About

Steven M. Boker is a Professor of Quantitative Psychology at the University of Virginia, where he directs the Human Dynamics Laboratory and the LIFE Academy. He is an internationally recognized expert in Structural Equation Modeling (SEM) and modeling longitudinal data from a dynamical systems perspective. His novel developments include the Differential Structural Equation Modeling (dSEM) and Latent Differential Equations (LDE) methods for testing and comparing models of dynamical systems in mixed longitudinal and cross-sectional data, as well as the Windowed Cross-Correlation (WCC) method for determining nonstationary relative phase in multivariate time series. Dr. Boker developed the RAMpath path analysis method used in modern SEM programs and was one of the original developers of the OpenMx SEM software. His substantive research includes measuring and modeling the dynamics of dyadic conversation, age-related changes in resiliency through coupled dynamics between stress and affect, coupled dynamics between mothers and infants, and adaptive dynamic models for addiction. He has authored or co-authored over 150 journal articles, book chapters, and software packages, and has received awards including the 2024 Distinguished Researcher Award from UVA and the 2020 Saul Sells Award for lifetime achievement in multivariate psychology.

Research topics

• Computer Science
• Business
• Psychology
• Gerontology
• Human–computer interaction
• Medicine
• Cognitive psychology
• Process management

Selected publications

• Leveraging Daily Social Experiences to Motivate Healthy Aging

  The Journals of Gerontology Series B · 2021 · 16 citations

  • Computer Science
  • Psychology
  • Computer Science

  Models of healthy aging highlight the motivating influence of social connections. Social experiences constantly shape our thoughts and behaviors throughout daily life, and these daily processes slowly and consistently influence our health and well-being. In this article, we discuss research that has moved from cross-sectional laboratory designs emphasizing individual behaviors to more naturalistic within-person paradigms linking daily social experiences to emotional, cognitive, and physical well-being. We mention research gaps that need to be filled to advance our knowledge of the powerful forces of the social environment for motivating healthy aging. We also offer future directions to move this research forward. We conclude with an outlook on how to leverage these powerful forces in novel intervention approaches that are sensitive to the constantly changing nature of the person and the environment.

  PublisherOA PDFDOI

• AN ADAPTIVE EQUILIBRIUM REGULATION MODEL OF RESILIENCE

  Innovation in Aging · 2019-11-01

  articleOpen access1st authorCorresponding

  Abstract Adaptive equilibrium regulation (AER) models distinguish between the effects of acute versus chronic stressors as a system responds to changes in the environment. Acute stressors have a short time interval during which the stressor is present. Chronic stressors have an onset and may also have an offset, but the stress persists over a period of weeks, months or years. Resilience to an acute stressors may involve rapid self-regulation back to equilibrium without affecting the regulation process itself. Resilience to a chronic stressor may require the system to readapt itself so that regulation of the chronic stressor becomes more effective over time. We present a differential equation model that allows for adaptation of regulation in response to chronic stress and illustrate its use in intensive longitudinal burst data from the Notre Dame Study of Health and Wellbeing.

  PublisherOA PDFDOI

• QUANTIFYING SENSITIVE DEPENDENCE OF INITIAL CONDITION USING STRUCTURAL EQUATION MODELING

  Innovation in Aging · 2019-11-01

  articleOpen accessSenior author

  Abstract Human systems display sensitive dependence of initial condition. That is, even though two individuals may be similar in most regards, small differences between these individuals may have far reaching consequences later in life. In dynamical systems analysis, this sort of behavior is quantified with maximum Lyapunov exponents. These exponents quantify the degree to which small differences in initial condition between two systems affect trajectories of these systems later in time. Current methods for estimating maximum Lyapunov exponents are sensitive to noise and this sensitivity leads to estimation errors when researchers attempt to estimate these exponents on data obtained from human participants. Additionally, most current methods only allow for maximum Lyapunov exponent estimation using univariate time series. In this presentation, we present a method for using structural equation modeling for estimating latent maximum Lyapunov exponents from noisy multivariate time series and discuss applications of this method for analyzing human generated data.

  PublisherOA PDFDOI

• Coupled latent differential equation with moderators: Simulation and application.

  Psychological Methods · 2013-05-06 · 50 citations

  articleOpen access

  Latent Differential Equations (LDE) is an approach using differential equations to analyze time series data. Due to its recent development, some technique issues critical to performing an LDE model remain. This article provides solutions to some of these issues, and recommends a step-by-step procedure demonstrated on a set of empirical data, which models the interaction between ovarian hormone cycles and emotional eating. Results indicated that emotional eating is self-regulated. For instance, when people have more emotional eating behavior than normal, they will subsequently tend to decrease their emotional eating behavior. In addition, a sudden increase will produce a stronger tendency to decrease than a slow increase. We also found that emotional eating is coupled with the cycle of the ovarian hormone estradiol, and the peak of emotional eating occurs after the peak of estradiol. Self-reported average level of negative affect moderates the frequency of eating regulation and the coupling strength between eating and estradiol. Thus, people with a higher average level of negative affect tend to fluctuate faster in emotional eating, and their eating behavior is more strongly coupled with the hormone estradiol. Permutation tests on these empirical data supported the reliability of using LDE models to detect self-regulation and a coupling effect between two regulatory behaviors.

  PublisherOA PDFDOI

Frequent coauthors

• C. S. Bergeman

  University of Notre Dame

  2 shared

• Susan T. Charles

  University of California, Irvine

  1 shared

• Kelly L. Klump

  Michigan State University

  1 shared

• Michael C. Neale

  Virginia Commonwealth University

  1 shared

• Yueqin Hu

  Beijing Normal University

  1 shared

• Urte Scholz

  University of Zurich

  1 shared

• Robert Moulder

  University of Colorado Boulder

  1 shared

• Rana Sagha Zadeh

  Cornell University

  1 shared

Awards & honors

• 2024 Distinguished Researcher Award from UVA
• 2020 Saul Sells Award for lifetime achievement in multivaria…
• Fellow of the American Psychological Association
• Fellow of the Association for Psychological Science