Quantitative

Area Director: Gitta Lubke

Overview

Doctoral candidates concentrating in quantitative psychology receive advanced training in statistical methods and quantitative models that are applicable to a wide variety of data reflecting human behavior as well as biological characteristics.

The quantitative area emphasizes a broad range of topics, including traditional analysis of variance and regression, multivariate analysis, categorical data analysis, structural equation modeling (SEM), item response theory, longitudinal analysis, Baysian analysis, finite mixture modeling, computational statistics, and statistical learning methods (i.e., data-mining).

Quantitative students typically focus on methods development and/or evaluation, but can also apply these methods to a topic in a substantive area of psychology, such as cognitive, clinical, developmental, of behavior genetics.  

The extent of the substantive training above and beyond the quantitative training will depend on the interests of the individual student.

The quantitative area faculty train students to have expertise in a variety of analytical tools and to advance methodology through novel research on statistical applications and creative use of existing techniques.  

Topics of expertise within the area include applied statistics, longitudinal analysis, Bayesian statistics, factor analysis and SEM, robust statistics, missing data, computational statistics, item response theory, mixture analysis, and statistical learning.  

As in all of our areas, there is great flexibility of curriculum, and students may work with a variety of faculty, both within and between programs.