The M.S. in Quantitative Methods in the Social Sciences (QMSS) is a 30-credit program that equips students to understand and apply a range of quantitative methods while developing clear and concise written and oral communication skills in the presentation of research techniques and findings associated with the analysis of quantitative data.
The flexible and interdisciplinary nature of the program enables students to master analytic approaches and statistical techniques developed across a variety of social and behavioral science research areas. It also allows students the opportunity to work in areas that are of interest to them and to work through the application of these methods in more substantively centered concentration courses.
Students will learn how to decide which analytic approaches and methodologies are most suitable for specific research problems and will master their application. They will graduate with proficiency in the procedures for acquiring, managing, and analyzing data and reporting their findings in a direct and compelling manner.
Four optional areas of concentration serve students from and draw on professors from many different fields, including sociology, psychology, demography, education, political science, anthropology, history, economics, and statistics.
Students start the program by taking three core courses — two in statistics and one in research methods. They will then choose six elective courses, in consultation with their advisor, and will complete the program by taking a 3-credit capstone course in which they will develop an individual project
Students take a two-course sequence in statistics. They may choose to take the two courses from either the Educational Psychology program or from the Sociology program, depending on their particular interests. Students earn a total of 6 credits from the statistics sequence.
Educational Psychology Statistics Sequence
- Statistics and Computer Programming I: Introduction to the basic principles underlying data exploration, description, and analysis, statistical inference, and the use of computer packages for data analysis. Topics covered include (but are not limited to) measures of central tendency, measures of variability, probability and the normal curve, samples and populations, hypothesis testing, ANOVA, correlation, and an introduction to linear regression analysis.
- Statistics and Computer Programming II: In this course, we will move from the building blocks of quantitative data analysis covered in Statistics and Computer Programming I to the application statistical tools to test hypotheses and draw conclusions. Potential topics include (but are not limited to) multiple regression, categorical data analysis, non-linearity, mediation, moderation, repeated measures designs, and cross-classified data.
Sociology Statistics Sequence
- Sociological Statistics I: The broad focus of this course will be on the application of introductory statistics within the realm of sociological research. Topics covered include measures of central tendency, measures of variability, probability and the normal curve, samples and populations, hypothesis testing, ANOVA, correlation, and bivariate linear regression analysis.
- Sociological Statistics II: The broad focus of this course will be on applications of multivariate analysis in social science research, including multiple regression and logistic regression. Additional topics covered could include multivariate analysis of variance/covariance, factor analysis, categorical data analysis, among other relevant topics.
Research Methods (3 credits)
This course will cover issues pertaining to the research process and basic statistical skills and analytic strategies needed to collect and analyze quantitative data in different settings. In addition to methodological considerations, the course will address the social, political, and ethical dimensions of research design. Students will be introduced to a variety of topical areas, including survey development and administration, including measurement, instrumentation, sampling and distribution, and the process of securing institutional review board approval. The course is expected to culminate in the development of a research proposal that could lead to a larger capstone research project.
In addition to the required core courses, students in their last semester will enroll in a 3-credit capstone course and work through the process of developing an independent research project under the direction of a faculty mentor who will provide individual guidance.
Below is a listing of selected elective courses that have been offered in recent years or are expected to be offered on a relatively consistent basis moving forward.
- General Linear Models
- Statistical Computing
- Hierarchical Linear Models
- Categorical Data Analysis
- Geographic Information System: Basic and Advanced Techniques
- Path Analysis, Factor Analysis and Structural Equation Models
- Topics in Multivariate Methods
- Introduction to Psychometrics
- Item Response Theory
- Social Demography and Geographies of the Disadvantaged
- Methods of Demographic Analysis
- Advanced Methods of Demographic Analysis
- Data Mining Methods
- Spatial Demography
- Within National Inequalities: From Pareto to Piketty
- Global Economic Inequality: Measurement and Analysis
- Global Cities
- Theories of Neighborhood Change
- Cognitive Development and Learning Processes in Education
- Instructional Issues: Individual and Cultural Factors
- Evaluation Research
- Technology, Learning, and Development
- Advanced Seminar on Technology, Learning, and Development
- Advanced Seminar in Formative and Non-cognitive Assessment
- Introduction to Demography
- Social Policy and Socio-Economic Outcomes in Industrialized Countries: Lessons from the Luxembourg Income Study
Possible Areas of Concentration
Students may design their own area of concentration by drawing from elective courses offered by the Graduate Center and approved by the program director. The program is designed to allow for the following concentrations.
This concentration is available for students who want to focus on securing advanced training in quantitative analysis without opting to specialize in a particular substantive area. In addition to the three core courses, students will choose five courses from the program list, each of which covers advanced topics such as: assessment of treatment effects in evaluations research (propensity matching and related techniques); data mining methods (including CART and CHAID, and neural network predictive models); geographical information systems and related quantitative methods for spatial analysis; multi-level modeling; and time series modeling.
Students who select this concentration will complete a rigorous course sequence focused on analyzing socio-economic inequalities at multiple levels and across multiple disciplines. The concentration will approach the study of inequality not only through a global or cross-national lens, but in terms of how spatial scale and local institutional patterns shape and are shaped by higher level processes. It will stress the importance of understanding gender, racial, and ethnic inequality trends and dynamics, as well as how place and scale play a part in the causes and consequences of inequality, including in the political domain. Patterns and trends will be analyzed using high quality datasets and methods developed specifically for inequality research, as well as more generally applicable social science methods, including GIS and spatial statistics.
Digital technologies are one of the most disruptive forces in education. From the instant access of information made available by the internet to innovative ways to present and have students interact with material, such as digital games and simulations, to innovative ways of organizing instruction, such as flipped classrooms and massive open online courses (MOOCs), digital technologies have changed the way we approach both formal and informal education. These digital approaches to education have provided unprecedented amounts of data, creating a need for new analytic tools and methods.
Students enrolled in the Data Analytics for Learning concentration will develop the skills and knowledge required to analyze data produced by digital learning environments. The concentration will involve a rigorous series of courses on the theory, methods, and research related to learning with digital technologies. Emphasis will be placed on both the theory behind designing effective digital environments for learning and the empirical tools for evaluating educational outcomes and the learning process within these environments.
Demographic methods facilitate our understanding of the causes and consequences of changes in population-related phenomena such as family formation, fertility and reproductive health, disease, aging and mortality, urbanization, racial and ethnic composition, and mobility, and how such changes shape social, economic, and political processes and outcomes at the local, national, and international levels. Demographic methods are also used widely in both the for-profit and not-for-profit sectors as well as in government. The concentration in Demographic Methods enables students to develop a deep understanding of and conduct rigorous analyses of population structure and processes, as they apply in these different environments.