Core quantitative faculty includes:
Amanda J. Fairchild, Associate Professor, Member of Clinical-Community and Experimental PhD programs
Dr. Fairchild’s work centers on the development of quantitative methods for the study of physical and psychological health-related behaviors. Much of this work has centered on mediation analysis, or the investigation of third variables that elucidate the relation between predictors and dependent variables. Dr. Fairchild also has interest in the validity and reliability of measurements, as well as effect size measures.
Svetlana V. Shinkareva, Associate Professor, Member of the Experimental PhD program
Dr. Shinkareva’s research focuses on the development and application of quantitative methods to neuroimaging data. Her current interests include applying machine learning methods to fMRI data to study the neural basis of semantic knowledge representation.
Alberto Maydeu-Olivares, Professor, Member of the Experimental PhD program
Affiliated faculty include:
John Richards, Professor, Member of the Experimental PhD program
Dr. Richards has several areas of methodological / quantitative expertise that are closely integrated with his research on infant attention. He has developed an interesting model of infants looking behavior toward multimedia stimuli (e.g., children's TV viewing). This model borrows its theoretical background from biological models and uses quantitative examination of statistical distributions compared to infants viewing behaviors. A second area of work is the use of quantitative cortical source methods using high-density EEG recording to infer areas of the brain involved in psychological behavior.
Douglas H. Wedell, Professor, Member of the Experimental PhD program
Dr. Wedell’s quantitative interests revolve around measurement issues related to contextual bias and mathematical models of contextual processes. Within measurement he has investigated reliability and validity issues related to contextual effects on dominance and proximity based measures. His mathematical models are primarily focused on understanding biases in responding, especially related to judgment and choice.
Brian Habing (Statistics), Associate Professor, Adjunct Member of the Experimental PhD program
Dr. Habing’s psychometric research focuses on theoretical, computational, and applied issues in item response theory, scale construction, and multivariate statistics. His publications include a number of papers on multivariate and nonparametric item response theory, with his research in that area being supported by the National Science Foundation. He is currently co-investigator on an NSF grant studying the simultaneous modeling of dominance/monotone and unfolding/Thurstone items. In addition to his research, he co-created the courses STAT 778/EDRM 828 Item Response Theory and STAT 530 Exploring Multivariate Data courses at USC.