Longitudinal & Behavioral Data Science Lab (Grimm)

Health and Developmental Methods Lab (Grimm)
Keywords
Keywords: Longitudinal data analysis, data mining, structural equation modeling, mixture modeling
Lab Area
Quantitative Psychology
Lab Director
Kevin J. Grimm, Ph.D
Actively Recruiting Undergraduate Researchers
Yes
Actively Recruiting Graduate Students
Yes

The Longitudinal & Behavioral Data Science Lab seeks to develop and evaluate methods and statistical models used to capture key characteristics of individual change processes, the determinants of longitudinal change process, and the determinants of between-person differences in the individual change process. We have focused on the development and application of machine learning methods to latent variable and longitudinal models common in the social and behavioral sciences. We have published papers on growth models, linear and nonlinear mixed-effects models, growth mixture models, latent class models, linear dynamic models, machine learning techniques, and exploratory approaches to studying individual change. The majority of our applications are focused on health, cognition, and achievement outcomes.

Lab Director and Principal Investigator

Kevin J. Grimm, Ph.D., Professor of Psychology

I'm a Professor in the Department of Psychology at Arizona State University. I received my B.A. in Mathematics and Psychology with a concentration in Education from Gettysburg College in 2000, and my M.A. and Ph.D. in Psychology at the University of Virginia (2001-2006). At the University of Virginia, I studied structural equation modeling and longitudinal data analysis with Dr. John McArdle and Dr. John Nesselroade. After completing my Ph.D., I worked with Dr. Robert Pianta as a research associate in the Center for the Advanced Study of Teaching and Learning at the University of Virginia. In 2007, I became an Assistant Professor in the Department of Psychology at the University of California, Davis, and was promoted to Associate Professor in 2011. In 2014, I joined the Department of Psychology at Arizona State University and was promoted to Full Professor in 2016. 

Graduate Students

Danielle Rodgers

Danielle is a third-year graduate student in the Quantitative Research Methods Ph.D. program at Arizona State University. She earned my Masters in Psychology (Quantitative Methods) at California State University, Fullerton, and her B.A. in Psychology at the University of California, Santa Barbara. Her research interests revolve around machine learning techniques and missing data. 
 
Russell Houpt

Russell is a second-year graduate student in the Quantitative Research Methods Ph.D. program at Arizona State University. He earned his B.A. in Psychology and Mathematics at Hope College, after which he worked for two years as a data manager for two health and psychology labs at Wayne State University. His research interests currently include machine learning techniques, latent class models, and longitudinal analysis. 

Alumni

Joel Steele, Ph.D. (University of California, Davis, 2011),
•    Assistant Professor, Department of Psychology, Portland State University
•    Associate Professor, Department of Psychology, Portland State University

 
Laura Castro-Schilo, Ph.D. (University of California, Davis, 2013)

•    Assistant Professor, Department of Psychology, University of North Carolina-Chapel Hill
•    Research Statistician, SAS

 
Jonathan Helm, Ph.D. (University of California, Davis, 2014)

•    Post-doctoral Researcher, University of California, Davis
•    Post-doctoral Researcher, The Pennsylvania State University
•    Assistant Professor, Department of Psychology, San Diego State University

 
Pega Davoudzadeh, Ph.D. (University of California, Davis, 2016)

•    Post-doctoral Researcher, University of California, Davis
•    People Data Researcher, Yahoo!
•    People Research Scientist, Facebook
 

Katerina Marcoulides, Ph.D. (Arizona State University, 2017)

•    Assistant Professor, Research and Evaluation Program, College of Education, University of Florida
•    Assistant Professor, Department of Psychology, University of Minnesota

Gina Mazza, Ph.D. (Arizona State University, 2018)
•    Research Associate, Division of Biomedical Statistics & Informatics, Mayo Clinic
•    Associate Consultant I, Division of Biomedical Statistics & Informatics, Mayo Clinic

Kimberly Fine, Ph.D. (Arizona State University, 2019)
•    Postdoctoral Fellow, Indiana University 
•    Principal Statistician, Medtronic

