This is a page to help you figure out which research methods courses will help meet your training needs.
The Applied Statistics, Social Science, and Humanities department at NYU Steinhardt, alongside other NYU Steinhardt departments, provides qualitative, quantitative statistics, and a variety of other research methods courses. We serve as a methods hub for students across the school, university, and city. We offer undergraduate, masters, and doctoral level courses that are open to students across New York University, and our courses tend to attract a large and diverse student body with a variety of learning goals, needs, and backgrounds.
Doctoral students outside NYU interested in our offerings may sign up for our graduate-level courses through the IUDC consortium.
Some of our research methods classes may require prerequisites, entrance processes, or permission codes. We outline our courses below and explain what you will learn, who should take them, and provide information about sequencing and level.
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Quantitative Methods Courses
For further information, contact Prof. Daphna Harel at daphna.harel@nyu.edu
If you are a program director or advisor and your program is looking to adopt any of these classes into your requirements, please contact us so that we can work on scheduling a section at a time that doesn’t conflict with your other program requirements.
Introductory Course Sequences
APSTA-GE 2001 Statistics for the Social and Behavioral Sciences (Fall) - This is a basic, start-from-zero, introductory statistics course. It is taught using the R programming language and teaches about descriptive statistics, confidence intervals, hypothesis tests, p-values, statistical significance, simple linear regression, and other introductory topics. Students will learn both the thinking behind these methods, as well as how to calculate the results from their own data using R.
APSTA-GE 2002 Applied Linear Modeling (Spring) – Following APSTA-GE 2001, this spring semester class extends the introductory topics to go more deeply intof linear modeling. Students will learn how to conduct (and interpret) multiple linear regression, logistic regression, and interaction terms and will be introduced to multilevel models. This class is also taught in R. Students who previously took an introductory statistics class and have some awareness of R may choose to start in this class rather than APSTA-GE 2001. Students choosing to take APSTA-GE 2002 without APSTA-GE 2001 should be comfortable with descriptive and inferential statistics—such as hypothesis tests, p-values, and confidence intervals—but do not need to have exposure to linear modeling.
APSTA-GE 2085 Basic Statistics (Fall/Spring) – Similar to APSTA-GE 2001, this class teaches students introductory statistics in a start-from-zero manner. Students will learn how to describe the methods they are using, how to calculate results, and how to interpret their results in light of their research questions. It covers the same topics as APSTA-GE 2001, but instead is taught using Stata as a programming language.
APSTA-GE 2086 Basic Statistics II (Spring) – Following APSTA-GE 2085, this spring semester class teaches the basics of linear modeling, including single and multiple linear regression, logistic regression, and an introduction to multilevel models. Students will learn how to describe the methods they are using, how to calculate results, and how to interpret their results in light of their research questions. This class is also taught in Stata. Students who previously took an introductory statistics class and have some awareness of Stata may choose to start in this class rather than APSTA-GE 2085. Students choosing to take APSTA-GE 2086 without APSTA-GE 2085 should be comfortable with descriptive and inferential statistics—such as hypothesis tests, p-values, and confidence intervals—but do not need to have exposure to linear modeling.
Intermediate Courses
APSTA-GE 2003 Intermediate Quantitative Methods (Fall) – This class provides an introduction to linear and logistic regression, emphasizing conceptual understanding and applications rather than mathematical theory. The course focuses on building understanding through programming statistical simulations (in R) and visualization rather than formal mathematical derivations.
APSTA-GE 2012 Causal Inference (Fall) – This class introduces a range of strategies for how to design and analyze studies to best support causal claims. Students taking this course should have previously completed APSTA-GE 2001/APSTA-GE 2002 or APSTA-GE 2085/APSTA-GE 2086 or their equivalents.
APSTA-GE 2017 Databases and Data Science Practicum (Spring) – This course introduces students to the tools, workflows, and collaborative practices used by professional data scientists. Students will gain hands-on experience working with real-world data. Covering essential tools such as the command line, Git, SQL, and Python, the course emphasizes the practical skills needed to navigate modern data environments and contribute effectively to data science teams.
APSTA-GE 2044 Generalized Linear Models (Spring, 2nd 7 weeks) – Using R, learn the extensions of regression modeling to non-continuous outcomes. Students will work with binary, categorical, ordinal, count, and bounded continuous data using common models such as logistic regression, Poisson regression, and more. We recommend students have taken APSTA-GE 2003 or equivalent before this course.
APSTA-GE 2134 Experimental and Quasi-Experimental Design (Fall) – This class teaches students about the common experimental designs they may choose to use when designing their own studies, or analyzing data collected from these designs.
APSTA-GE 2139 Survey Research Methods (Fall) – This class is non technical and teaches students how to design a research study that uses surveys from conception of the research question, to who to send the survey to, to the development of a questionnaire. Students will not collect data during this course, but will leave the course ready to do so in subsequent semesters. No software skills needed. Students will take this class preferably after an introductory course, but could take it concurrently with APSTA-GE 2001 or APSTA-GE 2085 if they feel confident.
