Measurement and Statistical Analysis (MASA)

The PhD in Family and Human Development offers a training specialization for students interested in the study of Measurement and Statistical Analysis (MASA). Students choosing the MASA specialization will undertake in-depth study of statistical and measurement methodologies that offer great utility for research in human development, family studies, and education, among other areas. Faculty and students in MASA study, evaluate, and develop statistical and measurement methods applicable to investigating issues in family and human development as well as the social sciences in general. Students whose primary interest is in measurement, methods, and statistical modeling should complete the MASA specialization, along with additional coursework and research focused on quantitative methods. Students whose primary interest is in other substantive areas within FHD (e.g., social relationships, developmental processes) but who would like to develop strength in measurement and statistical analysis should also consider the MASA specialization.

How to Apply:
Applications should be submitted for the PhD in Family and Human Development with a Specialty in Measurement and Statistical Analysis. (information about the application process can be found at the bottom of this page.) Please see the Graduate Handbook: Program in Family and Human Development for a full description of the application process.  

Faculty affiliated with the MASA program and their methodological interests are as follows:

Connor Sheehan – Longitudinal methods, bio-statistics, demographic techniques, event history analysis.

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

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

Natalie Wilkens – longitudinal data analyses within a structural equation model framework

Courses

The MASA specialization requires 18 hours total:

REQUIRED = 9 hours

*Requirements assume Quantitative Methods in the Social Sciences I, Lab I, Quantitative Methods in the Social Sciences II, and Lab II as prerequisites

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)

3.

  • Option 1: An approved course in Introduction to Measurement (classical test theory, reliability, validity, assessment design, assessment interpretation)
  • Option 2: An approved course in Multilevel Modeling (multilevel research designs, intraclass correlations, random intercept model, random slope model, longitudinal models, cross-level interactions, centering, three-level models)

ELECTIVES = 9 hours

*The below list is not exhaustive. The MASA specialization elective hours can overlap with courses taken toward the FHD statistics electives requirements. Note that Analysis of Variances does NOT count as an elective for the MASA specialization.

Advanced Modeling

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

Measurement

  • Item Response Theory

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 MASA faculty if required courses are not offered within a reasonable timeframe (e.g., due to faculty sabbaticals or other leaves of absence). For students admitted for Fall 2018 or later, the three required courses for the MASA Specialization must be completed, unless an appropriate substitution has been approved. For students admitted prior to Fall 2018, Advanced Regression Techniques can be substituted for the requirement currently listed as "3." For students admitted prior to Fall 2014, either Psychometrics or Structural Equation Modeling may fulfill the core requirement currently listed as "2." Completion of the MASA specialization requires that at least 12 hours of the requirements be taken within the Sanford School (this does not include the introduction sequence). All courses counting toward the MASA specialization must be completed with a minimum grade of B-.


For additional information, please contact:

Dr. Marilyn Thompson, Associate Director for Measurement and Statistical Analysis
Email: M.Thompson@asu.edu
(480)727-6924