candidacy examination is a test of knowledge in the student's area of specialization, requiring students to demonstrate knowledge and reasoning in the key content areas in their specialization as defined by their academic division. This examination is normally held after the candidate has completed all required courses.

Oral Proposal

All doctoral candidates must present their dissertation proposals orally and in person to the dissertation committee.

Final Defense of the Dissertation

The final dissertation defense is approximately two hours in length and is based upon the candidate’s dissertation.

The degree and major requirements displayed are intended as a guide for students entering in the Fall of 2024 and later. Students should consult with their academic program regarding final certifications and requirements for graduation.

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Quantitative Analytics Program

Explore our quantitative analytics program.

The rotational Quantitative Analytics program is designed to provide you with the opportunity to gain comprehensive professional and industry experience that prepares you to develop, implement, calibrate, and validate various analytical models. Wells Fargo hires a number of PhDs and Master’s Candidates within the Capital Markets, and Risk Analytics and Decision Science teams.

What will you do?

Your responsibilities include, but are not limited to:

Developing and validating models for different uses under the direction of experienced team members according to the track of your choice:

The Capital Markets Track deals with the mathematical models for pricing, hedging and risking complex financial instruments. Wells Fargo trading portfolios include products in all traded asset classes such as credit, commodity, Equity, FX Rate, Mortgages, and Asset-Backed Finance.

The Risk Analytics & Decision Science Track deals with the statistical, econometric, and machine-learning/AI models for a variety of applications, including loss and revenue forecasting, credit decisions, financial crimes, fair lending, operational risks, and analysis of unstructured data such as text and audio.

Use Python, R, C++, SAS, SQL or other programming languages as well as mathematical/statistical packages for model development and validation
Perform mathematical model development and validation (risk assessment) under the direction of experienced team members
Produce required documentation to evidence model development or validation
Understand business needs and providing possible solutions through clear verbal and written communications to management and fellow team members
Stay up to speed on industry challenges and new and innovative modeling techniques used across Wells Fargo to solve business problems or enhance business capabilities.
Participate in model related projects for varying purposes, methodologies and relevant lines of business

Is this opportunity right for you?

Program structure and desired qualifications:

Full-time program for Master's and PhD candidates. This is a 12-month rotational program that starts in July (1-month classroom training followed by two rotations).
Summer internship for Master's and PhD candidates. Program length is 10 weeks.
Enrolled in a Master’s or PhD program in: Statistics, Applied or Computational Mathematics, Computer Science, Economics, Physics, Quantitative Finance, Operations Research, Data Science, Engineering or related quantitative field or a related quantitative field
Excellent computer programing skills and use of statistical software packages such as Python, R, SAS, SQL, Spark, Java, and C++
Strong verbal, written communication and interpersonal skills
For the Capital Markets Track : Experience and demonstrated knowledge in mathematical and numerical methods including Monte Carlo methods, differential equations, linear algebra, applied probability, and statistics
For the Risk Analytics & Decision Science Track : Experience and demonstrated first-hand knowledge in a number of these areas: data analysis, statistical modeling, machine learning/AI models, data management, and computing

What does my future look like?

Upon successful completion of the program, participants will be permanently placed in one of Wells Fargo's model development or model validation groups:

Artificial Intelligence Machine Learning Model Development
Traded Products Model Development
Risk Modeling Group
Market and Counterparty Risk Analytics
Mortgage Model Development
Corporate Model Risk
Commercial Banking Model Development
Consumer Modeling

Where are the opportunities?

Summer internship and full-time opportunities are located in Charlotte, NC. Additional locations may be added based on business needs.

Helpful resources

Learn about the Quantitative Analytics Centers of Excellence .

Learn more about the application process.

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Any tools at the following website are provided for educational and illustrative purposes only. Wells Fargo does not guarantee the accuracy of the calculations and their applicability to your circumstances.

College of Education

Measurement, quantitative methods, & learning sciences doctoral program, learn more about our graduate programs rsvp now for a free virtual info session monday, sept. 23 at 6 p.m..

Student teacher with a student behind a Third Ward mural

The University of Houston's Measurement, Quantitative Methods, & Learning Sciences (MQM-LS) doctoral program equips students with the skills necessary to design, conduct and interpret quantitative research projects that help solve our society's most difficult problems. Students develop a broad understanding of psychological and learning theories while also receiving strong quantitative methods training. With these skills, our graduates can measure and analyze a wide variety of topics and issues in psychology and education with unique insights. Students received a wide variety of research opportunities within the Department of Psychological, Health, & Learning Sciences; the College of Education and UH. Our mix of quantitative methods training and learning sciences training produces strong candidates ready to compete in a competitive job market.

PHLS Faculty
Mission & Values
Student & Alumni Profiles

About the Program

69 hours of minimum required coursework
4 years to complete program when enrolled full-time (at least 9 hrs/semester)
MQM-LS Student Handbook
MQM-LS Program at a Glance
Factors Considered in Graduate Admissions and Awarding of Fellowships
UH Graduate School

What will I learn while attending the MQM-LS program?

MQM-LS students gain knowledge of measurements and quantitative research methods and theoretical foundations in human development and learning theory through:

Candidacy research project
Comprehensive Examination Portfolio
Dissertation

What can I do with my degree?

Upon completion of the program, graduates will be qualified to enter careers in a varity of roles and settings, including:

University and college professors
Researchers in Research and Accountability Divisions of public school systems
Data analysts or research specialists
Independent consultants

Important MQM-LS Resources

The following is a collection of important program resources:

American Psychological Association Division 5 (Quantitative and Qualitative Methods)
American Psychological Association Division 15 (Educational Psychology)
American Psychological Association Division 45 (The Society for the Psychological Study of Culture, Ethnicity, and Race)
American Educational Research Association Division C (Learning and Instruction)
American Educational Research Association Division D (Measurement & Research Methodologies)

MQM-LS Faculty

The following is a list of current mqm-ls faculty:, dr. weihua fan.

Measurement, Quantitative Methods & Learning Sciences

Faculty Profile | Email

Dr. Allison Master

Dr. margit wiesner.

PHLS Homepage
Our Programs

The MQM-LS faculty's research seeks to develop and improve research approaches and techniques while applying them to better understanding issues in psychology, education and youth behavior. Visit the PHLS Research Portal to learn more about our diverse interests and discover faculty pursuing answers to the questions that matter to you.

Feel free to contact faculty directly to learn more about their research. You can find contact information in the Research Portal or by visiting the COE Faculty Directory .

PHLS Research Portal

Financial Aid

All MQM-LS doctoral students are encouraged to apply for scholarships through the UH and the College of Education. To learn more about how to fund your graduate studies, visit the Graduate Funding page .

Graduate Tuition Fellowship

Graduate Tuition Fellowship (GTF) provides tuition remission for 9 credit hours, during the academic year, to students who enroll in at least 9 credit hours. During the summer term, GTFs are contingent upon available budget. Not all years in the graduate program may be covered by this program.

Assistantships

Graduate appointments are usually available to students during the first two years of graduate studies. The program doesn't cover mandatory fees or course fees. Not all years in the graduate program are covered by this program.

To learn more about funding your education, contact the COE's College of Graduate Studies at [email protected] or call 713-743-7676.

