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Kevin Grimm

Professor
Faculty, TEMPE Campus, Mailcode 1104
Biography: 

I am 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. (2003) and Ph.D. (2006) in Psychology at the University of Virginia. In graduate school, I 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 my Ph.D., I 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, I joined the faculty in the Department of Psychology at the University of California, Davis as an Assistant Professor, and was promoted to Associate Professor in 2011. In 2014, I moved to the Department of Psychology at Arizona State University, and was promoted to Full Professor in 2016.

My research interests include multivariate methods for the analysis of change, multiple group and latent class models for understanding divergent developmental processes, nonlinearity in development, machine learning techniques for psychological data, and cognitive/achievement development.

I am an author of Growth Modeling: Structural Equation and Multilevel Modeling Approaches with Nilam Ram and Ryne Estabrook, which was published by Guilford Press in 2017. The book provides extensive coverage of growth models, including linear and nonlinear trajectory models, growth mixture models, growth models with nonlinear link functions, and latent difference score models. We present and describe programming code for Mplus, OpenMx in R, NLMIXED in SAS, and nlme in R. The programming code can be found on the book's companion website.

I teach undergraduate and graduate quantitative courses at Arizona State University, including Longitudinal Growth Modeling, Machine Learning in Psychology, Structural Equation Modeling, and Intermediate Statistics. I also teach workshops sponsored by the American Psychological Association's Advanced Training Institute and Statistical Horizons.

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: 
Spring 2022
Course NumberCourse Title
PSY 499Individualized Instruction
PSY 591Seminar
PSY 599Thesis
PSY 792Research
PSY 799Dissertation
Fall 2021
Course NumberCourse Title
PSY 399Supervised Research
PSY 499Individualized Instruction
PSY 533Structural Equation Modeling
PSY 599Thesis
PSY 792Research
PSY 799Dissertation
Spring 2021
Course NumberCourse Title
PSY 499Individualized Instruction
PSY 592Research
Fall 2020
Course NumberCourse Title
PSY 399Supervised Research
PSY 499Individualized Instruction
PSY 533Structural Equation Modeling
PSY 592Research
PSY 792Research
Spring 2020
Course NumberCourse Title
PSY 499Individualized Instruction
PSY 533Structural Equation Modeling
Fall 2019
Course NumberCourse Title
PSY 399Supervised Research
PSY 499Individualized Instruction
PSY 792Research
Summer 2019
Course NumberCourse Title
PSY 799Dissertation
Spring 2019
Course NumberCourse Title
PSY 598Special Topics
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
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.