Quantitative Methods and Computational Psychology Research

Research on robust inferences strives to understand the most reliable statistical features of any behavior. This work includes a complete revision of univariate and multivariate statistics from the direct identification of outliers and influential observations. This topical area is led by Rand Wilcox.

The basic principles of psychological measurement are used and applied to scale development and validation in several content areas. This includes advances in Item Response Theory (IRT) and in Common Factor Analysis (CFA) and methods to study measurement invariance. This topical area is led by Mark Lai.

Individual differences in psychological behaviors are a complex function of both genetic and non-genetic sources, and advances in statistical analysis have played a crucial role in new results. We examine the basic benefits and limitations of family data, including twins, and these issues are combined with the use of measured genotypes to better understand these sources of variation. This topical area is led by Laura Baker.

Individuals and groups make many important real-life decisions about health and work, marriage and family planning, and about engaging in risky behaviors. We study the elementary processes behind such decisions, including the development of group and individual utility functions. This topical area is led by Joe Arvai, Wandi De Bruine, and Richard John.

The accurate measurement of developmental changes from longitudinal data are a mainstay of developmental, personality, and motivational psychology. Recent advances in latent trajectory analysis, multi-level survival analysis, growth mixture modeling, and systems dynamics modeling, are all combined with contemporary psychometric measurement models. This topical area is led by Chris Beam and Meng Chen.

Social behaviors and interactions are inherently complex, but advances in computational models are providing new insights into social reasoning and decision-making. By employing advanced methods such as natural language processing and neural network models, we aim to unravel the cognitive and neurobiological foundations of human social behaviors. This topical area is led by Morteza Dehghani and Stephen Read.