COE Financial Aid and Scholarships
UH Graduate Funding
UH Graduate Financial Information

Houston, Texas

Houston is the fourth largest city in the United States and one of the nation's most diverse cities. This fact benefits our students and faculty both personally and professionally. Home to more than 100 different nationalities and where more than 60 different languages are spoken, Houston is the perfect environment to practice what you're learning in the classroom. The city also boasts more than 12,000 theater seats and 11,000 diverse restaurants featuring cuisines from around the globe (Don't know where to start? Just ask a Houstonian, and they're sure to bombard you with at least a dozen places to eat.)

Houston is bustling with culture, energy and offers something for everyone inside and outside the classroom.

(Background photo: “ Metropolis ” by eflon is licensed under CC BY 2.0 .)

Student Housing & Residential Life
Greater Houston Partnership - Welcome to Houston

Ready to Apply?

Mqm-ls program application deadline: feb. 1 (domestic students), mqm-ls program application deadline: feb. 1 (international students).

Are you ready to apply to the University of Houston MQM-LS doctoral program ? Yes? You can learn more about the application process by visiting the College of Education's Graduate Admissions page or jump right into the application process by visiting the UH's How to Apply to Graduate School page .

If you need more information about the MQM-LS program, we are here to help. You can always contact the COE Office of Graduate Studies by phone at 713-743-7676 or by email .

The Measurement, Quantitative and Learning Sciences doctoral program is a member of UH's Psychological, Health, & Learning Sciences department .

Program Director: Dr. Weihua Fan

UH College of Education Stephen Power Farish Hall 3657 Cullen Blvd., Room 491 Houston, TX 77204-5023

Undergraduate: [email protected] or 713-743-5000 Graduate: [email protected] or 713-743-7676 General: [email protected] or 713-743-5010

PhD Programs with Quantitative Concentrations

Public policy.

Social Sciences

Public Health Sciences

Comparative human development, political science.

The University has PhD degree programs in various departments and schools that provide a concentration on quantitative methods. New doctoral applicants who are interested in developing specialty in quantitative methods, depending on their disciplinary interests, may look into one of these degree programs.

Doctoral Programs in Quantitative Methods

Business administration.

Econometrics and Statistics , one of the eight dissertation areas in the Booth School PhD program, is concerned with the combination of economic, mathematical, and computer techniques in the analysis of economic and business problems such as forecasting, demand and cost analyses, model-building, and testing empirical implications of theories. Study in this area integrates a comprehensive program of course work with extensive research. The program is designed for students who wish to do research in statistical methods that are motivated by business applications. Students are able to design an individual program of study by combining courses in specific areas of business, such as economics, finance, accounting, marketing, or international business with advanced courses in statistical methods. Empirical work has always been an important part of the research effort at Chicago Booth in all fields of study. Econometrics and statistics courses are thus useful choices in satisfying the basic discipline or coordinated sequence.

Quantitative Methods are a key component of the Core curriculum. Specialized Fields in Quantitative Methods include:

Quantitative Study of Inequality
Applied econometrics

Tools of Policy Analysis provides in-depth and technical expertise that can be applied to a broad range of subject areas.The following are included among the five specialties: Program evaluation, statistics, and survey methods.

Methods in Human Development Research . Research on human development over the life span and across social and cultural contexts thrives on multiple theoretical perspectives. This research requires creation and improvement of a wide range of research methods appropriately selected for and tailored to specific human development problems. Faculty in the department employ research methods that span the full range from primarily qualitative to primarily quantitative and to strategic mix of both. Across all the substantive domains in Comparative Human Development, theoretical understanding is greatly advanced by methodology; therefore the Department pays serious attention to research design, data collection, analytic strategies, and presentation, evaluation, and interpretations of evidence. The Department has contributed some of the most influential work on psychological scaling on the basis of the item response theory (IRT), multivariate statistical methods, analysis of qualitative data, modeling of human growth, and methods for cross-cultural analysis. Current research interests include (a) assessment of individual growth and change in important domains of development that are often intertwined, (b) examination and measurement of the structure, process, and quality of individual and group experiences in institutionalized settings such as families, schools, clinics, and neighborhoods, and (c) evaluation of the impact of societal changes or interventions on human development via changes in individual and group experiences, with particular interest in the heterogeneity of growth, process, and impact across demographic sub-populations and across social cultural contexts.

Concentration in Biostatistics ,The PhD program in the Department of Public Health Sciences is supported by a core methodological curriculum in population-based research on human health. Students completing a concentration in biostatistics will be prepared to develop state-of-the-art quantitative reasoning and techniques of statistical science, mathematics, and computing, and to apply these to current and future research problems in biomedical science and population health. In addition, these students will complete a minor program of study in a substantive area of application. As such they will be particularly well prepared to engage in collaborative population-based health research.

Methodology is one of the five fields in the department. Many students choose the department’s introductory sequence in quantitative methods, followed by more advanced seminars in data analysis and model building. Students with more advanced methodological skills can take further coursework in the department or related courses in economics, public policy or statistics.

Special Fields in Methodology . The Department of Sociology at the University of Chicago has a rich group of faculty members who provide graduate training and conduct research in methods and models for sociological research. These methods can be divided roughly into four categories: Field and ethnographic methods; statistical methods; survey and related methods; and mathematical modeling methods. PhD. students are required to demonstrate competence in two special fields. The Special Field Requirement is generally met during the third and fourth years of graduate study. Students must pass the Preliminary Examination at the PhD. level before meeting the Special Field Requirement. This requirement may be met in three ways: by examination, with a review essay, or through a specified sequence of methods courses. Five types of special fields in methodology are recognized: (1) social statistics, (2) survey research methods, (3) qualitative methods (4) methodology for social organization research, and (5) mathematical sociology.

Psychology (Quantitative Research Methods), PHD

On this page:, at a glance: program details.

Location: Tempe campus
Second Language Requirement: No

Program Description

Degree Awarded: PHD Psychology (Quantitative Research Methods)

The PhD program in psychology with a concentration in quantitative research methods offers an immersive education in advanced statistical techniques and research methodologies that are employed in the conduct of both basic and applied psychological research.

A collaborative, interdisciplinary approach to research empowers students to deepen their understanding and tackle key issues, such as exploring the limits of existing methods, pushing the methodological frontiers forward, evaluating the effectiveness of established and emerging methodologies, and improving the robustness of psychological research through innovative measurements and analytical methods.

What sets this program apart is its distinguished, award-winning faculty, known for their expertise and dedication to training the next generation of psychological methodologists. Alongside the faculty, students gain practical experience and master techniques in the areas of measurement, study design, data analysis, statistical modeling, and evaluation of the utility of new and existing methods.

Graduates of this program emerge as experts in quantitative research who are prepared to make meaningful contributions to the field by developing and applying sophisticated statistical and methodological solutions to address pressing research issues.

Quantitative Faculty Research Labs

Degree Requirements

Curriculum plan options.

