Educational Psychology

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College of Education and Human Development wordmark.
Department of Educational Psychology wordmark.

Simulation Lab for Longitudinal Data Research

Focus

  • Structural equation models
  • Finite mixture model
  • Longitudinal models, including latent growth curve models, mixed-effects models, and growth mixture models
  • Development and validation of new scales/instruments

"The aim of my 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."

Nidhi Kohli headshot

Nidhi Kohli

  • Lab director
  • Assistant professor, quantitative methods in education program, Department of Educational Psychology

Research group

Yadira Peralta-Torres headshot

Yadira Peralta-Torres Ph.D. graduate student, Fulbright Scholar

Yadira's research interests focus in correlated data, such as mixed-effects models, hierarchical models and structural equation model. Her current research is analyzing the underpinnings of these methodologies and their behavior under different conditions.

Recent publications

Kohli, N., Harring, J. R., & *Zopluoglu, C. (in press). A finite mixture of nonlinear random coefficient models for continuous repeated measures data. Psychometrika. doi: 10.1007/s11336-015-9462-0 (link to supplemental results)

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.

*Indicates co-author was an UMN student during part or all of the work