The T. Denny Sanford School of Social and Family Dynamics (The Sanford School) offers specialized training for students interested in the study of Quantitative Methodology. The Quantitative Methodology Specialization is open to any doctoral student in The Sanford School. 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, education, and other areas. Faculty and students interested in quantitative methods study, evaluate, and develop statistical and measurement methods. 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. Doctoral 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 completing this 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 program in Family and Human Development or the PhD program in Sociology. 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 the form in the Appendices of their Graduate Handbook.

Specialization Requirements

Required Courses (6 hours)

*Requirements assume Introduction to Regression and Linear Models and its Lab, and Advanced Regression and Nonlinear Models and its Lab as prerequisite courses

  1. SSFD's Exploratory and Confirmatory Factor Analysis for the Social Sciences (introduction to matrix algebra, principal components analyses, exploratory factor analysis, confirmatory factor analysis, fundamentals of structural equation modeling)
  2. SSFD's Structural Equation Modeling for the Social Sciences (theory and application of structural equation modeling; path analysis, latent regression models, multiple group analysis, models for longitudinal data)

Elective Courses (12 hours)

*Students must complete 12 hours in elective courses. The below list is not exhaustive. The Quantitative Methodology Specialization elective hours can overlap with courses taken toward a degree program’s statistics electives requirements. Note that Introduction to Regression and Linear Models, Advanced Regression and Nonlinear Models, and Analysis of Variance courses do NOT count as electives for the Quantitative Methodology Specialization.

Advanced Modeling

  • Pattern-Centered Analysis
  • Bayesian Methods
  • Advanced Bayesian Methods
  • Experimental and Quasi-experimental Designs for Research


  • Item Response Theory
  • Psychometrics

Statistical Methods for Small Group and Longitudinal Designs

  • Analysis of Dyadic Interaction
  • Structural Equation Modeling with Longitudinal Data
  • Latent Growth and Mixture Models with Longitudinal Data

Statistical Methods for Large and Complex Samples

  • Social Network Analysis
  • Large/Secondary Dataset Analysis

*Appropriate substitutions will be considered by the Quantitative Methodology faculty if required courses are not offered within a reasonable timeframe (e.g., due to faculty sabbaticals or leaves of absence). Completion of the Quantitative Methodology Specialization requires that at least 12 hours of the requirements be taken within The Sanford School (not including the introduction sequence). All courses counting toward the Quantitative Methodology Specialization must be completed with a minimum grade of B-.


Faculty affiliated with the Quantitative Methodology Specialization and their methodological interests are as follows:

Natalie Eggum – latent variable modeling and longitudinal data analyses

Dawn DeLay – social network analysis, dyadic analysis, interdependent (nonindependent) data, and longitudinal social relationship models

Masumi Iida – multilevel modeling of intensive longitudinal methods, and dyadic data analysis

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.

Marilyn Thompson – structural equation modeling, factor analysis, measurement invariance, multilevel modeling of longitudinal and clustered data

Mengya Xia – Intensive longitudinal methods, structural equation modeling, mixture modeling (e.g., latent class analysis), multilevel modeling of longitudinal data, methods for modeling dynamic patterns (e.g., time-varying effect modeling)

For additional information, please contact:

Natalie Eggum, Coordinator for the Quantitative Methodology Specialization