84 credit hours, a written comprehensive exam, an oral comprehensive exam, a prospectus and a dissertation

Required Core (3 or 4 credit hours) PSY 502 Professional Issues in Psychology (3) or PSY 531 Multiple Regression in Psychological Research (4)

Concentration (3 credit hours) PSY 533 Structural Equation Modeling (3)

Other Requirements (31 credit hours) PSY 530 Intermediate Statistics (4) PSY 532 Analysis of Multivariate Data (3) PSY 534 Psychometric Methods (3) PSY 536 Statistical Methods in Prevention Research (3) PSY 537 Longitudinal Growth Modeling (3) PSY 538 Advanced Structural Equation Modeling (3) PSY 539 Multilevel Models for Psychological Research (3) PSY 540 Missing Data Analysis (3) PSY 543 Statistical Mediation Analysis (3) PSY 555 Experimental and Quasi-experimental Designs for Research (3)

Electives (22 or 23 credit hours)

Research (12 credit hours)

Culminating Experience (12 credit hours) PSY 799 Dissertation (12)

Additional Curriculum Information Electives are determined in consultation with the student's supervisory committee.

Other requirements courses may be substituted for other courses based on consultation with the student's supervisory committee.

Admission Requirements

Applicants must fulfill the requirements of both the Graduate College and The College of Liberal Arts and Sciences.

Applicants are eligible to apply to the program if they have earned a bachelor's or master's degree from a regionally accredited institution.

Applicants must have a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in the last 60 hours of their first bachelor's degree program or a minimum cumulative GPA of 3.00 (scale is 4.00 = "A") in an applicable master's degree program.

All applicants must submit:

graduate admission application and application fee
official transcripts
SlideRoom application and fee
statement of purpose form
curriculum vitae or resume
three letters of recommendation
proof of English proficiency

Additional Application Information An applicant whose native language is not English must provide proof of English proficiency regardless of their current residency.

ASU does not accept the GRE® General Test at home edition.

To apply to the doctoral program, applicants must follow the instructions on the doctoral program admissions instructions and checklist. It is strongly recommended that applicants download and print the instructions and checklist to ensure completion of the application process and that all required supplemental forms are included.

The Department of Psychology application process is completed online through ASU's graduate admission services, which includes the application form and official transcripts. Application to the Department of Psychology doctoral programs is also completed via SlideRoom, for processing of supplemental application materials. The SlideRoom account requires an additional fee.

Applicants must submit three academic letters of recommendation from faculty members who know the student well. Three letters are required, but four letters of recommendation may be submitted.

Next Steps to attend ASU

Learn about our programs, apply to a program, visit our campus, application deadlines, career opportunities.

Quantitative psychologists possess advanced statistical and methodological expertise applicable to various research challenges. While rooted in psychology, their skills find broad applications in fields such as education, heath, neuroscience and marketing. Graduates of the doctorate in psychology (quantitative research methods) program excel in interdisciplinary collaboration and effective communication of complex ideas.

Potential careers induce roles as:

consultants
data scientists
policy analysts
psychology professors
psychometricians
research scientists

Program Contact Information

If you have questions related to admission, please click here to request information and an admission specialist will reach out to you directly. For questions regarding faculty or courses, please use the contact information below.

[email protected]
480/727-4561

Program Details

Program requirements .

Complete Required Coursework
Pass Preliminary Examination
Serve as Teaching or Research Assistant
Complete 2 Research Papers
Write & Defend Dissertation

Funding is available for up to five years , provided the student maintains satisfactory progress in the program. Funding beyond five years is up to the discretion of the department.

For more information, review our standard funding package .

The Quantitative Methods PhD students typically take courses in CS, Economics, IE, Management, and Statistics.

For detailed descriptions of the courses, please visit Purdue’s Online Course Catalog .

Graduate Assistantship Appointments

Quantitative Methods PhD students will participate as a graduate research assistant (RA) or graduate teaching assistant (TA) each semester for four years.

Graduate Research Assistant (RA)

RA roles involve a PhD student in a faculty research project.

Graduate Teaching Assistant (TA)

TA roles often begin with tasks such as grading and helping in computer lab sessions and progress to course delivery responsibilities. All students are required to teach at least one course during their time in the program.

Graduates of Purdue’s Quantitative Methods PhD program have gone on to secure post-doctoral and teaching placements at the following institutions:

Sejong University
University of North Carolina Pembroke
University of South Alabama
University of Western Ontario
Yuan Ze University

Others have secured placement in industry working in the financial, retail fields, and technology fields.

Cummins, Inc
JP Morgan Chase & Co
Sam’s Club

About the Department

[email protected]

Explore areas of research

Psychological Sciences

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Graduate Program

Quantitative Methods

Program overview.

Faculty in the Quantitative Methods (QM) program train students in state-of-the-art statistical methods and engage in research that develops and applies such methods. Students in the QM doctoral program develop expertise in the principles of research design and in the theoretical foundations and application of advanced statistical models for human behavior. Students work closely on research projects with a faculty mentor throughout their graduate career, and often collaborate with other faculty and students. QM faculty collectively have expertise in factor analysis and structural equation modeling; network analysis; measurement and item response theory; exploratory data analysis; mediation and moderation; longitudinal methods; multilevel modeling; mixture modeling; categorical data analysis; and generalized linear models. Quantitative faculty approach the study of these topics from a variety of angles, such as: developing computational tools to promote the use of new or existing methods; evaluating the performance of such methods under real-world conditions; and applying these methods in novel and sophisticated ways to solve substantive problems. Several QM faculty have substantive specializations in, for example, individual differences, personality psychology, clinical psychology, learning sciences, and developmental psychology, which facilitate intensive investigation of analytic approaches critical to those substantive domains. Students may pursue greater or lesser degrees of substantive psychological training, in addition to quantitative training, depending on their and their advisors' interests.

The QM program is housed within the Department of Psychology and Human Development at Peabody College-- a top-ten ranked school of education for the past ten years. This unique context exposes QM students to a variety of applications, methods, and statistical problems that arise in psychological and educational research, as well as the social sciences more generally.

QM faculty teach courses on a broad variety of fundamental and advanced topics in design and data analysis. These courses are attended by students from a variety of social science disciplines, as well as by QM students. QM students are encouraged to tailor their curriculum to maximize relevancy for their particular research interests, background, and career goals. QM course offerings include correlation and regression; analysis of variance; psychological and educational measurement; data science methods; multivariate analysis; psychological, field, and clinical research methods; item response theory (basic and advanced); exploratory/graphical data analysis; structural equation modeling; factor analysis; latent growth curve modeling; categorical data analysis; multilevel modeling; mixture modeling; nonparametric statistics; individual differences; causal analysis in field experiments and quasi-experiments; network analysis; statistical consulting; and meta-analysis. Additionally, many of our students get an optional Minor in Biostatistics . Students may also take courses in Scientific Computing , and/or other areas of psychology and education. Several research centers on campus also provide QM students with training opportunities. Vanderbilt’s new Data Science Institute (DSI) offers numerous workshops, short courses, colloquia, and collaboration opportunities using data science methods and tools. QM faculty also serve as teaching faculty and/or faculty affiliates of the DSI and are involved with the development, operations, and strategic goals of the DSI. Also, the Vanderbilt Kennedy Center maintains a statistics and methodology core which provides a methodology lecture series as well as statistical consulting training and resources. Additionally, students gain presentation and research skills by participating in the Quantitative Methods Forum (schedule below).

Core faculty

More information about individual faculty's research programs can be obtained from their websites by clicking on their names. Alternately, a list of QM faculty is available here . Prospective students are encouraged to contact core QM faculty with shared interests to ask questions about the program. Core QM faculty recruit and train Ph.D. students through the QM program.

