Graduate Research Methods Courses

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, 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 2001, this spring semester class extends the introductory topics to the basics of linear modeling. Students will learn how to conduct regression analyses, including single and multiple linear regression, logistic regression, and an introduction 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 2001. Students choosing to take 2002 without 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 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 2085. Students choosing to take 2086 without 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 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.

APSTA-GE 2013 Missing Data (Spring, 2nd 7 weeks, offered every other year) – This class teaches students how to handle 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 missing data.

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 2017 Databases and Datascience Practicum (Spring) – This class teaches the modern tools used to manage data. Covering topics like SQL, Git, and R, students will learn how to store, access, and retrieve data for analysis. 

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 2355 Data Science Translation (Spring, every other year) – This class teaches you how to communicate the results of your statistical analyses. Students should have experience with introductory statistics and the basics of linear modeling (such as 2001/2002 or 2085/2086) before taking this course.

Advanced Courses

The APSTA program offers masters and doctoral level classes for students interested in becoming advanced users of statistical methods.

APSTA-GE 2012 Causal Inference (Fall) – This class teaches the most important aspects of how to make causal claims from your data. Students have the option of working in Stata or R for their work, and should have completed either 2001/2002 or 2085/2086 or their equivalents. 

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 to traditional and non-traditional applications. Students will use the R programming language to model latent traits and concepts from observed data.

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) – Students will learn how to process real-world data, which often does not come in an easy-to-analyze clean format using R. Students will then cover supervised machine learning techniques to discover trends and insights from the data.

APSTA-GE 2048 Generative AI in the Social Sciences (Spring) – Students will use R and Python to learn how to query large language models to solve social science research questions. Harness AI as a tool, students will be equipped with modern strategies for data analysis.  

Qualitative Methods Courses

For further information or program-specific advice, contact Prof. Lisa Stulberg at lisa.stulberg@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. 

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.