Contribute meaningful work to multiple fields of psychology while you explore and apply scientific methods to human development, social interactions, and behavioral relationships in an experimental psychology master's degree targeted toward your career aspirations.
In the experimental psychology master's degree, students are trained to apply scientific methods to basic psychological processes in perception, brain and behavior relationships, thinking, memory, learning, social interactions, human development and related areas. It is a broad and flexible program that provides a solid stepping-stone into careers or further study in psychology. A choice of tracks–in experimental psychology or engineering psychology–allows students to specialize their degree toward their career goals and aspirations.
The MS program in experimental psychology builds on the strengths of faculty research and student interests in experimental psychology broadly defined. The program as a whole provides a foundation for further advanced academic study in human factors and/or experimental psychology.
Plan of study
The program includes core courses, elective courses, and a thesis. It also offers students two tracks to choose from: experimental psychology and engineering psychology.
The experimental psychology track embraces the application of the scientific method to the study of behavior. Faculty are experts in a variety of fields including addiction, attention, cognition, development, evolutionary psychology, forensic psychology, perception, psychopathology, and social psychology, among others.
The engineering psychology track examines human capabilities to sense, perceive, store, and process information and how these human factors impact interactions with technology. This knowledge is applied to the design, use, and maintenance of human-machine systems. Courses emphasize the role of human behavior and performance in both simple and complex human-machine systems. Students are trained in both research methods of experimental psychology and application of the results to contemporary problems in industry. This track prepares students to function as effective engineering psychologists in industrial, governmental, or consulting organizations.
Students in the engineering psychology track must select two electives (students should check for course prerequisites or if permission of the instructor is required). Any graduate course at RIT can be taken as an elective, assuming prerequisites are met.
Students select a thesis adviser during the first year. Selection of an advisor, thesis topic, and research proposal must be completed in the second semester of the first year of the program. Ongoing research activity is expected through the summer term of the program. At the completion of the thesis, students will publicly present their findings and defend their research before a thesis committee.
Government (Local, State, Federal)
Typical Job Titles
Cooperative education, or co-op for short, is full-time, paid work experience in your field of study. And it sets RIT graduates apart from their competitors. It’s exposure–early and often–to a variety of professional work environments, career paths, and industries. RIT co-op is designed for your success.
The program includes an optional cooperative education component. Co-op is generally completed in the summer after the first year of the program. The co-op experience provides experiential learning that integrates with classroom education and allows students to apply psychological principles to problems in a variety of work environments. Co-op may be completed in any business or industrial setting.
Experimental Psychology, MS degree, typical course sequence
Sem. Cr. Hrs.
This course reviews descriptive and inferential statistics. Basic and advanced conceptual material will be presented to assist students in their understanding of diverse data analytic methods, their appropriate application, and how to interpret statistical analyses. Topics include one- and two-sample inferential procedures, interval estimation, correlation, nonparametric tests, linear regression, and analysis of variance. Students will learn to integrate concepts with computer applications. Course content will be taught through lectures, discussion, and applied data analysis exercises. Student mastery of the material will be evaluated through small group discussion of data set analyses, written results of the analyses following APA style, and two exams. Lecture (Fall, Spring, Summer).
Graduate Research Seminar
The guiding principle of Graduate Research Seminar is that it provides students the opportunity to begin examining potential thesis topics during the student's first semester in the program. The course will involve faculty presentations of their research offered weekly through the semester. (This course is restricted to EXPSYC-MS Major students.) Seminar (Fall).
The Thesis courses will vary widely but will fulfill the work plan agreed by the student and the adviser. The guiding principles of the Thesis Proposal course are to initiate thesis research including selecting a thesis advisor, choosing and defining a topic, surveying relevant research literature, and planning the research. To complete the course, the student will successfully submit and defend a thesis proposal, which is a detailed and complete plan of the thesis research. The thesis proposal should include exhaustive review of relevant literature, statement of the student's thesis, formulation of hypotheses, operational definitions of independent and dependent variables, and a detailed procedure for carrying out the research. The proposal may also include a section on anticipated results with a detailed plan for analysis of data. (This course is restricted to EXPSYC-MS Major students.) Thesis (Spring).