Advanced Courses
The APSTA program offers masters and doctoral level classes for students interested in becoming advanced users of statistical methods.
APSTA-GE 2013 Missing Data (Spring, 2nd 7 weeks, offered every other year) – This class teaches students how to understand, think about, and deal with missing data in their datasets. Students should be comfortable with programming in R (basics) and have an understanding of regression modeling. Students will learn multiple techniques for handling missing data (as well as the pros and cons of each).
APSTA-GE 2042 Multilevel Models (Fall) – Using R, learn how to fit linear mixed effect/hierarchical/multilevel linear models to data where observations come in natural repeated measurement structures.
APSTA-GE 2123 Bayesian Inference (Spring, 2nd 7 weeks) – This class teaches the basics of Bayesian inference through programming in R. Students should have a basic understanding of calculus, although the class does not involve solving calculus problems. Students should also be comfortable with programming in R and with regression modeling and probability distributions.
APSTA-GE 2094 Modern Approaches in Measurement (Spring) – This class teaches the basics and applications of psychometric measurement and unsupervised machine learning for traditional and non-traditional applications. The course covers item response theory, structural equation modeling, latent class analysis, clustering, and more. Students will use the R programming language and apply learned concepts to real data sources.
Statistics and Society
APSTA-GE 2062 Ethics of Data Science (Spring, every other year) – This class makes you think about what is and isn’t ethical in statistics and data science. It does not have any programming component, so you do not have to be comfortable with statistical software.
APSTA-GE 2331 Data Science for Social Impact (Fall) – This class is a broad, survey-style class on a wide array of topics relevant to the link between data science and research and social change that cannot be solved by advanced coding/modeling techniques alone. Topics include research questions (and who chooses them); links between hoped for impact and methodological choices (e.g. qualitative, mixed methods, randomized controlled trials, quasi-experimental designs); measurement pitfalls and misattributions; big data and AI (for social good and social ill); data management and researcher degrees of freedom; data ethics; and co-interpretation and dissemination of key findings. Students should enter the class familiar with basic statistics, research, and/or data science.
APSTA-GE 2355 Data Science Translation (Spring, every other year) – This class teaches you how to communicate the results of your statistical analyses, both more broadly and with a focus on non-researcher audiences and partners (e.g., community members, practitioners, policymakers). Students should have experience with introductory statistics and the basics of linear modeling (such as APSTA-GE 2001/APSTA-GE 2002 or APSTA-GE 2085/APSTA-GE 2086) before taking this course.
Machine Learning and AI
APSTA-GE 2011 Supervised and Unsupervised Machine Learning (J-Term) – Students will use R or Python to learn classification and clustering techniques.
APSTA-GE 2047 Messy Data and Machine Learning (Fall) – This course provides an introduction to the concepts of supervised machine learning and the tools needed to work with the sorts of complex, messy datasets that are common in real applications. The course also covers the basics of large language models (LLMs): how they can be used programmatically, how they represent text numerically, and the transformer-based architecture that underpins modern LLMs. The course focuses on building intuition and on practical applications (mostly in R), with limited emphasis on mathematical theory.
APSTA-GE 2048 Generative AI for the Social Sciences (Spring) – Students will learn how to use new large language models and generative AI to accelerate research and answer novel social science research questions. Topics covered include the structure of large language models, building and validating annotations at scale, and running silicon simulations of social science phenomena.
Qualitative Methods Courses
For further information or program-specific advice, contact Prof. Dana Burde at dana.burde@nyu.edu
If you are a program director or advisor and your program is looking to adopt any of these classes into your requirements, please contact us so that we can work on scheduling a section at a time that doesn’t conflict with your other program requirements.
Below is a list of our courses (all graduate level), with a bit of description for each.
Introductory Course
RESCH-GE 2140 Approaches to Qualitative Inquiry (Fall + Spring) – The introductory graduate qualitative course, which serves as a “gateway” to a number of the others, is open to both master’s and doctoral students. The course investigates the epistemological, methodological, political, and ethical issues surrounding qualitative methods and introduces students to the range of methods in the qualitative paradigm, across the fields represented in Steinhardt, including historical archival research, textual analysis, ethnography or fieldwork, and semi-structured/open-ended interviewing. Students learn about and read examples of the methods studied, and they gain hands-on practice in methods such as interviewing and observation. The course is designed for students who plan to conduct their own research for theses or dissertations or those who would just like an introduction to qualitative work so that they can better understand and evaluate research they encounter in their fields.
Second-Level Courses
These courses require 2140 as a prerequisite (or an exception from the instructor).
RESCH-GE 2142 Interview and Observation (Spring, with an occasional Fall section) – The course, which focuses on interviewing, provides (primarily doctoral) students with the theoretical, conceptual, and practical skills to conduct and analyze semi-structured interviews. Students gain hands-on experience conducting and analyzing interviews, in preparation for integrating interview data into their master’s theses or dissertation research.