* Sun-Joo Cho (item response theory; generalized latent variable modeling; test development and validation)
* Alex Christensen (network analysis; data science; psychometrics; measurement)
David Cole (structural equation modeling; mediation analysis; longitudinal methods; developmental psychopathology)
Shane Hutton (survival analysis; dynamical systems modeling)
David Lubinski (measurement; assessment; individual differences; intellectual talent development)
Kristopher Preacher (structural equation modeling; multilevel modeling; mediation and moderation)
Sonya Sterba (mixture models; multilevel and longitudinal methods; latent variable models)
Chris Strauss (measurement and assessment; multilevel measurement; structural equation modelling)
Hao Wu (model evaluation; uncertainty quantification; robust and nonparametric methods; structural equation modeling)

(* = interested in recruiting a QM Ph.D. student to start in the 2025-2026 academic year)

Emeritus faculty

Joseph Rodgers (general multivariate methods; exploratory/graphical data analysis; multidimensional scaling and measurement; behavior genetics; adolescent development)
Jim Steiger (structural equation modeling; model evaluation; inferential methods; statistical computing)
Andrew Tomarken (categorical data analysis; generalized linear models; longitudinal methods; clinical psychology)

Affiliated faculty

Li Chen (statistical consulting; quantitative pedagogy)
Scott Crossley (natural language processing)
Will Doyle (data science; education policy)
Kelly Goldsmith (business analytics, marketing, consumer psychology)

The program maintains its own quantitative computer lab, and additionally individual faculty have labs and computing resources that support their research programs. There are also computing labs in the department and elsewhere in Peabody College that are supplied with statistical software often used for classroom teaching. Special funds for research-related software and computing equipment, as well as external workshop and conference travel, are available to QM students.

Information for Prospective QM Applicants

QM doctoral program graduates are prepared for faculty positions in academic settings, methodology positions in basic or applied research centers, or methodology positions in industry. Students work together with their advisor and advisory committee to refine their career goals, and tailor their research, coursework, and teaching experiences accordingly. The American Psychological Association reports that there are far more jobs for doctoral students trained in quantitative methods in psychology than there are applicants. Further information can be found here , here , and here .

The QM program is designed to lead to a Ph.D. degree within 5 years. In the first two years, students take a series of fundamental methods courses and begin working on research with their advisor. To build students' oral presentation skills, students present their research to the program on a yearly basis. Students who did not enter with a full year of calculus also complete such coursework in the Mathematics Department during this time. In their third year, students complete their masters thesis and continue research in collaboration with their advisor and others, while furthering their expertise with an individualized set of advanced coursework. Students take an exam in their third or fourth year that is based on reading lists related to content in courses they have taken up until that point. In their fourth and fifth years students finish their coursework and conduct a dissertation project under the guidance of their advisor and other committee members, while building additional independent research and/or teaching skills relevant to their particular career goals.

Doctoral applicants admitted to the QM program receive a guaranteed 5 years of stipend and tuition support, which usually takes the form of a combination of research assistantships and/or teaching assistantships in quantitative courses (for instance, the introductory graduate statistics sequence). Additionally, QM students have a successful track record of obtaining prestigious NSF fellowships. Senior students routinely also may obtain other kinds of stipends as statistical analysts or consultants for various research projects and grants on campus; these opportunities serve as valuable supplementary training experiences. Some students also serve as teaching instructors for their own section of an undergraduate statistics course or undergraduate measurement course in order to deepen their teaching credentials. Application instructions are available here .

QM Masters Program

In Spring 2014, the QM program launched a terminal M.Ed. in Quantitative Methods. This program is distinct from our longstanding research-focused Ph.D. program. More information about the goals and expectations for applicants to our M.Ed. program can be found here .

Graduate QM Minor

Doctoral students outside the QM program may elect to minor in quantitative methods. This formal minor involves taking four advanced methods courses from the QM program beyond the first year required graduate statistics sequence (6 courses total). The minor requires a 3.5 average GPA (for all 6 minor courses), with no grade below a B. The minor provides students with exceptional training in the application of complex psychometric and statistical procedures and provides students with skills that can enhance the quality of their research program over the course of their career. Many students find that the credential of a graduate minor in quantitative methods is a valuable asset in the pursuit of research-oriented academic positions or quantitatively-oriented industry positions after graduation. Detailed information on minor requirements can be obtained from the Psychological Sciences graduate student handbook. For more information, contact Kris Preacher .

Undergraduate QM Minor

The QM program offers an 18-credit undergraduate minor in quantitative methodology. For information on our new undergraduate QM minor, please click here .

Quantitative Methods Colloquium Series

The QM program offers a weekly Quantitative Methods Colloquium Series which covers novel methodological advances, cutting-edge applications of quantitative methods, inclusivity in QM, teaching pedagogy in QM, QM professional development activities, QM outreach, and QM workshops. The QM colloquium series features a mix of external speakers from different settings (e.g., academia and industry) and different stages of their careers in order to expose our QM students to a variety of career paths and perspectives. Each semester our QM forum also contains internal program speakers, QM students and QM faculty, to allow us to share our research with, and gain feedback from, our colleagues. For more information on the QM Colloquium please visit the Colloquium schedule .

Quantitative Methods Outreach

At least once per year the QM Colloquium Series features an Open House where statistical consulting problems presented by Peabody faculty guest(s) receive a program-level discussion. Additionally, our QM program offers a statistical consulting course on a yearly basis to which Peabody faculty can submit statistical problems to serve as student projects. QM faculty also maintain a listserv ([email protected]) to which Peabody faculty can submit statistical problems that are limited in scope. Submitted questions will first be considered for open house or course project slots and secondarily for a graduate assistant to the QM faculty for further attention.

Fall 2024 QM Course Offerings

PSY-GS 8861-01: Statistical Inference . TR 1:15p - 2:30p Hutton
PSY-GS 8870-01 / PSY-PC 3735-01: Correlation and Regression . TR 9:30a - 10:45a Strauss
PSY-GS 8873-01: Structural Equation Modeling . TR 11:00a - 12:15p Cole
PSY-GS 8876-01 / PSY-PC 3724-01: Psychological Measurement / Psychometrics . T 4:15p - 7:05p Lubinski
PSY-GS 8878-01 / PSY-PC 7878-01: Statistical Consulting . T 1:15p - 4:05p Strauss
PSY-GS 8879-01 / PSY-PC 3743-01: Factor Analysis . F 10:10a - 1:00p Preacher
PSY-GS 8882-01: Multilevel Modeling . W 10:10a - 1:00p Preacher

Undergraduate

PSY-PC 2110-01: Introduction to Statistical Analysis . TR 11:00a - 12:15p Hutton
PSY-PC 2110-05: Introduction to Statistical Analysis . MWF 11:15a - 12:05p Chen
PSY-PC 2110-06: Introduction to Statistical Analysis . MWF 12:20a - 1:10p Osina
PSY-PC 2110-07: Introduction to Statistical Analysis . MWF 10:10a - 11:0 0a Chen
PSY-PC 2110-08: Introduction to Statistical Analysis . TR 9:30a - 10:45a Vinci-Booher
PSY-PC 2110-09: Introduction to Statistical Analysis . TR 1:15p - 2:30p Wu
PSY-PC 2110-10: Introduction to Statistical Analysis . TR 2:45p - 4:00p Wu
PSY-PC 3722-01: Psychometric Methods . TR 8:00a - 9:15a Cho

Systems, Synthetic, and Quantitative Biology

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Harvard was one of the first institutions to offer a program to explore this exciting new field. The program’s core curriculum includes courses on the methods and logic that shape research, how to conceptualize and present research, and an introduction to the faculty’s research.

