Grimm, K. J. & McArdle, J. J. (2005). A note on the computer generation of structural expectations. In F. Dansereau & F. Yammarino (Eds.) Multi-level issues in strategy and research methods (Volume 4 of Research in multi-level issues) (pp. 335-372). Amsterdam: JAI Press/Elseiver.
Grimm, K. J. (2007). Multivariate longitudinal methods for studying developmental relationships between depression and academic achievement. International Journal of Behavioral Development, 31, 328-339.
Ram, N., & Grimm, K. J. (2007). Using simple and complex growth models to articulate developmental change: Matching method to theory. International Journal of Behavioral Development, 31, 303-316..
Grimm, K. J. & Ram, N. (2009). Nonlinear growth models in Mplus and SAS. Structural Equation Modeling: An Interdisciplinary Journal,16, 676-701.
Grimm, K. J., Pianta, R. C., & Konold, T. (2009). Longitudinal multitrait-multimethod models for developmental research. Multivariate Behavioral Research, 44, 233-258.
Grimm, K. J., McArdle, J. J., & Widaman, K. F. (2010). Family-level variance in verbal ability change in the Intergenerational Studies. In K. Trzesniewski, M. B. Donnellan, & R. E. Lucas (Eds.), Secondary data analysis: An introduction for psychologists. Washington, DC: American Psychological Association.
Grimm, K. J., & Ram, N. (2009). A second-order growth mixture model for developmental research. Research in Human Development, 2-3, 121-143.
Ram, N., & Grimm, K. J. (2009). Growth mixture modeling: A method for identifying difference in longitudinal change among unobserved groups. International Journal of Behavioral Development, 33, 565-576.
Grimm, K. J., Steele, J. S., Mashburn, A. J., Burchinal, M., & Pianta, R. C. (2010). Early behavioral associations with achievement trajectories. Developmental Psychology, 46, 976-983.
Grimm, K. J., & Widaman, K. F. (2010). Residual structures in latent growth curve modeling. Structural Equation Modeling: An Interdisciplinary Journal, 17, 424-442.
Grimm, K. J., Ram, N., & Estabrook, R. (2010). Nonlinear structured growth mixture models in Mplus and OpenMx. Multivariate Behavioral Research, Multivariate Behavioral Research, 45, 887-909.
Grimm, K. J., Ram, N., & Hamagami, F. (2011). Nonlinear growth curves in developmental research. Child Development, 82, 1357-1371.
Grimm, K. J., & Ram, N. (2011). Growth curve modeling from an SEM perspective. In B. Laursen, T. Little, & N. Card (Eds.), Handbook of developmental research methods (pp. 411-431). New York: Guilford Publications.
Grimm, K. J. (2012). Intercept centering and time coding in latent difference score models. Structural Equation Modeling: An Interdisciplinary Journal, 19, 137-151.
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: An Interdisciplinary Journal, 19, 268-292.
Grimm, K. J., Zhang, Z., Hamagami, F., & Mazzocco, 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.
Grimm, K. J., Castro-Schilo, L., & Davoudzadeh, P. (2013). Modeling intraindividual change in nonlinear growth models with latent change scores. GeroPsych, 26, 153-162.
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.
Cameron, C. E., Grimm, K. J., Steele, J. S., Castro-Schilo, L., & Grissmer, D. W. (2015). Nonlinear Gompertz curve models of achievement gaps in mathematics and reading. Journal of Educational Psychology, 107, 789-804.
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.
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.
Grimm, K. J., & Liu, Y. (2016). Residual structures in growth models with ordinal outcomes. Structural Equation Modeling: A Multidisciplinary Journal, 23, 466-475.
Grimm, K. J., Mazza, G. L., & Mazzocco, M. M. M. (2016). Advances in methods for assessing longitudinal change. Educational Psychologist, 51, 342-353.
Grimm, K. J., Mazza, G. L., & Davoudzadeh, P. (2017). Model selection in finite mixture models: A k-fold cross-validation approach. Structural Equation Modeling: A Multidisciplinary Journal, 24, 246-256.
Stegmann, G., & Grimm, K. J. (2018). A new perspective on the effects of covariates in mixture models. Structural Equation Modeling: A Multidisciplinary Journal.
Fine, K., Suk, H-W., & Grimm, K. J. (2019). An examination of a functional mixed-effects modeling approach to the analysis of longitudinal data. Multivariate Behavioral Research.
Grimm, K. J., & Stegmann, G. (2019). Modeling change trajectories with count and zero-inflated outcomes: Challenges and recommendations. Addictive Behaviors, 94, 4-19.
Grimm, K. J., & Jacobucci, R. (2021). Reliable Trees: Reliability Informed Recursive Partitioning for Psychological Data. Multivariate Behavioral Research.
Grimm, K. J., Helm, J., Rodgers, D., & O'Rourke, H. (2021). Analyzing cross-lag effects: A comparison of different cross-lag modeling approaches. New Directions for Child and Adolescent Development, 11-33.