Choose one of the following:
Applied Psychology Methods
This course explores various types of applied research methods as well as important methodological issues and concepts in areas of applied psychology. Methodologies studied include experimentation, quasi-experimentation, content analysis, surveys, and interviews. Methodological issues cover research ethics, reliability, threats to internal and external validity, demand characteristics, volunteer participant problems, and issues in sampling. Lecture (Fall).
Graduate Research Methods
This course provides students with sufficient background in the skills and knowledge necessary to be able to conduct psychological research on a wide variety of problems. In addition to introducing students to numerous research methods used in the discipline, the course will also assist students in planning their thesis research proposal. In parallel with covering core topics in research methodology (such as varieties of data, the role of theory and models in science, psychophysiological methods, subjective methods, and experimental design) the course is designed to guide students through the process of creating a feasible research proposal. Students will also use data to test their designs and practice their analyses. (This course is restricted to EXPSYC-MS Major students.) Lecture (Fall, Spring, Summer).
In this course the students will learn to recognize the integrated (systems) nature of Engineering Psychology, the centrality of human beings in systems design, and to use the topics covered and the available knowledge base to adapt the environment to people. This course will cover several fundamental models of human information processing in the context of human-system interactions. The models may include but are not limited to Signal Detection Theory, Information Theory, theories of attention, both normative and naturalistic decision-making models, Control Theory, and the Lens Model of Brunswick, as well as models of the human as a physical engine, that is, anthropometry, biomechanics, and work physiology. Most topics include readings in addition to the course text as well as a lab exercise with a detailed lab report. Seminar (Biannual).
PSYC Elective (Experimental track)
The Thesis courses will vary widely but will fulfill the work plan agreed by the student and the thesis adviser. The guiding principle of the Thesis course is to complete the thesis research proposed in Thesis Proposal. The Thesis course consists of carrying out the thesis research, including collection and analysis of data, and completion and public defense of the thesis document for partial fulfillment of the requirements of the degree. (This course is restricted to EXPSYC-MS Major students.) Thesis (Fall).
Choose one of the following:
Total Semester Credit Hours
This course explores marine biology by focusing on the diversity of life and influence of oceanographic phenomena on the various ecosystems. Morphological and physiological adaptations along with environmental threats will also be investigated. The course will explore marine conservation issues, in depth. (Prerequisites: BIOL-240 or equivalent course or graduate student standing in the ENVS-MS program.) Lecture 4 (Fall).
Advanced Conservation Biology
This course focuses on the application of ecological principles to conservation issues. Human impact on species diversity will be emphasized as it relates to agricultural, forest, coastal and wetland ecosystems. Case studies of management practices used to manage and restore disturbed ecosystems will be included. Students will explore a topic in depth through writing a review paper of published literature. (Prerequisites: BIOL-240 or equivalent course or graduate student standing in the ENVS-MS program.) Lecture 3 (Spring).
This course provides students with an introduction to the practical application of various research methods that can be used in human computer interaction. The course provides an overview of the research process and the literature review, and provides experience with qualitative, survey, and experimental research methods. Students will study existing research and design and conduct studies. Students will need to have taken a statistics course before registering for this class. (Prerequisites: DECS-782 or STAT-145 or equivalent course.) Lecture 3 (Fall, Spring).
Foundations of Human-Computer Interaction
Human-computer interaction (HCI) is a field of study concerned with the design, evaluation and implementation of interactive computing systems for human use and with the study of major phenomena surrounding them. This course surveys the scope of issues and foundations of the HCI field: cognitive psychology, human factors, interaction styles, user analysis, task analysis, interaction design methods and techniques, and evaluation. This course will focus on the users and their tasks. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall, Spring).
Information and Interaction Design
Designing meaningful relationships among people and the products they use is both an art and a science. This course will focus on the unique design practice of: representing and organizing information in such a way as to facilitate perception and understanding (information architecture); and, specifying the appropriate mechanisms for accessing and manipulating task information (interaction design). This course will also explore the various design patterns (design solutions to particular problems) that are appropriate for the HCI professional. Students will need prior knowledge of an interface prototyping tool. (Prerequisite: ISTE-200 or equivalent course.
Co-requisite: HCIN-610 or equivalent course.) Lecture 3 (Fall, Spring).
This project-based course will focus on the formal evaluation of products. Topics include usability test goal setting, recruitment of appropriate users, design of test tasks, design of the test environment, test plan development and implementation, analysis and interpretation of the results, and documentation and presentation of results and recommendations. (Prerequisites: HCIN-600 and HCIN-610 or equivalent courses.) Lecture 3 (Spring, Summer).
