Ashley Danielle Brown
Psychometric Researcher
Research Department
Johnson O'Connor Research Foundation
Chicago, IL 60611

office: 161 East Erie Street, Ste. 304


BS in Physics and Mathematics, Marshall University, 1999-2004
MS in Physics, BS in Psychology, The University of Kentucky, 2004-2007, 2009-2011
PhD in Personality/Health Psychology Northwestern University, 2011-2017


Postdoctoral Scholar in Personality Psychology University of Southern California, 2017-2018 Psychometric Researcher Johnson O'Connor Research Foundation, 2018-present

Research interests

As a personality psychologist with an extensive background in physics and math, I'm interested in psychological Grand Unified Theories. To that end, my research focuses on psychometrics and computational models of personality. For my doctoral thesis, I designed and validated a simulation that blends elements of Jeffrey Gray’s Reinforcement Sensitivity Theory and Revelle and Condon’s Cues-Tendencies-Actions model.

Psychometrics and Scale Development

My interest in building integrative models (read, GUTs) of personality has guided my choice of research collaborators. During my undergraduate career, I worked with Tom Widiger to develop a self-report measure of obsessive-compulsive personality disorder based on the Five-Factor Model of personality traits. Later, I chose Bill Revelle as my PhD adviser not only because of his extensive experience in quantitative and computational approaches to personality, but also because he has long been a champion of the idea that personality is the "last refuge of the generalist in psychology." During my first year at Northwestern, I used data from Bill's SAPA Project website to investigate the relationships between personality traits, intelligence, and response styles on various tests of cognitive ability. My work at the Johnson O'Connor Research Foundation is similar; I get to play with the relationships among nearly a century's worth of scores on over twenty aptitude tests.

Psychometrics and Computational Modeling

Integrating disparate lines of research both within personality psychology (e.g. traits, abilities, interests) and between personality psychology and other subdisciplines (e.g. social, cognitive, clinical) demands rigorous attention to psychometric detail. My statistical computational modeling research with Bill Revelle uses simulated SAPA data (which are Massively Missing Completely at Random) in order to pit various analytic techniques against each other; our results indicate that SAPA's simple available-case correlations are often superior to those obtained using full-information maximum likelihood estimation.

Computational Modeling, Affect, and Personality

Personality influences individuals' risk for unfavorable life outcomes like disease and incarceration, but psychologists won't be able to do anything with that knowledge unless they're able to understand individual differences more precisely. Because computational modeling has such great potential to lend much-needed precision to the study of personality, I have developed a program in R, "CTA-RST," that simulates behavior and emotion in accordance with the predictions of Reinforcement Sensitivity Theory.

Selected publications and presentations