The Sanford School (SSFD) offers specialized training for students interested in the study of Quantitative Methodology. The Quantitative Methodology specialization is open to any doctoral student in SSFD. Students choosing the Quantitative Methodology specialization will undertake in-depth study of statistical and measurement methodologies that offer great utility for research in human development, family studies, sociology, and education, among other areas. Faculty and students interested in quantitative methods study, evaluate, and develop statistical and measurement methods applicable to investigating issues in family and human development, sociology, and related fields. Students whose primary interest is in measurement, methods, and statistical modeling should complete the Quantitative Methodology specialization, along with additional coursework and research focused on quantitative methods. Students whose primary interest is in other substantive areas within Family and Human Development or Sociology but who would like to develop strength in measurement and statistical analysis should also consider the Quantitative Methodology specialization.
How to Apply:
The Quantitative Methodology specialization is open to any doctoral student in the Sanford School. Prospective students should apply either through the PhD in Family and Human Development program or the PhD in Sociology program. Please see the Graduate Handbook: Program in Family and Human Development or the Graduate Handbook: Program in Sociology for full descriptions of the application process. Existing students may apply for the Quantitative Methodology Specialization by completing this form.
Faculty affiliated with the Quantitative Methodology Specialization and their methodological interests are as follows:
Dawn DeLay – social network analysis, dyadic analysis, interdependent (nonindependent) data, and longitudinal social relationship models
Masumi Iida – multilevel modeling of longitudinal and dyadic data
Justin Jager – structural equation modeling, latent growth modeling, pattern-centered analysis (e.g., latent class analysis and growth-mixture modeling)
Roy Levy – psychometrics, item response theory, structural equation modeling, Bayesian networks, Bayesian inference, and assessment design
Holly O'Rourke - mediation analysis and statistical performance of mediation models, longitudinal mediation models, latent change score models, structural equation models for longitudinal data, statistical power
Connor Sheehan – Longitudinal methods, bio-statistics, demographic techniques, event history analysis.
Monica Tsethlikai - structural equation modeling, Bayesian statistics for small samples, item response theory
Marilyn Thompson – structural equation modeling, factor analysis, measurement invariance, multilevel modeling of longitudinal and clustered data
Natalie Eggum – longitudinal data analyses within a structural equation model framework