Experimental Psychology Master of Science Degree

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.


Outcomes Rate of RIT Graduates from this degree

Overview for Experimental Psychology MS

Why Study Experimental Psychology at RIT?

  • Customize Your Program: Choose between two dynamic tracks: experimental psychology or engineering psychology.
  • Flexible Options to Complete Your Degree: Choose between a thesis or capstone project to earn your master's in experimental psychology.
  • Preparation for Advanced Study: Receive a strong foundation in human factors and/or experimental psychology to prepare you for further studies if desired.

The experimental psychology master's degree is a broad and flexible program that provides a solid stepping-stone into careers in experimental psychology or further study in psychology. A choice of tracks–in experimental psychology or engineering psychology–allows you to customize the program around your career goals and aspirations.

What is Experimental Psychology?

In experimental psychology, you 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. RIT's master's in experimental psychology builds on the strengths of faculty research and student interests in experimental psychology broadly defined. The experimental psychology graduate program, as a whole, provides a foundation for further advanced academic study in human factors and/or experimental psychology.

Master's in Experimental Psychology Courses

The experimental psychology degree includes core courses, elective courses, and a thesis. It also offers 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. You are trained in both research methods of experimental psychology and the application of the results to contemporary problems in industry. This track prepares you to function as an effective engineering psychologist in industrial, governmental, or consulting organizations.

Electives: If you choose the engineering psychology track, you must select two electives. Any graduate course at RIT can be taken as an elective, assuming prerequisites are met.

Capstone or Thesis: As part of the master's in experimental psychology, you will choose either a capstone project or a thesis. Students who select the capstone project will embark on a range of projects to demonstrate their ability to apply this knowledge in various assignments. A variety of written projects (white paper, focused literature review, and a resume) and an oral presentation are required for you to show proficiency in your area of expertise.

Students who select to complete a thesis will select a thesis adviser in the first year, followed in the second year by a thesis topic and research proposal. You will conduct your thesis, including the collection and analysis of data, in the second year. Ongoing research activity is expected through the summer term of the program. At the completion of the thesis, you will publicly present your findings and defend your research before a thesis committee.

Students are also interested in: Human-Computer Interaction MS, Engineering Psychology Adv. Cert.


Careers and Experiential Learning

Typical Job Titles

Research Associate User Experience Researcher
Data Analyst Human Factors Researcher
Lab Director Specialist

Cooperative Education and Internships

What makes an RIT education exceptional? It’s the ability to complete relevant, hands-on career experience. At the graduate level, and paired with an advanced degree, cooperative education and internships give you the unparalleled credentials that truly set you apart. Learn more about graduate co-op and how it provides you with the career experience employers look for in their next top hires.

Co-ops take your knowledge and turn it into know-how. A liberal arts co-op provides hands-on experience that enables you to apply your knowledge in professional settings while you make valuable connections between course work and real-world applications.

The experimental psychology master's degree 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.

Featured Work

Featured Profiles

Curriculum Update in Process for 2024-2025 for Experimental Psychology MS

Current Students: See Curriculum Requirements

Experimental Psychology, MS degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
Graduate Statistics
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 3 (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).
Choose one of the following:
 Thesis Proposal (Thesis Option)
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).
 Specialized PSYC Elective (Non-Thesis Option)
PSYC Elective
Institute Electives
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 3 (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 3 (Fall, Spring, Summer).
Choose one of the following:
Graduate Engineering Psychology (Engineering Psychology track)
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)
Second Year
Choose one of the following:
 Thesis (Thesis Option)
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).
 Graduate Psychology Capstone (Non-Thesis Option)
This is a project-based course for students enrolled in the MSc Experimental Psychology non-Thesis track focusing on discipline-specific scientific communication skills in the area of Psychology. The capstone course will provide students the opportunity to combine and incorporate knowledge and skills learned in prior coursework and experiences and demonstrate their ability to apply this knowledge in various assignments. A variety of written projects (white paper, focused literature review, and a resume) and an oral presentation will be required and should allow students to demonstrate proficiency in the Program. Project 3 (Fall, Spring).
PSYC Elective
Choose one of the following:
PSYC Elective
Institute Elective
Total Semester Credit Hours

