College of Education and Human Development

Department of Educational Psychology

Nidhi Kohli

  • Royal and Virginia Anderson Professor of Quantitative Methods in Education; Program Coordinator

Nidhi Kohli

Areas of interest

  • Statistical models for longitudinal data (random effects models, latent growth curve models, and growth mixture models)
  • Monte Carlo simulation studies
  • Model fit evaluation
  • Development and validation of new scales/instruments

I am currently accepting doctoral students in the Quantitative Methods in Education (QME) program. Students with research interests that align with mine are encouraged to apply, including students whose research interests fit broadly under the umbrella of the topics above.


PhD, University of Maryland, 2011
MEd, University of Nevada, 2006


I joined the Department of Educational Psychology in 2012, after doing a postdoctoral research fellowship at Palo Alto Medical Foundation Research Institute working with massive Electronic Health Record (EHR) data. My research focuses on the development and improvement of statistical methods for analyzing educational, psychological, and more generally social and behavioral sciences data, particularly longitudinal (measures repeated on the same individuals over time) data. In the methodological framework, I predominantly work in the areas of latent growth curve modeling, mixed-effects modeling, and growth mixture modeling. The core agenda of my methodological research is to better understand nonlinear relationships among observed and latent variables using state-of-the-art latent variable methods, especially nonlinear mixed-effects models and its variants, and nonlinear structural equation models (e.g., piecewise growth models). The aim of this work is to move the educational statistics literature forward and provide researchers and practitioners the theoretical underpinnings and empirical guidance to utilize these methods to address important substantive questions in education, psychology, and human development.

Visit my research website, Longitudinal Methods Development Lab, for more information. 

Courses I teach

  • EPsy 8282 - Statistical Analysis of Longitudinal Data
  • EPsy 8266 - Statistical Analysis Using Structural Equation Methods

Selected publications. Complete list available at Experts@Minnesota and Google Scholar

*Indicates current and former students mentored or supervised by Dr. Kohli.

*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:

*Zhang, Z., *Rohloff, C. T., & Kohli, N. (2022). Model fit indices for random effects models: Translating model fit ideas from latent growth curve models. Structural Equation Modeling: A Multidisciplinary Journal, DOI:

*Rohloff, C. T., Kohli, N., & Chung, S. (2022). The impact of functional form complexity on model overfitting for nonlinear mixed-effects models. Multivariate Behavioral Research, DOI:

*Zhang, Z., *Rohloff, C. T., & Kohli, N. (2022). Commentary on “Obtaining interpretable parameters from reparameterized longitudinal models: Transformation matrices between growth factors in two parameter-spaces”. Journal of Educational and Behavioral Statistics, DOI:

*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.

Davison, M. L., Davenport, E. C., Kohli, N., Kang, Y., & Park, K. (2021). Addressing quantitative and qualitative hypotheses using regression models with equality restrictions and predictors measured in common units. Multivariate Behavioral Research, 56(1), 86–100.

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.