Computer software that teaches is referred to as courseware. This course is a continuation of HCIN-660 that transitions from general instructional design into the actual application of these principles in a computer-based environment. Although the basic principles of instructional design hold true in all media environments, using these teaching and learning principles is somewhat different when developing instruction that will be delivered by computer. This course teaches procedures that have already been successful in the design and development of courseware. Successful students should have one year of object-oriented programming. (Prerequisites: HCIN-660 or equivalent course.) Lecture 3 (Spring).
Current Topics in HCI
Human-Computer Interaction (HCI) is an evolving field. This course is designed to study the current themes and advanced issues of HCI. Topics will vary depending upon current research and developments in the field. Lecture 3 (Spring).
Topics in HCI for Biomedical Informatics
This course will provide a theoretical and case-based study of several areas of HCI, all considered within the application domain of biomedical informatics. Course topics include a scientific approach to UI design (usability engineering), domain-specific user analysis and user profiles, social and cultural influences, general and domain-specific design issues, information visualization, data integration, mobile devices, security, privacy, and ethics. (Prerequisites: HCIN-610 or equivalent course.) Lecture 3 (Spring).
Agent-Based and Cognitive Modeling
This course is intended as an introduction to the emerging areas of agent-based modeling and cognitive modeling. Both modeling approaches are at the intersection of research (theory development and confirmation) and computational simulation. This course will be an introduction to these topics, focusing on the research aspects of agent-based modeling and the development and testing of cognitive models. The role of visualization in modeling development and analysis is presented. Students will analyze the social science literature for current models and theories and will develop computational models incorporating these theories. (Prerequisites: HCIN-600 or equivalent course.) Lec/Lab 3 (Spring).
Prototyping Wearable and Internet of Things Devices
Wearable computers and Internet of Things devices involve both hardware and software. In order to design user experiences for these systems, professionals must understand how they are built. Students will learn how to rapidly prototype and evaluate wearable and IoT devices combining hardware and software. Experience in programming is helpful but not a prerequisite. Lecture 3 (Fall).
Human-Computer Interaction with Mobile, Wearable, and Ubiquitous Devices
Mobile phones are now a major computing platform, and wearable and Internet of Things devices are emerging as major technologies. Each device offers different interaction opportunities and challenges. Students will learn about the research in interaction with these devices and how to design effective interactions for mobile, wearable, and ubiquitous devices. (Prerequisites: HCIN-610 or equivalent course.) Lecture 3 (Spring).
User-Centered Design Methods
This course will focus on the major user centered design methodologies used in the development of applications and environments. Topics include: evolution of software design methods, emergence of user-centered design, and key concepts, attributes and process of the major design methodologies. Software design projects will be required. (Prerequisites: HCIN-610 or equivalent course.) Lecture 3 (Spring).
Collaboration, Technology, and the Human Experience
Students will examine the role of technology and group collaboration in organizations. An overview of relevant theory, current and emergent technologies, and trends in collaborative science will provide the context for strategic implementation and development of collaborative environments. Group projects using collaborative technologies will be required. (Prerequisites: HCIN-600 and HCIN-610 or equivalent courses.) Lecture 3 (Spring).
Program Evaluation and Design
This course teaches the systematic application of social research procedures to evaluate the conceptualization, design, implementation, and utility of human resource development programs. (This course is restricted to student in the HRDE-MS program.) Lecture 3 (Fall, Spring).
Biomechanics of Human Movement
Students collect data in the lab and conduct the data analysis using MATLAB software. Topics include skeletal anatomy and mechanics, muscle anatomy and mechanics, theory and application of electromyography, motion and force measuring equipment and techniques, human locomotion, balance and falls, inverse dynamics modeling of the human body, and current topics in musculoskeletal biomechanics research (Prerequisites: ISEE-330 or MECE-320 or BIME-200 or equivalent course or graduate standing in ISEE-MS, ISEE-ME, SUSTAIN-MS, SUSTAIN-ME, ENGMGT-ME, MECE-MS or MECE-ME program required.) Lecture 3 (Fall).