PSYC Electives

Natural Language Processing I
This course provides theoretical foundation as well as hands-on (lab-style) practice in computational approaches for processing natural language text, for problems that involve natural language meaning and structure. The course has relevance to cognitive science, artificial intelligence, and science and technology fields. Machine learning, including standard and recent neural network methods, is a central component of this course. Students will develop natural language processing solutions individually or in teams using Python, and explore additional relevant tools. Expected: Programming skills, demonstrated by coursework or instructor approval. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Fall).
Natural Language Processing II
Study of a focus area of increased complexity in natural language processing. The focus varies each semester. Students will develop skills in computational linguistics analysis in a laboratory setting, according to professional standards. A research project plays a central role in the course. Students will engage with relevant research literature, research design and methodology, project development, and reporting in various formats. (Prerequisite: PSYC-681 or (IDAI-610 and IDAI-620) or equivalent courses.) Lecture 3 (Spring).
Graduate Speech Processing
This course introduces students to speech and spoken language processing with a focus on real-world applications including automatic speech recognition, speech synthesis, and spoken dialog systems, as well as tasks such as emotion detection and speaker identification. Students will learn the fundamentals of signal processing for speech and explore the theoretical foundations of how human speech can be processed by computers. Students will then collect data and use existing toolkits to build their own speech recognition or speech synthesis system. This course provides theoretical foundation as well as hands-on laboratory practice. Expected: Programming skills, demonstrated by coursework or instructor approval. Lecture 3 (Fall).
Graduate Biopsychology
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 3 (Spring).
Graduate Cognition
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).
Graduate Perception
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 3 (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 3 (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).
Human Factors in Artificial Intelligence
This course will provide students with fundamental information for human-centered design of applications of artificial intelligence. There are three parts to the course: The first part is about methods of design and evaluation. The second part introduces students to the psychology of sensation and perception, memory, attention, judgment, decision-making, and problem solving, as well as human error and reliability. Finally, students will become familiar with design principles as they apply to displays and controls, human-computer interaction, human-automation interaction, and human-centered automation. Guest lectures and case studies will be examined to illustrate topics covered in it and to provide a survey of the current state of AI research, development, and controversies. Ethics and moral responsibility in technology development, with links to current policy debates, are also discussed in this context. Lecture 1 (Fall).
Graduate Special Topics in Psychology
This course is designed to allow the student to focus on a given special topic or area of research relative to school psychology. Such topics or activities may include selected readings, assessment techniques, direct intervention skills, or indirect intervention skills. This course may be offered from 1 to 3 credit hours depending on the specific topic covered. (This course is restricted to EXPSYC-MS Major students.) Seminar 3 (Fall, Spring).
Advanced Research in Psychology
Practicum open to MSc Experimental Psychology students. This course gives the student first-hand experience in the field of Psychology. The experience may involve a specific research project or other relevant professional development projects independent of the student’s thesis research. Students are closely supervised by a faculty member and will develop skills and gain experience in relevant advanced research and professional development in Experimental Psychology. (This course is restricted to EXPSYC-MS Major students.) Research 3 (Fall, Spring, Summer).