Gabriela Stegmann, Ph.D. (Arizona State University, 2019) 
•    Data Scientist, Aural Analytics

Heather Gunn, Ph.D. (Arizona State University, 2019)
•    Postdoctoral Fellow, University of California, Los Angeles
 

Collaborators

  • ·         Steven Boker, Ph.D., Professor of Psychology, University of Virginia
  • ·         Ian Campbell, Ph.D., Project Scientist, University of California, Davis
  • ·         Carol Connor, Ph.D., Chancellor’s Professor, School of Education, University of California, Irvine
  • ·         Mary Davis, Ph.D., Professor of Psychology, Arizona State University
  • ·         Leah Doane, Ph.D., Associate Professor of Psychology, Arizona State University
  • ·         Jason Downer, Ph.D., Research Associate Professor, Curry School of Education, University of Virginia
  • ·         Ryne Estabrook, Ph.D., Assistant Professor of Medical Social Sciences, Northwestern University
  • ·         Emilio Ferrer, Ph.D., Professor of Psychology, University of California, Davis
  • ·         Simona Ghetti, Ph.D., Professor of Psychology, University of California, Davis
  • ·         David Grissmer, Ph.D., Principal Scientist, Curry School of Education, University of Virginia
  • ·         Frank Infurna, Ph.D., Assistant Professor of Psychology, Arizona State University
  • ·         Ross Jacobucci, Ph.D., Assistant Professor of Psychology, University of Notre Dame
  • ·         Jungmeen Kim-Spoon, Ph.D., Professor of Psychology, Virginia Tech
  • ·         Kathy Lemery-Chalfant, Ph.D., Professor of Psychology, Arizona State University
  • ·         Michèle Mazzocco, Ph.D., Professor of Child Development, University of Minnesota
  • ·         John J. McArdle, Ph.D., Professor of Psychology and Gerontology, University of Southern California
  • ·         Marisol Perez, Ph.D., Associate Professor of Psychology, Arizona State University
  • ·         Robert Pianta, Ph.D., Dean of the Curry School of Education, University of Virginia
  • ·         Nilam Ram, Ph.D., Professor of Human Development and Family Studies, Pennsylvania State University
  • ·         Richard Van Dorn, Ph.D., Senior Mental Health Services Researcher, Research Triangle Institute
  • ·         Lijuan Wang, Ph.D., Associate Professor of Psychology, University of Notre Dame
  • ·         Keith Widaman, Ph.D., Associate Dean & Professor, Graduate School of Education, University of California, Riverside
  • ·         Zhiyong Johnny Zhang, Ph.D., Associate Professor of Psychology, University of Notre Dame

Select Publications

Books

  1. Grimm, K. J., Ram, N., & Estabrook, R. (2017). Growth modeling: Structural equation and multilevel modeling approaches. New York, NY: Guilford.
  2. Jacobucci, R., & Grimm, K. J. (under contract). Exploratory data mining for social and behavioral scientists. New York, NY: Guilford.