The program has 48 faculty located in the Faculty of Arts and Sciences, Harvard Medical School, and Harvard-affiliated teaching hospitals including Dana-Farber Cancer Institute, Mass General, and Boston Children’s Hospital. SSQB is one of 14 PhD programs in the Harvard Integrated Life Sciences program that collectively gives you access to over 900 faculty research groups situated in the heart of Boston’s biotech hub. Our students are working on projects that range from fundamental problems in biology to translational research, whose goal is to directly affect medicine and global sustainability.

Graduates of the program have gone on to faculty positions at prestigious institutions such as MIT and Princeton University, while others are now industry leaders as startup founders or as decision makers at companies including Boston Consulting Group, Yumanity Therapeutics, McKinsey & Company, and Regeneron.

Additional information on the graduate program is available from the Systems, Synthetic, and Quantitative Biology PhD Program , and requirements for the degree are detailed in Policies .

Admissions Requirements

Please review the admissions requirements and other information before applying. You can find degree program-specific admissions requirements below and access additional guidance on applying from the Systems, Synthetic, and Quantitative Biology PhD Program .

Academic Background

Applicants typically have a background in biology, physics, chemistry, computer science, engineering, or mathematics and work to forge a new approach to biology that combines theoretical and experimental approaches. The typical student has a strong background in one of the disciplines relevant to systems biology and an interest in interdisciplinary research.

Personal Statement

Standardized tests.

GRE General: Optional

Contacting Faculty

Applicants should indicate their faculty of interest in the application. You are not required to contact any faculty in advance but are welcome to.

Applications are reviewed by the admissions committee during December and early January. Selected applicants are notified if they have been chosen for an on-campus interview. These visits provide students with the opportunity to meet with faculty and current students and to get a better feel for our community and the types of research conducted here. Applicants invited for an interview who reside overseas and cannot visit the Harvard campus may interview remotely.

Theses and Dissertations

Theses & Dissertations for Systems, Synthetic, and Quantitative Biology

See list of Systems, Synthetic, and Quantitative Biology faculty

APPLICATION DEADLINE

Questions about the program.

Graduate Program in Quantitative Biomedical Sciences

Dartmouth graduate students earn top spots at national big data case competition.

Competing for the first time, four teams from the QBS MS program, including a team of two graduate students and one undergraduate, created a diagnostic tool to solve the problem posed to 37 different teams.

QBS Associate Director: Rob Frost, PhD

Dr. Frost brings a unique depth of experience to QBS leadership in his role as Associate Director.

QBS Director: Scott Gerber, PhD

Dr. Gerber Highlights the Importance of Interdisciplinary Research and Training

4+1 Programs

Epidemiology 4+1 Program

Health Data Science 4+1 Program

Medical Informatics 4+1 Program

QBS Faculty Member Diane Gilbert-Diamond Recognized for Outstanding Mentoring

Dr. Gilbert-Diamond's approach to mentoring acknowledges the emotional and academic development of her mentees, and she has created an environment where everyone feels supported.

Embark on an Elite Academic Experience at Dartmouth College

Dartmouth’s Quantitative Biomedical Sciences (QBS) program has ushered in an unparalleled academic experience that challenges students to think critically with an interdisciplinary lens to solve complex biomedical problems facing local and global populations.

Bridging the intersection of health care, epidemiology biomedicine, biostatistics, population health, and big data, QBS faculty deliver cutting-edge theory while also serving as dedicated mentors who are passionate about student outcomes and success. They are accessible and attentive to student needs and foster an environment of collaboration, engagement, and lifelong learning.

Learn about our MS and PhD Programs

QBS and the Dartmouth Geisel School of Medicine offer 3 unique, interdisciplinary Masters degree programs .

Masters in Health Data Science

Masters in Epidemiology

Masters in Medical Informatics

QBS and the Guarini School of Graduate and Advanced Studies offers an unparalleled PhD in Quantitative Biomedical Sciences .

Learn More about QBS

The dartmouth qbs difference.

Our QBS programs give students the opportunity to be part of a lively, passionate community of students that thrives on collaboration, the sharing of ideas, and supporting and challenging one another to be their best. This sets the stage for students to collaborate across disciplines and areas of expertise to find unique insights in data that lead to truly innovative solutions for the world. Meet our current PhD students and current master’s students to learn more about our community.

Six Reasons To Join QBS at Dartmouth:

Why Dartmouth QBS?

QBS Community

Interdisciplinary Program

Career Opportunities

The Ivy League Experience

Dedicated Faculty

Strong Alumni Network

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Psychology (Quantitative Research Methods), PhD

Program description
At a glance
Degree requirements
Admission requirements
Tuition information
Application deadlines
Career opportunities
Contact information

Data Analysis, Data Analytics, Data Mining, Data Science, Data analysis and mining, Machine Learning, Psychology, Quantitative Science, Research Methods, analysis, approved for STEM-OPT extension, statistics

Elevate your expertise in psychology research methodologies at one of the top-ranked programs in the nation. You'll collaborate with esteemed faculty to master cutting-edge statistical techniques, innovate in psychological research and become a leader in shaping the future of the field.

This program may be eligible for an Optional Practical Training extension for up to 24 months. This OPT work authorization period may help international students gain skills and experience in the U.S. Those interested in an OPT extension should review ASU degrees that qualify for the STEM-OPT extension at ASU's International Students and Scholars Center website.

The OPT extension only applies to students on an F-1 visa and does not apply to students completing a degree through ASU Online.

College/school: The College of Liberal Arts and Sciences
Location: Tempe
STEM-OPT extension eligible: Yes

84 credit hours, a written comprehensive exam, an oral comprehensive exam, a prospectus and a dissertation

Required Core (3 or 4 credit hours) PSY 502 Professional Issues in Psychology (3) or PSY 531 Multiple Regression in Psychological Research (4)

Concentration (3 credit hours) PSY 533 Structural Equation Modeling (3)

Electives (22 or 23 credit hours)

Research (12 credit hours)

Culminating Experience (12 credit hours) PSY 799 Dissertation (12)

Additional Curriculum Information Electives are determined in consultation with the student's supervisory committee.

Other requirements courses may be substituted for other courses based on consultation with the student's supervisory committee.

Applicants must fulfill the requirements of both the Graduate College and The College of Liberal Arts and Sciences.

Applicants are eligible to apply to the program if they have earned a bachelor's or master's degree from a regionally accredited institution.

All applicants must submit:

graduate admission application and application fee
official transcripts
SlideRoom application and fee
statement of purpose form
curriculum vitae or resume
three letters of recommendation
proof of English proficiency

Additional Application Information An applicant whose native language is not English must provide proof of English proficiency regardless of their current residency.

ASU does not accept the GRE® General Test at home edition.

Applicants must submit three academic letters of recommendation from faculty members who know the student well. Three letters are required, but four letters of recommendation may be submitted.

Session	Modality	Deadline	Type
Session A/C	In Person	12/05	Final

Potential careers induce roles as:

consultants
data scientists
policy analysts
psychology professors
psychometricians
research scientists

Department of Psychology | PSY 201 [email protected] 480-727-4561

Quantitative Biosciences (Ph.D.)