Institute Electives

Marine Biology
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).
Foundations in Research
This introduction to research comprises two parts. The first part introduces interdisciplinary cognitive science and its impact in society together with foundational notions about the research process (including responsible conduct of research) and publication practices and grant writing. The second part provides an entry point to later methods courses by establishing shared computational foundations. Lecture 3 (Fall).
Laboratory Methods
Scientists use a wide range of experimental methods to elucidate the function of the human brain and mind. This course will provide an overview of these methods, in order to allow students to understand a wide range of scientific studies and to be able to select an appropriate method for a specific research topic. Such methods include neuroimaging, psychophysiology, single-cell recordings, computational modeling, and cognitive psychology and behavioral methods that use measures such as response time and decision accuracy to test theories concerning the nature of mental processes and representations. Lecture 3 (Fall).
Foundations of Scientific Computing
This course will introduce students to foundational concepts in numerical computation that are useful for engineering and the mathematical, computational, and physical sciences. Topics will include floating-point arithmetic, error analysis, linear and nonlinear equations, numerical solution of systems of algebraic equations, constrained and unconstrained optimization, polynomial interpolation, numerical differentiation and integration, numerical solution of ordinary differential equations, truncation error, and basic methods for sampling stochastic processes. Implementation of various numerical methods and solvers will be done in Python, MATLAB, and R. Connections to computational modeling of cognition will be made throughout the course as motivating examples for various key concepts and tools. (Prerequisites: COGS-600 and (PSYC-640 or PSYC-717) or equivalent courses.) Lecture 3 (Fall).
Philosophical Foundations of Cognitive Science
This course will introduce students to the philosophical foundations of cognitive science. Topics will include the nature and distribution of consciousness, including cognitive, neurobiological, and informational theories; theories of cognition, including computational-representational and non-representational “EEE” (embodied/emergent/enactive) theories; theories of emotion, affect, valence, and motivation; theories of action and agency, and evolutionary theory. All of these discussions will be cutting-edge research in human and nonhuman animal cognition. The class will also include a discussion of competing conceptual, inferential, and conceptual strategies across the disciplines that comprise cognitive science. (Prerequisites: COGS-600 or equivalent course.) Lecture 3 .
Foundations of Cognitive Modeling
This course will introduce students to the mathematical and philosophical foundations of cognitive modeling as well as the key concepts and tools needed for developing and applying cognitive architectures. Furthermore, the course will survey seminal papers as well as leading computational frameworks used in understanding human cognition and intelligence.Topics will include fundamentals of signal detection theory, probability modeling and information theory, the Lens Model, statistical (Bayesian) modeling of various cognitive actions and behavior, dynamical systems, symbolic and sub-symbolic representations, and simulation using artificial neural networks. Students will learn how to use one or more major cognitive architectures, e.g., MicroSAINT, Act-R, Soar, Nengo, and build basic computational models of cognitive processes, including those related to categorization, language, memory, decision making, and reasoning, fitting and evaluating their models to different kinds of behavioral data. Lecture 3 (Biannual).
Graduate Psycholinguistics
This graduate seminar gives an advanced introduction to psycholinguistics which investigates the cognitive mechanism that allows humans to produce, comprehend and learn language. It discusses some of the central topics in the field, including (1) an historical overview of the field; (2) a survey of behavioral, neurological, and computational methodologies; (3) a review of empirical results and theoretical perspectives; (4) a roadmap for future directions that are likely to advance the field. Seminar 3 (Biannual).
Animal Cognition
This course draws on latest findings in comparative cognition sciences, broadly construed, to examine fundamental questions about the evolution of mind on Earth and what that can tell us about the nature of mind wherever it is found—whether in human beings, nonhuman animals, or machines. Lec/Lab 3 (Biannual).
Research Methods
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).
Usability Testing
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).
Interactive Courseware
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).
Data-Driven Human Biomechanics
Topics include musculoskeletal 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. Students collect data in the lab and conduct the data analysis using MATLAB software or Python software. (Prerequisites: ISEE-330 or MECE-320 or BIME-200 or equivalent course or KGCOE graduate students.) 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 equivalent course or students in ISEE-MS, SUSTAIN-MS, ENGMGT-ME, or MIE-PHD programs.) 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, SUSTAIN-MS, ENGMGT-ME, or MIE-PHD programs or those with 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 class is restricted to graduate students in COS, KGCOE, GCCIS, CHST or CLA.) Lecture 3 (Spring).
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).
Cognitive Assessment
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 3 (Spring).
Social-Emotional Assessment
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 3 (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 3 (Spring).
Advanced Consultation
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 3 (Fall).
Academic Intervention
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 3 (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).
Statistical Software
This course is an introduction to the statistical-software package R, which is 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; graphics; matrices and arrays; simulations and app development with Shiny. (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 class is restricted to students in the APPSTAT-MS, SMPPI-ACT, or APPSTAT-U programs.) Lecture 3 (Fall, Spring, Summer).
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, or APPSTAT-U programs.) Lecture 3 (Spring, Summer).
Multivariate Analysis
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
Biodiversity, the diversity of life on earth from genes to ecosystems, is on the decline worldwide and considered one of the most pressing issues facing humanity. This interdisciplinary course explores the wide-ranging challenges and opportunities to understand biodiversity loss and address biodiversity conservation, with a focus on human wellbeing, cultural values, social science dimensions, and other humanistic discipline contributions. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lecture 3 (Biannual).

Admissions and Financial Aid

This program is available on-campus only.

Offered Admit Term(s) Application Deadline STEM Designated
Full‑time Fall or Spring Fall - February 15 priority deadline, rolling thereafter; Spring - rolling Yes
Part‑time Fall or Spring Rolling No

Full-time study is 9+ semester credit hours. Part-time study is 1‑8 semester credit hours. International students requiring a visa to study at the RIT Rochester campus must study full‑time.

Application Details

To be considered for admission to the Experimental Psychology MS program, candidates must fulfill the following requirements:

English Language Test Scores

International applicants whose native language is not English must submit one of the following official English language test scores. Some international applicants may be considered for an English test requirement waiver.

79 6.5 56

International students below the minimum requirement may be considered for conditional admission. Each program requires balanced sub-scores when determining an applicant’s need for additional English language courses.

How to Apply Start or Manage Your Application

Cost and Financial Aid

An RIT graduate degree is an investment with lifelong returns. Graduate tuition varies by degree, the number of credits taken per semester, and delivery method. View the general cost of attendance or estimate the cost of your graduate degree.

A combination of sources can help fund your graduate degree. Learn how to fund your degree

Additional Information


Applicants should have completed at least 15 semester hours of coursework 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.


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.

Latest News

  • January 9, 2023

    a baboon sitting.

    Teaching STEM by playing with primates

    Caroline DeLong, professor and undergraduate program director of psychology, and a team of researchers at RIT and Carnegie Mellon University are exploring the idea of engaging children with STEM skills through the lens of interacting with animals. They are working with a group of olive baboons at Rochester’s Seneca Park Zoo.

  • March 31, 2022

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    RIT’s Graduate Showcase celebrates scholarship April 7

    From robot waiters to river otters, RIT’s Graduate Showcase will cover a wide variety of topics representing graduate scholarship from the university’s Henrietta and global campuses. The symposium, held April 7, will feature oral presentations in the morning and poster presentations, demonstrations, and visual exhibitions in the afternoon.