Articles

  1. Grimm, K. J. (2007). Multivariate longitudinal methods for studying developmental relationships between depression and academic achievement. International Journal of Behavioral Development, 31, 328-339.
  2. Grimm, K. J., Pianta, R. C., & Konold, T. R. (2009). Longitudinal multitrait-multimethod models for developmental research. Multivariate Behavioral Research, 44, 233-258.
  3. Grimm, K. J., & Ram, N. (2009). Nonlinear growth models in Mplus and SAS. Structural Equation Modeling: A Multidisciplinary Journal, 16, 676-701.
  4. Grimm, K. J., & Ram, N. (2009). A second-order growth mixture model for developmental research. Research in Human Development, 2-3, 121-143.
  5. Grimm, K. J., & Widaman, K. F. (2010). Residual structures in latent growth curve analysis. Structural Equation Modeling: A Multidisciplinary Journal, 17, 424-442.
  6. Grimm, K. J., Ram, N., & Estabrook, R. (2010). Nonlinear structured growth mixture models in Mplus and OpenMx. Multivariate Behavioral Research, 45, 887-909.
  7. Grimm, K. J., Ram, N., & Hamagami, F. (2011). Nonlinear growth curves in developmental research. Child Development, 82, 1357-1371.
  8. Grimm, K. J., An, Y., McArdle, J. J., Zonderman, A. B., & Resnick, S. M. (2012). Recent changes leading to subsequent changes: Extensions of multivariate latent difference score models. Structural Equation Modeling: A Multidisciplinary Journal, 19, 268-292.
  9. Grimm, K. J., *Steele, J. S., Ram, N., & Nesselroade, J. R. (2013). Exploratory latent growth models in the structural equation modeling framework. Structural Equation Modeling: A Multidisciplinary Journal, 20, 568-591.
  10. Grimm, K. J., Zhang, Z., Hamagami, F., & Mazzocco, M. M. (2013). Modeling nonlinear change via latent change and latent acceleration frameworks: Examining velocity and acceleration of growth trajectories. Multivariate Behavioral Research, 48, 117-143.
  11. Grimm, K. J., *Casto-Schilo, L., & *Davoudzadeh, P. (2013). Modeling intraindividual change in nonlinear growth models with latent change scores. GeroPsych, 26, 153-162.
  12. *Serang, S., Zhang, Z., *Helm, J., *Steele, J. S., & Grimm, K. J. (2015). Evaluation of a Bayesian approach to estimating nonlinear mixed-effects mixture models. Structural Equation Modeling: A Multidisciplinary Journal, 22, 202-215.
  13. *Davoudzadeh, P., *McTernan, M. L., & Grimm, K. J. (2015). Early school readiness predictors of grade retention from kindergarten through eighth grade: A multilevel discrete-time survival analysis approach. Early Childhood Research Quarterly, 32, 183-192.
  14. Grimm, K. J., & *Marcoulides, K. M. (2016). Individual change and the timing and onset of important life events: Methods, models, and assumptions. International Journal of Behavioral Development, 40, 87-96.
  15. Grimm, K. J., & *Liu, Y. (2016). Residual structures in growth models with ordinal outcomes. Structural Equation Modeling: A Multidisciplinary Journal, 23, 466-475.
  16. *Jacobucci, R., Grimm, K. J., & McArdle, J. J. (2016). Regularized structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 23, 555-566.
  17. Grimm, K. J., *Mazza, G. L., & Mazzocco, M. M. M. (2016). Advances in methods for assessing longitudinal change. Educational Psychologist, 51, 342-353.
  18. *Serang, S., Grimm, K. J., & McArdle, J. J. (2016). Estimation of time-unstructured nonlinear mixed-effects mixture models. Structural Equation Modeling: A Multidisciplinary Journal, 23, 856-869.
  19. Grimm, K. J., *Mazza, G., & *Davoudzadeh, P. (2017). Model selection in finite mixture models: A k-fold cross-validation approach. Structural Equation Modeling: A Multidisciplinary Journal, 24, 246-256.
  20. *Jacobucci, R., Grimm, K. J., & McArdle, J. J. (2017). A comparison of methods for uncovering sample heterogeneity: Structural equation model trees and finite mixture models. Structural Equation Modeling: A Multidisciplinary Journal, 24, 270-282.
  21. *Serang, S., *Jacobucci, R., *Brimhall, K. C., & Grimm, K. J. (in press). Exploratory mediation analysis via regularization. Structural Equation Modeling: A Multidisciplinary Journal.
  22. *Marcoulides, K. M., & Grimm, K. J. (in press). Data integration approaches to longitudinal growth modeling. Educational & Psychological Measurement.
  23. *Gonzalez, O., *Wurpts, I. C., *O’Rourke, H., & Grimm, K. J. (in press). Evaluating Monte Carlo simulations through data mining methods. Structural Equation Modeling: A Multidisciplinary Journal.
  24. *Stegmann, G., & Grimm, K. J. (in press). A new perspective on the effects of covariates in mixture models. Structural Equation Modeling: A Multidisciplinary Journal.
    1. Castro-Schilo, L., & Grimm, K. J. (in press). Using residualized change versus difference score for longitudinal research. Journal of Social and Personal Relationships.