RESCH-GE 2147 Fieldwork: Data Collection (Fall) – This first part of a two-part sequence (though students are welcome to just take one semester) focuses on the collection of ethnographic data and qualitative interview data, with a focus on ethnographic observation and fieldnote writing (and an increasing focus on interview data collection). The course often serves as a first step towards dissertation data collection for doctoral students.
RESCH-GE 2148 Fieldwork: Data Analysis (Spring) – The second course in the sequence, this semester focuses on analysis of fieldnote data and interview transcripts, with attention to the range of coding approaches and tools. The course often serves as an early opportunity for students writing dissertations to analyze data they have begun to collect in the previous semester. Students can take one or both semesters of the Fieldwork sequence.
EDLED-GE 2209 Seminar in Advanced Qualitative Design (Fall) – This course builds on the skills developed in foundational qualitative research courses and addresses a range of conceptual, methodological, ethical, political, and logistical issues embedded in efforts to conduct "disciplined" and deeply theoretically-informed research. The foundational and advanced course objectives include that students will: build upon previous understanding of research traditions, designs, and analytic modes often associated with/deployed in qualitative research; discuss how theory and epistemological frameworks shape the processes and products of research; become conversant with debates in qualitative research on topics related to ethics, validity, rigor, trustworthiness, and subjectivity; collect and analyze qualitative data through various methods (e.g. interviewing and participant observation); craft student voice.
Non-Sequenced/Stand-Alone Courses
These courses do not require 2140 as a prerequisite.
RESCH-GE 2006/ELEC-GG 2876 Indigenous Research (Spring) – This course, developed and taught by Professor Eve Tuck and cross-listed with Gallatin, is appropriate for students who are working on a larger culminating research or inquiry project, creative project, or curriculum project. Prior coursework in research methods or research design is recommended. The course examines approaches to research by and for Indigenous communities and appraises the lived consequences of research for Indigenous peoples. Discussions and assignments attend to participatory ethics and practices, Indigenous data sovereignty, and Indigenous feminist interventions on research.
RESCH-GE 2143 Participatory Action Research (Fall + Spring) – Taught by instructors in the Administration, Leadership, and Technology department (and a requirement for many of its graduate students), this course introduces various approaches to and traditions of participatory action research (PAR), covering various action research traditions, including practitioner research, and issues of positionality, methodology, validity and ethics. The course also supports student preparation for PAR studies, e.g., for dissertations, and is also appropriate for students who are already employing PAR approaches for ongoing research.
RESCH-GE 3040 The Listening Guide (Fall) – Designed and co-taught by Professor Carol Gilligan, the Listening Guide sequence is offered in partnership with the Law School and serves both graduate students at Steinhardt and law school students. An interview-focused, workshop-centered course in a method of data collection and analysis developed by Professor Gilligan and grounded in psychological inquiry, the seminar is designed for students working with interviews or other narrative materials. Students work to develop research questions that fit the method and then gain hands-on experience with the collection and analysis of interview data.
RESCH-GE 3045 Advanced Listening Guide (Spring) – Building on the Fall course, and relying on it as a prerequisite, this advanced seminar in the method continues the focus on data collection and analysis of interview collection via the Listening Guide method of psychological inquiry. The course features hands-on work with interview data and a heavy emphasis on student work and peer workshopping.
RESCH-GE 3414 Discourse Analysis (every other Fall; next time, Fall 2027) – Cross-listed with and primarily housed in Teaching and Learning, this course was developed and is taught by Professor Shondel Nero and offers a survey of various discourse analytic approaches to research in education and related areas of inquiry. The course considers discourse analysis (DA) for various educational contexts through interdisciplinary lenses and various disciplinary traditions that impact education such as linguistics, psychology, sociology, media and communications, visual and performing arts. The course is primarily designed for doctoral students in education and related fields and explores important educational issues at the intersections of language, discourse, and power.
Mixed Methods Courses
APSY-GE 2835 Mixed Methods Course (Spring) – Students and established researchers who have been educated in either qualitative or quantitative methods rarely have the chance to learn how to meaningfully integrate the two. This course (open to both MA and doctoral students) is designed to introduce students to mixed methods research, including designs for data collection, analysis, and integration. The course assumes that students are already familiar with the traditional uses of both quantitative and qualitative methods, so it does not cover issues related to design, methods, measurement, analysis, validity, reliability, inferences within those methods. The course also assumes competence in other basic steps in the research process, such as reviewing literature, preparing a manuscript, etc.
Dissertation Proposal Training
RESCH-GE 3001 Dissertation Proposal Seminar (Fall + Spring) – While this course falls under the RESCH series, it is not a qualitative methods course. It was a schoolwide requirement for many years, and still is a requirement in many programs in Steinhardt. It serves students across the school, is taught by a range of regular instructors from across the school, and is designed for students working in many disciplines with a range of methodologies. The course assists with the development of dissertation proposals and emphasis is placed on the logic of proposal construction and is organized as a writing workshop, combining instructor framing, structured peer engagement, and protected writing time. The goal is for students to leave the course with a viable, committee-ready dissertation proposal draft.
Applied Statistics, Social Science, and Humanities
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