Kevin Grimm

Professor
Faculty
TEMPE Campus
Mailcode
1104

Biography

Kevin Grimm, Ph.D., is a professor in the Department of Psychology. He directs the Health and Developmental Research Methods Laboratory at Arizona State University. He received his B.A. in Mathematics and Psychology with a concentration in Education from Gettysburg College in 2000, and his M.A. and Ph.D. in Psychology from the University of Virginia (2001-2006). At the University of Virginia, Dr. Grimm studied structural equation modeling and longitudinal data analysis (e.g., growth curve analysis, longitudinal mixture modeling, longitudinal measurement, and dynamic models) with Jack McArdle and John Nesselroade. After completing his Ph.D., Dr. Grimm worked with Bob Pianta as a research associate in the Center for the Advanced Study of Teaching and Learning at the University of Virginia. In 2007, Dr. Grimm became an Assistant Professor in the Department of Psychology at the University of California, Davis and in 2011, he was promoted to Associate Professor at the University of California, Davis.

Dr. Grimm's research focuses on longitudinal methods for the study of change at the individual and group-level. His research in longitudinal methods has highlighted the use of the structural equation modeling framework to specify linear and nonlinear change models to study individual patterns of development (change), growth mixture models to evaluate whether there are unmeasured groups of individuals that follow distinct change patterns, and latent change models to evaluate lead-lag relationships in multivariate repeated measures. Grimm's current research focuses on data integration, the specification of growth models for binary and ordinal outcomes, longitudinal measurement invariance, and the development and application of data mining techniques for psychological science.

Education

  • Ph.D. Psychology, University of Virginia 2006
  • M.A. Psychology, University of Virginia 2003
  • B.A. Mathematics and Psychology, Gettysburg College 2000

Research Interests

I have three principal research interests: (1) multivariate methods for the analysis of change, (2) using multiple group and latent class models to understand divergent developmental processes, and (3) the development and application of data mining methods for psychological science.

Multivariate Change

My research in this area focuses on methods to analyze repeated measures data to evaluate long-term systematic trends and between-person differences therein. Such data are typical in the study of developmental changes, such as changes in mathematics, reading, behavior problems, and depression. These sorts of data often show systematic patterns of change; however the pattern and amount of change often vary over people making modeling of these types of data more complex. My research in this area has focused on model specification (Grimm, 2007; Grimm & Liu, 2016; Grimm & Marcoulides, 2016; Grimm, Ram, & Hamagami, 2011; Grimm & Widaman, 2010; Ram & Grimm, 2007), nonlinear forms of change (Grimm & Ram, 2009; Grimm, Ram, & Estabrook, 2010; Grimm, Ram, & Hamagami, 2011; Grimm, Zhang, Hamagami, & Mazzocco, 2013), and latent change score models (Grimm, 2012; Grimm, An, McArdle, Zonderman, & Resnick, 2012; Grimm, Castro-Schilo, & Davoudzadeh, 2013; Grimm, Zhang, Hamagami, & Mazzocco, 2013; McArdle & Grimm, 2010).

Modeling Divergent Developmental Processes

My research in this area focuses on models for examining heterogeneity in development. The growth models allows for a specific type of heterogeneity as the variability in latent intercepts and slopes is normally distributed. Growth mixture models, a combination of the finite mixture model and growth model, allow for heterogeneity to be examined in terms of latent classes with divergent developmental trajectories. My work in this area has focused on model specification (Grimm, McArdle, & Hamagami, 2007; Ram & Grimm, 2009), the incorporation of measurement models to aid in the determination of latent classes (Grimm & Ram, 2009), modeling nonlinear trajectories with multiple latent classes (Grimm, Ram, & Estabrook, 2010; Serang, Zhang, Helm, Steele, & Grimm, 2015), and model selection (Grimm, Mazza, & Davoudzadeh, 2017).

Data Mining Methods for Psychological Science

Data mining methods are not necessarily well suited for psychological science where our statistical models involve unmeasured (latent) variables, our theories involve indirect effects, and our data have dependency due to repeated measurement or clustering. My research in this area has focused on the combination of data mining methods with statistical models used in psychological science. This work can be seen in Jacobucci, Grimm, and McArdle (2016) where regularized regression was combined with structural equation models, Serang, Jacobucci, Brimhall, and Grimm where lasso regression was incorporated into mediation models, and Grimm, Mazza, and Davoudzadeh where k-fold cross-validation was used for model selection in mixture models. We are currently working on recursive partitioning approaches for nonlinear mixed-effects models, the development of more efficient recursive partitioning algorithms for use with latent variable models, missing data algorithms for data mining methods, and the development of new recursive partitioning algorithms for psychological data.

Select Publications

  1. Grimm, K. J., & Ram, N. (2009). Nonlinear growth models in Mplus and SAS. Structural Equation Modeling: A Multidisciplinary Journal, 16, 676-701.
  2. Grimm, K. J., & Widaman, K. F. (2010). Residual structures in latent growth curve analysis. Structural Equation Modeling: A Multidisciplinary Journal, 17, 424-442.
  3. Grimm, K. J., Ram, N., & Estabrook, R. (2010). Nonlinear structured growth mixture models in Mplus and OpenMx. Multivariate Behavioral Research, 45, 887-909.
  4. Grimm, K. J., Ram, N., & Hamagami, F. (2011). Nonlinear growth curves in developmental research. Child Development, 82, 1357-1371.
  5. 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.
  6. 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.
  7. Grimm, K. J., & Liu, Y. (2016). Residual structures in growth models with ordinal outcomes. Structural Equation Modeling: A Multidisciplinary Journal, 23, 466-475.
  8. Jacobucci, R., Grimm, K. J., & McArdle, J. J. (2016). Regularized structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 23, 555-566.
  9. 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.
  10. Grimm, K. J., Ram, N., & Estabrook, R. (2017). Growth modeling: Structural equation and multilevel modeling approaches. New York, NY: Guilford.

Research Activity

Courses

Fall 2018
Course NumberCourse Title
PSY 399Supervised Research
PSY 499Individualized Instruction
PSY 533Structural Equation Modeling
PSY 598Special Topics
Fall 2017
Course NumberCourse Title
PSY 399Supervised Research
PSY 499Individualized Instruction
PSY 530Intermed Statistics
Spring 2017
Course NumberCourse Title
PSY 537Longitudinal Growth Modeling
Fall 2016
Course NumberCourse Title
PSY 399Supervised Research
PSY 499Individualized Instruction
Spring 2016
Course NumberCourse Title
PSY 537Longitudinal Growth Modeling
Fall 2015
Course NumberCourse Title
PSY 399Supervised Research
PSY 499Individualized Instruction
PSY 591Seminar
Fall 2014
Course NumberCourse Title
PSY 399Supervised Research
PSY 499Individualized Instruction
PSY 537Longitudinal Growth Modeling

Service

I have been heavily involved in the dissemination and presentation of quantitative methods since receiving my Ph.D. in 2006. I have taught at the American Psychological Association’s Advanced Training Institutes on Structural Equation Modeling in Longitudinal Research and Exploratory Data Mining in the Behavioral Sciences since 2003 and 2009, respectively. I have directed these workshops since 2008 and 2015, respectively. For the past three years, I have taught a workshop, Exploratory Data Mining via SEACH Strategies, sponsored by James Morgan and taught at the University of Michigan. Finally, I have been an Associate Editor of Structural Equation Modeling: A Multidisciplinary Journal since 2012.