Advanced Topics in Human Factors and Ergonomics
Advanced topics are selected based on current ergonomic and human factors issues and interests of students. Course is taught using a seminar format. Students are required to select, read, and discuss scientific literature relevant to the fields of human factors and ergonomics. (Prerequisites: ISEE-330 or (0303-415 and 0303-516) or equivalent course or graduate standing in ISEE-MS, ISEE-ME, SUSTAIN-MS, SUSTAIN-ME or ENGMGT-ME program.) Lecture 3 (Spring).
Systems Safety Engineering
Acquaints students with practical aspects of safety engineering. Students acquire a working knowledge of legal and technical aspects of safety. Focuses on a systems approach to safety engineering. Topics include Workers Compensation, OSHA, Consumer Product Safety Commission, NIOSH Guidelines and various hazard analysis and utilization techniques. Students also are exposed to various theories of accident causation, research methodology and ways of evaluating safety programs and related research. (This course is restricted to students in the ISEE BS/MS, ISEE BS/ME, ISEE-MS, ISEE-ME, SUSTAIN-MS, SUSTAIN-ME or ENGMGT-ME programs or those with at least 4th year standing in ISEE-BS.) Lecture 3 (Spring).
This course is an introduction to the probabilistic models and statistical techniques used in the analysis of biological and medical data. Topics include univariate and multivariate summary techniques, one and two sample parametric and nonparametric inference, censoring, one and two way analysis of variance, and multiple and logistic regression analysis. (This course is restricted to students in the ACMTH-MS or MATHML-PHD or BIOINFO-MS programs.) Lecture 3 (Fall).
Marketing Concepts and Commercialization
An introduction to contemporary principles and practices of marketing. The course is structured around the process of marketing planning leading to the development of successful marketing strategies, including the commercialization of products and services in domestic and international environments. Focus is on environmental scanning techniques, setting and evaluating measurable objectives, innovating and controlling the interrelated components of product/service offering, planning and executing the marketing mix (channels of distribution, price, and promotion), and enhancing customer relationships through the delivery of customer value. Lecture 3 (Fall, Spring, Summer).
Teaching Deaf and Hard of Hearing Learners with Special Educational Needs
This course focuses on providing students with basic information regarding the needs of deaf and hard of hearing learners with special educational needs, including (1) developmental disability, (2) emotional or behavioral disorder (3) learning disability, attention deficit disorder or attention deficit hyperactivity disorder, or (4) visual impairment. Topics include incidence, identification, assessment, teaching strategies, and working with parents. The goal is to enable students to see students in a holistic fashion, and thus will include the perspectives of parents, teachers and deaf and hard of hearing learners with special educational needs. Learning strategies may include site visits, presentations, films, and interactive workshop style classes offered by experienced teachers, psychologists, counselors, disability advocates, and parents of learners with special educational needs. The course will regularly incorporate guest lecturers who have specialized expertise in teaching or research in one or more of the topic areas covered in the course. (Prerequisites: MSSE-703 or equivalent course and graduate standing in SEDDEAF-MS.) Lecture 3 (Spring).
This course concentrates on the development of theory and applied skills in intellectual assessment. Students learn to select and administer individual intelligence tests, to interpret results, to form test-based recommendations for intervention, and to provide written and oral reports. Assessment of persons who are culturally different or disabled is emphasized. (This course is restricted to SCPSYC-ACT or SCPSYC-MS Major students.) Lecture (Spring).
This course uses interviews, behavioral observations, rating scales, and projective measures for the assessment of child and adolescent personality and adaptive behavior. Students gain experience in administering, interpreting, and reporting results of measures currently used in the practice of psychology in the schools. Lecture (Spring).
Applied Behavior Analysis
This course reviews scientifically-based principles, concepts, and methods of behavior analysis. Topics covered include behavioral assessment, data analysis, and approaches to behavior change. A special focus is on the functional behavioral assessment process within schools. Students will learn to develop assessment-based behavior intervention plans, which are tailored to the unique needs of individual students, through a collaborative problem-solving process involving families and school staff. (This course is restricted to SCPSYC-ACT or SCPSYC-MS Major students.) Lecture (Spring).
A graduate level introduction to the field of behavioral neuroscience, the study of neurobiological basis of cognition and behavior. Topics include neuroanatomy and physiology, localization of function, brain injury, research methods in behavioral neuroscience, and biological basis of learning, language, memory, emotion, conscious states, sexual behavior, etc. Lecture (Spring).
