Longitudinal Methods Development (LMD) Lab
Members of the LMD lab pose with pillows printed with their first authored papers.
Focus
- Development and extension of statistical methodologies that effectively describe different underlying intrinsically nonlinear patterns of change
- Evaluation of the robustness (including accuracy and precision) of the methods developed or extended via Monte Carlo Simulation techniques
- Provide researchers and practitioners with the theoretical underpinnings and empirical guidance on how to effectively utilize these techniques to address important substantive questions in education and psychology.
Current projects
- Development of Bayesian Piecewise Crossed Random Effects Model
- Evaluation of Model Identification Issues for Crossed Random Effects Model
- Incorporating Covariates in Bayesian Piecewise Growth Mixture Models
- Global Model Fit Indices for Random Effects Models
Research group
Nidhi Kohli
Lab director
Royal and Virginia Anderson Professor of Quantitative Methods in Education, Department of Educational Psychology
Ziwei Zhang
Ziwei is interested in topics related to model development, estimation, and evaluation of intrinsically linear and nonlinear models for longitudinal data, specifically in the random effects modeling (REM) framework. In her first methodological project, she translated global model fit indices from the latent growth curve modeling framework to REM for intrinsically linear and nonlinear models. This helps researchers to evaluate the overall fit of a random effects model to data without employing model comparison approach. In her current project, she has developed a Bayesian random effects mediation model to examine mediation effects on longitudinal outcomes with intrinsically linear or nonlinear functions. Additionally, she has explored the impact of omitting confounders on the overall model performance of longitudinal mediation models. Ziwei is motivated to continue to provide flexible statistical modeling options, including model fit indices, within REM to a wider audience of methodologists and applied researchers.
Yue Zhao
Yue is a first-year PhD student in the QME program. She is interested in developing and applying longitudinal statistical models for educational and psychological data. She believes that each data set has its unique features, therefore her focus is developing and/or extending statistical models to address the uniqueness of each data set.
Past PhD students
Yadira Peralta-Torres
Current position: Assistant professor in the Department of Economics at the Center for Research and Teaching in Economics (CIDE), Mexico
Rik Lamm
Current position: Research, Evaluation, and Assessment Scientist in the Department of Research, Evaluation, and Assessment at the Bloomington Public Schools, MN
Corissa Rohloff
Current position: Research Scientist at the Human Resources Research Organization (HumRRO)
Recent publications
*Rohloff, C. T., Kohli, N., & Lock, E. F. (in press, 2024). Identifiability and estimability of Bayesian linear and nonlinear crossed random effects models. British Journal of Mathematical & Statistical Psychology. DOI: https://doi.org/10.1111/bmsp.12334
*Peralta, Y., Kohli, N., Kendeou, P., Davison, M. L., & Lock, E. F. (in press, 2023). Modeling the interrelation of reading and mathematics achievement trajectories: Is their development intertwined? Reading and Writing. https://doi.org/10.1007/s11145-023-10442-2
*Zhang, Z., *Rohloff, C. T., & Kohli, N. (2023). Model fit indices for random effects models: Translating model fit ideas from latent growth curve models. Structural Equation Modeling: A Multidisciplinary Journal, 30(5), 822–830.
*Rohloff, C. T., Kohli, N., & Chung, S. (2023). The impact of functional form complexity on model overfitting for nonlinear mixed-effects models. Multivariate Behavioral Research, 58(4), 723–742.
*Zhang, Z., *Rohloff, C. T., & Kohli, N. (2023). Commentary on “Obtaining interpretable parameters from reparameterized longitudinal models: Transformation matrices between growth factors in two parameter-spaces”. Journal of Educational and Behavioral Statistics, 48(2), 262–268.
*Peralta, Y., Kohli, N., Lock, E. F., and Davison, M. L. (2022). Bayesian modeling of associations in bivariate piecewise linear mixed-effects models. Psychological Methods,27(1), 44–64.
Kohli, N., & Sullivan, A. L. (2019). Linear-Linear piecewise growth mixture models with unknown random knots: A primer for School Psychology. Journal of School Psychology, 73, 89–100.
Kohli, N., *Peralta, Y., & *Bose, M. (2019). Piecewise random-effects modeling software programs. Structural Equation Modeling: A Multidisciplinary Journal, 26(1), 156–164.
*Peralta, Y., Kohli, N., & Wang, C. (2018). A primer on distributional assumptions and model linearity in repeated measures data analysis. Quantitative Methods for Psychology, 14(3), 199–217, DOI: doi:10.20982/tqmp.14.3.p199.
Lock, E. F., Kohli, N., & *Bose, M. (2018). Detecting multiple random changepoints in Bayesian piecewise growth mixture models. Psychometrika, 83(3), 733–750.
Kohli, N., *Peralta, Y., *Zopluoglu, C., & Davison, M. L. (2018). A note on estimating single-class piecewise mixed effect models with unknown change points. International Journal of Behavioral Development—Method & Measures Section, 42(5), 518–524.
Harwell, M. R., Kohli, N., & *Peralta, Y. (2018). A survey of reporting practices of computer simulation studies in statistical research. American Statistician, 72(4), 321–327.
Harwell, M. R., Kohli, N., & *Peralta, Y. (2017). Experimental design and data analysis in computer simulation studies in the behavioral sciences. Journal of Modern Applied Statistical Methods, 16(2), 3–28.
Sullivan, A. L., Kohli, N., Farnsworth, E. M., Jones, L., & Sadeh, S. (2017). Longitudinal models of reading achievement of students with and without learning disabilities. School Psychology Quarterly, 32(3), 336–349.
Kohli, N., Harring, J. R., & *Zopluoglu, C. (2016). A finite mixture of nonlinear random coefficient models for continuous repeated measures data. Psychometrika, 81(3), 851–880.
Wang, C., Kohli, N., & *Henn, L. (2016). A second-order longitudinal model for binary outcomes: Item response theory versus factor analytic framework. Structural Equation Modeling: A Multidisciplinary Journal, 23(3), 455–465.
Kohli, N., Hughes, J., Wang, C., *Zopluoglu, C., & Davison, M. L. (2015). Fitting a linear–linear piecewise growth mixture model with unknown knots: A comparison of two common approaches to inference. Psychological Methods, 20(2), 259–275.
Kohli, N., Koran, J., & *Henn, L. (2015). Relationships among classical test theory and item response theory frameworks via factor analytic models. Educational and Psychological Measurement, 75(3), 389–405.
Kohli, N., Sullivan, A. L., Sadeh, S. S., & *Zopluoglu, C. (2015). Longitudinal mathematics development of students with learning disabilities and students without disabilities: A comparison of linear, quadratic, and piecewise mixed effects models. Journal of School Psychology, 53(2), 105–120. [The first author received the following financial support for the research, authorship, and publication of this article: U of M Grant-in-Aid of Research, Artistry & Scholarship Program]
*Zopluoglu, C., Harring, J. R., & Kohli, N. (2014). FitPMM: An R routine to fit finite mixture of piecewise mixed–effect models with unknown random knots. Applied Psychological Measurement, 38(7), 583–584.
Kohli, N., Harring, J. R., & Hancock, G. R. (2013). Piecewise linear–linear latent growth mixture models with unknown knots. Educational and Psychological Measurement, 73(6), 935–955.
Kohli, N., & Harring, J. R. (2013). Modeling growth in latent variables using a piecewise function. Multivariate Behavioral Research, 48(3), 370–397.
*Indicates co-author was an UMN student during part or all of the work