The mission of the Georgia Tech PhD program in Quantitative BioSciences (QBioS) is to enable the discovery of scientific principles underlying the dynamics, structure, and function of living systems. The QBioS program is designed to provide PhD graduates with the skills and expert knowledge necessary to move directly into academia, industry and/or government, where they can apply their specific domain expertise and broadly relevant modeling tools.

for Integrative Genomics Lewis-Sigler Institute

Ph.d. program requirements, qcb graduate program requirements.

See QCB Student Handbook for program details.

QCB 515 Method and Logic in Quantitative Biology
QCB 537 (fall term) and QCB 538 (spring term): Current Research Topics in the Quantitative Life Sciences
COS/QCB 551 Introduction to Genomics and Computational Molecular Biology
Three elective courses from the lists below, including at least one from the Quantitative course list and one from the Biological course list
QCB 501 Topics in Ethics in Science, our Responsible Conduct of Research (RCR) course
MOL 550 The Graduate Primer
Participation in our Graduate Colloquium
Research rotations in your first year (three required)
General exam (taken in January of your second year)
Teaching (usually completed in fourth year of study)
Annual thesis committee meetings
Dissertation defense

The course of study for each student must be approved by the Director of Graduate Studies in the beginning of their first year, and course substitutions are possible with the permission of the DGS.

QCB 515: Method and Logic in Quantitative Biology

Close reading of published papers illustrating the principles, achievements, and difficulties that lie at the interface of theory and experiment in biology. Two important papers, read in advance by all students, will be considered each week; the emphasis will be on discussion with students as opposed to formal lectures. Topics include: cooperativity, robust adaptation, kinetic proofreading, sequence analysis, clustering, phylogenetics, analysis of fluctuations, and maximum likelihood methods. A general tutorial on Matlab and specific tutorials for the four homework assignments will be available.

COS/QCB 551: Introduction to Genomics and Computational Molecular Biology

This interdisciplinary course provides a broad overview of computational and experimental approaches to decipher genomes and characterize molecular systems. We focus on methods for analyzing "omics" data, such as genome and protein sequences, gene expression, proteomics and molecular interaction networks. We cover algorithms used in computational biology, key statistical concepts (e.g., basic probability distributions, significance testing, multiple hypothesis correction, data evaluation), and machine learning methods which have been applied to biological problems (e.g., hidden Markov models, clustering, classification techniques).

QCB 537/538 Current Research Topics in the Quantitative Life Sciences

Mandatory first-year graduate course centered around the weekly QCB seminar series, intended to help develop competency in critical reading and assessment of academic literature across subfields early in graduate training. Class meetings comprise student-driven presentations and discussions surveying research topics relevant to upcoming talks, with an emphasis on latest methodologies and debates. Assessment includes seminar and class attendance, in-class and in-seminar participation, and peer evaluation.

LSI Graduate Colloquium

QCB students are required to attend the LSI Graduate Colloquium during the fall and spring terms, usually held on Thursday afternoons. Second year students will give research talks in the fall term and fourth year students will present their work in the spring term. The series will end with first-year students giving short presentations on the work they have done in one of their rotations.

QCB 501: Topics in Ethics in Science

Discussion and evaluation of the role professional researchers play in dealing with the reporting of research, responsible authorship, human and animal studies, misconduct and fraud in science, intellectual property, and professional conduct in scientific relationships. Participants are expected to read the materials and cases prior to each meeting. Successful completion is based on regular attendance and active participation in discussion. This half-term course is designed to satisfy federal funding agencies' requirements for training in the ethical practice of scientists. Required for graduate students and post-docs.

(must take at least one)

APC 524 /MAE 506/AST 506 Software Engineering for Scientific Computing

CBE 517 Soft Matter Mechanics Fundamentals & Applications

CHM 503/CBE 524/MSE 514 Introduction to Statistical Mechanics

CHM 515 Biophysical Chemistry I

CHM 516 Biophysical Chemistry II

CHM 542 Principles of Macromolecular Structure: Protein Folding, Structure, and Design

COS 511 Theoretical Machine Learning

COS 513 Foundations of Probabilistic Modeling

COS 524/COS 424 Fundamentals of Machine Learning

COS 557 Artificial Intelligence for Precision Health

COS 597D Advanced Topics in Computer Science: Advanced Computational Genomics

COS 597F Advanced Topics in Computer Sci: Computational Biology of Single Cells

COS 597G Advanced Topics in Computer Science: Understanding Large Language Models

COS 597O Advanced Topics in Computer Science: Deep Generative Models: Methods, Applications & Societal Considerations

ELE 535 Machine Learning and Pattern Recognition

MAE 550/MSE 560 - Lessons from Biology for Engineering Tiny Devices

MAE 567/CBE 568 Crowd Control: Understanding and Manipulating Collective Behaviors and Swarm Dynamics

MAT 586/APC 511/MOL 511/QCB 513 Computational Methods in Cryo-Electron Microscopy

MOL 518 Quantitative Methods in Cell and Molecular Biology

MSE 504/CHM 560/PHY 512/CBE 520 Monte Carlo and Molecular Dynamics Simulation in Statistical Physics & Materials Science

NEU 437/537 Computational Neuroscience

NEU 501 Cellular and Circuits Neuroscience

NEU 560 Statistical Modeling and Analysis of Neural Data

ORF 524 Statistical Theory and Methods

PHY 561/2 Biophysics

QCB 505/PHY 555 Topics in Biophysics and Quantitative Biology

QCB 508 Foundations of Statistical Genomics

CHM 403 Advanced Organic Chemistry

CHM/QCB 541 Chemical Biology II

EEB 504 Fundamental Concepts in Ecology, Evolution, and Behavior II

EEB 522 Colloquium on the Biology of Populations

MAE 566 Biomechanics and Biomaterials: From Cells to Organisms

MAE 567/CBE 568 Crowd Control: Understanding and Manipulating Collective Behaviors and Swarm Dynamics

MOL 504 Cellular Biochemistry

MOL 506 Cell Biology and Development

MOL 521 - Systems Microbiology and Immunology (half-term course)

MOL 523 Molecular Basis of Cancer

MOL 559 Viruses: Strategy & Tactics

QCB 490 Molecular Mechanisms of Longevity

QCB 535 Biological networks across scales: Open problems and research methods of systems biology

QCB 570 Biochemistry of Physiology and Disease

(note: these do not count towards course requirements)

APC 350 Introduction in Differential Equations

COS 226 Algorithms and Data Structures

COS 343 Algorithms for Computational Biology

EEB 324 Theoretical Ecology

MOL/QCB 485 Mathematical Models in Biology

ORF 309/MAT 380 Probability and Stochastic Systems

QCB 302 Research Topics in QCB

QCB 311 Genomics

Please visit Course Offerings to see the most up-to-date course information.

Explore Programs

Quantitative biology - doctorate (phd).

DEGREE OVERVIEW

The PhD in quantitative biology is designed to train students to apply sophisticated quantitative techniques to solving basic and applied problems in biology. Students will attain substantially greater quantitative skills than in traditional doctoral programs in the biological sciences, providing them with a competitive advantage in business, industry, government, and academia.