This course will survey theoretical and empirical approaches to understanding the nature of the mental processes involved in attention, object recognition, learning and memory, reasoning, problem solving, decision-making, and language. The course presents a balance between historically significant findings and current state of-the-art research. Readings that have structured the nature and direction of scientific debate in these fields will be discussed. The course also includes discussions of methodology and practical applications. Students will have opportunities to develop their research skills and critical thinking by designing research studies in cognitive psychology. Seminar (Spring).
Graduate Development Psychology
This course is designed to enhance students' knowledge and skills with regard to infant, child, and adolescent development. We will examine a variety of topics that relate to the physical, cognitive, and social-emotional development of children and adolescents in the context of classic and current theory. We will also explore issues such as attachment, resiliency, and policy issues that pertain to positive child and adolescent development. Students will gain an enhanced knowledge of the sequence of child development and the processes that underlie it by studying child development from a chronological approach. Theories that discuss the various domains of development will be examined through each age period. This course will emphasize the interdependence of all domains of development and contribute to an appreciation of the interrelatedness of theory, research, and applications. Seminar (Fall).
The course is designed to provide students with a deeper understanding of topics in perception. This course will be organized such that students will work in groups on various projects as well as covering topics through readings and classroom discussion. The topics may include, but are not limited to: spatial frequency perception; aftereffects, visual illusions and their relationship to cortical function and pattern perception; color perception; depth and motion perception; higher order perception such as face and object recognition; and music and speech perception. The goal is to cover current research and theories in perception, looking at current developments and their antecedents. The course will be divided into various modules. Students will be assigned readings relevant to each section of the course, and will be expected to master the major concepts. Group discussion of the readings will complement lectures where the instructor will present relevant background material. There will also be laboratory time for the students, where they will examine empirical findings in perception, and develop their research skills in the field. Lecture (Biannual).
Graduate Social Psychology
This course explores topics related to understanding individuals in a social context. Topics may include, but are not limited to: Social Perception and Social Cognition; Attitudes; Social Identity; Prejudice and Discrimination; Interpersonal Attraction; Close Relationships; Social Influence; Prosocial Behavior; Aggression; Group Behavior; Artifacts and Methodological Issues in Social Psychology. Course format is seminar focused on reading assigned texts each week, writing reaction papers, and participating in discussion. Students will also conduct a study on the topic of their choice and present their findings both in an oral and written format. Seminar (Biannual).
Advanced Graduate Statistics
This course introduces students to more advanced inferential parametric and non-parametric data-analysis techniques commonly used in psychological research, but not covered (or not covered in depth) in the Graduate Statistics course. These techniques may include, but are not limited to: Reliability Analysis, Multiple Regression, Discriminant Analysis, Logistic Regression, Factor Analysis, Analysis of Covariance, Multivariate Analysis of Variance, Contrast Analysis, Mediator and Moderator Variable Analysis, Non-Parametric Tests, and Multi-level Modeling. The focus is on the conceptual understanding of these statistics, how different statistical procedures are applied in different research methods, how to perform analyses, how to interpret the results in the context of the research question, and how to communicate these results. (Prerequisites: PSYC-640 or equivalent course.) Lecture (Biannual).
Clinical and Experimental Neuropsychology
A graduate level introduction to the fields of clinical and experimental neuropsychology. Topics include the historical and theoretical underpinnings of modern neuropsychology and methods used to assess cognitive function including their selection, application, and interpretation. Disorders associated with damage to the brain and how they are assessed and managed will also be covered. Seminar 3 (Biannual).
This course focuses on the development of beginning competencies in consultation that will help students assist school professionals in building capacity to deliver effective services. Contextual influences on school consultation, models of consultation, and the stages of the consultation process within a problem-solving model will be emphasized. Issues relevant to individual case and classroom consultation will be covered. (Prerequisites: PSYC-620 or equivalent course.) Lecture (Fall).
Most referrals to school psychologists involve some sort of learning problem. What variables affect school learning? Are some influences more important than others? Which of these influences are alterable and therefore available as interventions to improve learning? What classroom strategies work best? We will examine theories of school learning and the basic psychological principles that apply to teaching and learning. This will be accomplished through the examination of the role of teachers, which includes their responsibility for teaching curriculum, classroom management, and the social and emotional growth of students. Students will learn to critically evaluate the instruction provided to a particular student in a given content area. In addition, students will learn to assess academic functioning within the learning environment, identify specific target areas for intervention, set appropriate goals and objectives, monitor student progress toward those goals and objectives, and evaluate the effectiveness of the intervention(s) in place as a result of the assessment. Students are expected to leave this course with a cursory understanding of the problem-solving process and the development and monitoring of effective interventions, and basic competence in applying this process. (Prerequisites: PSYC-630 or equivalent course.) Lecture (Spring).