ABOUT THE PROGRAM

The Department of Biology offers a Doctor of Philosophy degree in quantitative biology with research emphasis in genome biology & genetics, cell biology, ecology & systematics, or microbiology. The doctoral program is designed to train students to apply sophisticated quantitative techniques to solving basic and applied problems in biology. Students in this program will attain substantially greater quantitative skills than in traditional doctoral programs in the biological sciences, providing them with a competitive advantage in business, industry, government, and academia.

Admissions requirements and degree curriculum
Degree information in the University Catalog
Biology faculty and staff

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PROGRAM CONTACT

Name: Stephanie Fenton

Email: [email protected]

Learn more about this program on the Department or College website.

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UNIVERSITY CATALOG

Check out the University Catalog for more information.

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Combine an international MBA with a deep dive into management science. A special opportunity for partner and affiliate schools only.

A doctoral program that produces outstanding scholars who are leading in their fields of research.

Bring a business perspective to your technical and quantitative expertise with a bachelor’s degree in management, business analytics, or finance.

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PhD Program

Program overview.

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Rigorous, discipline-based research is the hallmark of the MIT Sloan PhD Program. The program is committed to educating scholars who will lead in their fields of research—those with outstanding intellectual skills who will carry forward productive research on the complex organizational, financial, and technological issues that characterize an increasingly competitive and challenging business world.

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Learn more about the program, how to apply, and find answers to common questions.

Admissions Events

Check out our event schedule, and learn when you can chat with us in person or online.

Start Your Application

Visit this section to find important admissions deadlines, along with a link to our application.

Click here for answers to many of the most frequently asked questions.

PhD studies at MIT Sloan are intense and individual in nature, demanding a great deal of time, initiative, and discipline from every candidate. But the rewards of such rigor are tremendous: MIT Sloan PhD graduates go on to teach and conduct research at the world's most prestigious universities.

PhD Program curriculum at MIT Sloan is organized under the following three academic areas: Behavior & Policy Sciences; Economics, Finance & Accounting; and Management Science. Our nine research groups correspond with one of the academic areas, as noted below.

MIT Sloan PhD Research Groups

Behavioral & policy sciences.

Economic Sociology

Institute for Work & Employment Research

Organization Studies

Technological Innovation, Entrepreneurship & Strategic Management

Economics, Finance & Accounting

Accounting

Management Science

Information Technology

System Dynamics

Those interested in a PhD in Operations Research should visit the Operations Research Center .

PhD Students_Work and Organization Studies

PhD Program Structure

Additional information including coursework and thesis requirements.

MIT Sloan Predoctoral Opportunities

MIT Sloan is eager to provide a diverse group of talented students with early-career exposure to research techniques as well as support in considering research career paths.

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Rising Scholars Conference

The fourth annual Rising Scholars Conference on October 25 and 26 gathers diverse PhD students from across the country to present their research.

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The goal of the MIT Sloan PhD Program's admissions process is to select a small number of people who are most likely to successfully complete our rigorous and demanding program and then thrive in academic research careers. The admission selection process is highly competitive; we aim for a class size of nineteen students, admitted from a pool of hundreds of applicants.

What We Seek

Outstanding intellectual ability
Excellent academic records
Previous work in disciplines related to the intended area of concentration
Strong commitment to a career in research

MIT Sloan PhD Program Admissions Requirements Common Questions

Dates and Deadlines

Admissions for 2024 is closed. The next opportunity to apply will be for 2025 admission. The 2025 application will open in September 2024.

More information on program requirements and application components

Students in good academic standing in our program receive a funding package that includes tuition, medical insurance, and a fellowship stipend and/or TA/RA salary. We also provide a new laptop computer and a conference travel/research budget.

Funding Information

Throughout the year, we organize events that give you a chance to learn more about the program and determine if a PhD in Management is right for you.

PhD Program Events

Docnet recruiting forum at university of minnesota.

We will be joining the DocNet consortium for an overview of business academia and a recruitment fair at University of Minnesota, Carlson School of Management.

September 25 PhD Program Overview

During this webinar, you will hear from the PhD Program team and have the chance to ask questions about the application and admissions process.

DocNet Recruiting Forum - David Eccles School of Business

MIT Sloan PhD Program will be joining the DocNet consortium for an overview of business academia and a recruitment fair at Utah, David Eccles School of Business.

October PhD Program Overview

Complete PhD Admissions Event Calendar

Unlike formulaic approaches to training scholars, the PhD Program at MIT Sloan allows students to choose their own adventure and develop a unique scholarly identity. This can be daunting, but students are given a wide range of support along the way - most notably having access to world class faculty and coursework both at MIT and in the broader academic community around Boston.

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Profiles of our current students

MIT Sloan produces top-notch PhDs in management. Immersed in MIT Sloan's distinctive culture, upcoming graduates are poised to innovate in management research and education.

Academic Job Market

Doctoral candidates on the current academic market

Academic Placements

Graduates of the MIT Sloan PhD Program are researching and teaching at top schools around the world.

view recent placements

MIT Sloan Experience

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The PhD Program is integral to the research of MIT Sloan's world-class faculty. With a reputation as risk-takers who are unafraid to embrace the unconventional, they are engaged in exciting disciplinary and interdisciplinary research that often includes PhD students as key team members.

Research centers across MIT Sloan and MIT provide a rich setting for collaboration and exploration. In addition to exposure to the faculty, PhD students also learn from one another in a creative, supportive research community.

Throughout MIT Sloan's history, our professors have devised theories and fields of study that have had a profound impact on management theory and practice.

From Douglas McGregor's Theory X/Theory Y distinction to Nobel-recognized breakthroughs in finance by Franco Modigliani and in option pricing by Robert Merton and Myron Scholes, MIT Sloan's faculty have been unmatched innovators.

This legacy of innovative thinking and dedication to research impacts every faculty member and filters down to the students who work beside them.

Faculty Links

Accounting Faculty
Economic Sociology Faculty
Finance Faculty
Information Technology Faculty
Institute for Work and Employment Research (IWER) Faculty
Marketing Faculty
Organization Studies Faculty
System Dynamics Faculty
Technological Innovation, Entrepreneurship, and Strategic Management (TIES) Faculty

Student Research

“MIT Sloan PhD training is a transformative experience. The heart of the process is the student’s transition from being a consumer of knowledge to being a producer of knowledge. This involves learning to ask precise, tractable questions and addressing them with creativity and rigor. Hard work is required, but the reward is the incomparable exhilaration one feels from having solved a puzzle that had bedeviled the sharpest minds in the world!” -Ezra Zuckerman Sivan Alvin J. Siteman (1948) Professor of Entrepreneurship

Sample Dissertation Abstracts - These sample Dissertation Abstracts provide examples of the work that our students have chosen to study while in the MIT Sloan PhD Program.

We believe that our doctoral program is the heart of MIT Sloan's research community and that it develops some of the best management researchers in the world. At our annual Doctoral Research Forum, we celebrate the great research that our doctoral students do, and the research community that supports that development process.

The videos of their presentations below showcase the work of our students and will give you insight into the topics they choose to research in the program.

Attention To Retention: The Informativeness of Insiders’ Decision to Retain Shares

2024 PhD Doctoral Research Forum Winner - Gabriel Voelcker

Watch more MIT Sloan PhD Program Doctoral Forum Videos

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PhD Programs

Accounting & Management
Business Economics
Health Policy (Management)
Organizational Behavior
Technology & Operations Management

Students in our PhD programs are encouraged from day one to think of this experience as their first job in business academia—a training ground for a challenging and rewarding career generating rigorous, relevant research that influences practice.