Systems and Organizational Interventions
This course will assist students in building their consultation skills, with an explicit focus on systems-level issues and interventions. Students will learn principles of population-based prevention and intervention services and family-school collaboration. An array of evidence-based schoolwide interventions will be explored in depth with a focus on the role of the school psychologist within the larger system. (Prerequisites: PSYC-620, PSYC-630, PSYC-650 and PSYC-721 or equivalent courses.) Lecture 3 (Spring).
This course is an introduction to two statistical-software packages, SAS and R, which are often used in professional practice. Some comparisons with other statistical-software packages will also be made. Topics include: data structures; reading and writing data; data manipulation, subsetting, reshaping, sorting, and merging; conditional execution and looping; built-in functions; creation of new functions or macros; graphics; matrices and arrays; simulations; select statistical applications. (This course is restricted to students in APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Fall, Spring).
Applied Linear Models - Regression
A course that studies how a response variable is related to a set of predictor variables. Regression techniques provide a foundation for the analysis of observational data and provide insight into the analysis of data from designed experiments. Topics include happenstance data versus designed experiments, simple linear regression, the matrix approach to simple and multiple linear regression, analysis of residuals, transformations, weighted least squares, polynomial models, influence diagnostics, dummy variables, selection of best linear models, nonlinear estimation, and model building. (This course is restricted to students in APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Fall, Spring).
Applied Linear Models - ANOVA
This course introduces students to analysis of models with categorical factors, with emphasis on interpretation. Topics include the role of statistics in scientific studies, fixed and random effects, mixed models, covariates, hierarchical models, and repeated measures. (This class is restricted to students in the APPSTAT-MS, SMPPI-ACT, STATQL-ACT or MMSI-MS programs.) Lecture 3 (Fall, Spring).
Multivariate data are characterized by multiple responses. This course concentrates on the mathematical and statistical theory that underlies the analysis of multivariate data. Some important applied methods are covered. Topics include matrix algebra, the multivariate normal model, multivariate t-tests, repeated measures, MANOVA principal components, factor analysis, clustering, and discriminant analysis. (Prerequisites: This class is restricted to students in APPSTAT-MS or SMPPI-ACT who have successfully completed STAT-611 or equivalent course.) Lecture 3 (Fall, Spring).
Design and Analysis of Clinical Trials
This is a graduate level survey course that stresses the concepts of statistical design and analysis for clinical trials. Topics include the design, implementation, and analysis of trials, including treatment allocation and randomization, factorial designs, cross-over designs, sample size and power, reporting and publishing, etc. SAS for Windows statistical software will be used throughout the course for data analysis. (This course is restricted to students in APPSTAT-MS or SMPPI-ACT.) Lecture 3 (Fall, Spring).
Graduate Biodiversity and Society
This course explores the problems, issues, and values stemming from the current massive loss of biodiversity. Various justifications for preserving or conserving biodiversity will be examined. Although principals of conservation biology are presented, the social/cultural dimensions of the issue will be emphasized. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture (Spring).
To be considered for admission to the MS in experimental psychology, candidates must fulfill the following requirements:
Hold a baccalaureate degree (or equivalent) from an accredited university or college.
Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
Have a minimum cumulative GPA of 3.0 (or equivalent)
Have a minimum of 15 semester hours of course work in undergraduate psychology or a related field (e.g., engineering, computer science, information technology), including one course in experimental psychology and one course in statistics.
Submit scores from the GRE.
Submit a personal statement describing the applicant’s goals for the program, focusing on their research interests and possible thesis research (including possible thesis mentors).
Submit two letters of recommendation from academic or professional sources.
International applicants whose native language is not English must submit scores from the TOEFL, IELTS, or PTE. A minimum TOEFL score of 79 (internet-based) is required. A minimum IELTS score of 6.5 is required. The English language test score requirement is waived for native speakers of English or for those submitting transcripts from degrees earned at American institutions.
Faculty in the department of psychology focus their research on a wide variety of topics across the discipline. They work closely with students to pursue their research and advise on thesis work. Learn more by exploring our psychology research areas.