Our doctoral students work with faculty and access resources throughout HBS and Harvard University. The PhD program curriculum requires coursework at HBS and other Harvard discipline departments, and with HBS and Harvard faculty on advisory committees. Faculty throughout Harvard guide the programs through their participation on advisory committees.

How do I know which program is right for me?

There are many paths, but we are one HBS. Our PhD students draw on diverse personal and professional backgrounds to pursue an ever-expanding range of research topics. Explore more here about each program’s requirements & curriculum, read student profiles for each discipline as well as student research , and placement information.

The PhD in Business Administration grounds students in the disciplinary theories and research methods that form the foundation of an academic career. Jointly administered by HBS and GSAS, the program has four areas of study: Accounting and Management , Marketing , Strategy , and Technology and Operations Management . All areas of study involve roughly two years of coursework culminating in a field exam. The remaining years of the program are spent conducting independent research, working on co-authored publications, and writing the dissertation. Students join these programs from a wide range of backgrounds, from consulting to engineering. Many applicants possess liberal arts degrees, as there is not a requirement to possess a business degree before joining the program

The PhD in Business Economics provides students the opportunity to study in both Harvard’s world-class Economics Department and Harvard Business School. Throughout the program, coursework includes exploration of microeconomic theory, macroeconomic theory, probability and statistics, and econometrics. While some students join the Business Economics program directly from undergraduate or masters programs, others have worked in economic consulting firms or as research assistants at universities or intergovernmental organizations.

The PhD program in Health Policy (Management) is rooted in data-driven research on the managerial, operational, and strategic issues facing a wide range of organizations. Coursework includes the study of microeconomic theory, management, research methods, and statistics. The backgrounds of students in this program are quite varied, with some coming from public health or the healthcare industry, while others arrive at the program with a background in disciplinary research

The PhD program in Organizational Behavior offers two tracks: either a micro or macro approach. In the micro track, students focus on the study of interpersonal relationships within organizations and the effects that groups have on individuals. Students in the macro track use sociological methods to examine organizations, groups, and markets as a whole, including topics such as the influence of individuals on organizational change, or the relationship between social missions and financial objectives. Jointly administered by HBS and GSAS, the program includes core disciplinary training in sociology or psychology, as well as additional coursework in organizational behavior.

Accounting & Management

Business economics , health policy (management) , marketing , organizational behavior , strategy , technology & operations management .

The Darden Ph.D. Program

Ph.d. - data analytics and decision sciences, data analytics and decision sciences.

Research in Data Analytics and Decision Sciences is focused on mathematical methodology with business applications and includes:

Dynamic programming
Game theory
Mechanism design
Machine learning
Optimization
Search theory
Wisdom of crowds
Forecasting
Decision analysis
Pricing and revenue management
Behavioral models of human decision making

Students will be required to take a few core courses but also have the freedom to choose from the many Ph.D. courses that the UVA campus offers, and are encouraged to follow their interests and expand their knowledge. Whatever portfolio they choose, students are expected to be strong in several areas of fundamental theory (e.g., real analysis, probability theory, micro-economics, game theory, decision theory, optimization) as well as methods (e.g., statistics, econometrics, experimental design, coding), so that they can begin exploring novel research questions by the middle of their second year. There is no requirement for prior experience in business applications. Students with a strong quantitative background can be taught all the necessary skills for success in academic research.

Michael Albert

Assistant professor of business administration; areas of expertise: mechanism design, machine learning.

Saed Alizamir

Associate professor of business administration.

Joe Andrasko

Professor of practice.

Manel Baucells

David m. lacross associate professor of business administration; areas of expertise: prescriptive and descriptive decision theory, behavioral models of human decision making.

Assistant Professor of Business Administration; Areas of Expertise: Data-driven Optimization, Machine Learning, Pricing

Robert L. Carraway

Yiorgos allayannis distinguished associate professor of business administration.

Academic General Faculty

Rupert Freeman

Assistant professor of business administration; areas of expertise: computational social choice, fair division, resource allocation, prediction mechanisms.

Yael Grushka-Cockayne

Professor of business administration; areas of expertise: forecasting, forecast aggregation, project management, data science for business.

Dana Popescu

Associate professor of business administration; areas of expertise: pricing optimization, revenue management and demand forecasting.

Assistant Professor of Business Administration; Areas of Expertise: Dynamic Models of Decision-Making Over Time, Search Theory, Game Theory, Incentive Design, Healthcare Applications

Quantitative Psychology

Information about the Quantitative Psychology Graduate Major

Quantitative psychology provides an opportunity for students to specialize in measurement, methodology and research design and analyses relevant to data in the social sciences. Psychology faculty currently includes Peter Bentler, Han Du, Craig Enders, Amanda Montoya, and Steven Reise. Key areas of interest among the faculty are structural equation modeling, item response theory, multilevel modeling, and the analysis of fMRI data.

The quantitative major at UCLA Psychology is a highly individualized program providing ample opportunity for one-on-one interaction with faculty. Students concentrating in quantitative psychology will generally fit into one of two categories. The first of these consists of students possessing excellent mathematical backgrounds and strong theoretical interests in technical problems in measurement theory, statistics, and modeling. The second group of students typically has more applied interests. While the latter group of students have preparation in mathematics, these students are more oriented toward the use of psychometric and analytic techniques in substantive research. Some students find it compatible to give equal attention to both these major aspects of the program. Students in the quantitative program are strongly encouraged to collaborate with faculty in substantive areas of psychology in addition to their quantitative training. These areas include but are not limited to couples analysis, longitudinal and diary data, health outcomes, and the biological underpinnings of psychopathology.

During the first year of graduate work, quantitative psychology students will be exposed to a broad spectrum of courses covering the major fields of psychology. Some time during this year will also be devoted to research activities and quantitative coursework. Concentration in the quantitative area will be more intense during the second year. At a minimum, students are expected to take course work in traditional measurement, item response theory, latent variable modeling, multivariate analysis, and hierarchical linear modeling. Additional coursework such as classes on factor analysis, statistical analysis of fMRI data, and intervention design and analysis are strongly encouraged. In addition to coursework in the Psychology Department, quantitative psychology students often take quantitative courses in other departments, including Education, Statistics, and Biostatistics.

More Quantitative Psychology Information

For a list of Required Courses please see the Psychology Handbook

COMMENTS

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Program Details
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Psychology (Quantitative Research Methods), PhD
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Ph.D. in Psychometrics and Quantitative Psychology
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Ph.D. Program Requirements
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Quantitative Biology
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Quantitative Methods Home
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PhD Program
MIT Sloan PhD Program graduates lead in their fields and are teaching and producing research at the world's most prestigious universities. Rigorous, discipline-based research is the hallmark of the MIT Sloan PhD Program. The program is committed to educating scholars who will lead in their fields of research—those with outstanding ...
PhD Programs
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Ph.D.
A 12-month program in partnership with UVA's McIntire School of Commerce focused on statistical analysis and leadership. ... Students with a strong quantitative background can be taught all the necessary skills for success in academic research. Data Analytics and Decision Sciences Michael Albert Assistant Professor of Business Administration ...
Quantitative Psychology • UCLA Department of Psychology
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