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Sy-Miin Chow
Professor of Human Development and Family Studies
Director, Quantitative Developmental Systems Methodology Core
  • Human Development and Family Studies - HDFS
  • Research
  • Methodology
  • QuantDev Methodology
  • Graduate Program
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  • Ph.D., Quantitative Psychology, University of Virginia
Currently Accepting Graduate Students
Office Address
236 Health and Human Development Building

Additional Websites
Professional Credentials


Professional Experience

2017-Present: Professor, Human and Development and Family Studies, Pennsylvania State University

2012-2017: Associate Professor, Human Development and Family Studies, Pennsylvania State University

2007-2012:  Assistant Professor, Department of Psychology, University of North Carolina

2005-2007:  Assistant Professor, Department of Psychology, University of Notre Dame

2005-2006:  Visiting Scientist, Max Planck Institute for Human Development, Berlin, Germany

2004-2005:  Assistant Research Professor, Department of Psychology, University of Notre Dame

Grants and Research Projects

I am a quantitative psychologist with expertise in (1) dynamic modeling, particularly methodologies for handling intensive longitudinal data typically encountered in the social and behavioral sciences (e.g., noisy multivariate data from multiple participants with relatively short time lengths; (2) technical and methodological issues that arise in studies of change and human dynamics; and (3) models and approaches for representing the dynamics of emotions, child development and family processes, as well as ways of promoting well-being and risk prevention. I was elected to be a fellow of the Alexander von Humboldt Foundation in Germany in 2005 and a member of the Society of Multivariate Experimental Psychology (SMEP) in 2012. I am a winner of the Cattell Award from SMEP as well as the Early Career Award from the Psychometric Society. I also served as Associated Editor (2013-2016) and Co-Editor of two special issues on Bayesian data analysis for Psychological Methods; and Associate Editor for Psychometrika (2011-2017). I work with colleagues, graduate and undergraduate students, and postdoctoral researchers on several NIH- and NSF-funded projects. Some of my key areas of research are described below.

Descriptions of Ongoing Research

  • Innovative Methods for Handling Data Analytic Issues in Intensive Longitudinal Data. Dynamic models are longitudinal models that are designed to describe more complex change processes. Due to these models’ explicit focus on process and dynamics, the associated data typically extend over substantially longer time spans (e.g., with greater than 35 measurement occasions) than those implicated in conventional panel models (typically, with less than 10 measurement occasions). One of my key research foci resides in developing methods for handling methodological issues encountered in the analysis of such data. Some of my representative work in this area involves fitting models with time-varying parameters (e.g., cyclic dynamics with time-varying amplitude; Chow, Hamaker, Fujita, & Boker, 2009; Chow, & Zhang, 2013; Chow, Zu, Shifren, & Zhang, 2011), developing continuous-time dynamic models for use with irregularly spaced data (obtained e.g., from experience sampling designs; Chow & Zhang, 2008), outlier detection in dynamic models (Chow, Hamaker, & Allaire, 2009), comparing the state-space modeling approach—one common modeling framework for formulating dynamic models—with other well-known modeling frameworks such as structural equation modeling (Chow, Ho, Hamaker, & Dolan, 2010), and developing methods for handling sudden shifts (regime switches) in human dynamics (Chow, Grimm, Guillaume, Dolan, & McArdle, 2013; Chow, & Fileau, 2010; Chow, & Zhang, 2013).
  • Methods for Fitting Nonlinear Dynamical Systems Models. With few exceptions, dynamic models in the behavioral sciences have focused almost exclusively on linear changes. As such, most of the existing software programs in social and behavioral sciences cannot readily handle models with nonlinear relationships among latent variables. Often, many nonlinear constraints have to be explicitly specified and some of the commonly utilized approaches can quickly become cumbersome. Such issues are especially salient in fitting differential equation models with nonlinear changes at the latent level. While differential equation models in general have special utility in accommodating irregular measurement intervals, the mathematical constraints involved are highly complex. To this end, my collaborators and I have extended existing techniques as well as developing new ones for fitting nonlinear dynamical systems models (Chow, Bendezú, Cole, & Ram, in press; Chow, Lu, Sherwood, & Zhu, 2016; Chow, Tang, Yuan, Song, and Zhu, 2011; Chow, Witkiewitz, Grasman, & Maisto, 2015).
  • Dynamic Models of Emotions, Lifespan Development and Family Dynamics. My methodological interests are motivated in part by empirical data analytic problems. There has been an emerging consensus that more sophisticated dynamic modeling tools are needed to better capture the complexities of different change processes. To this end, I have presented several novel applications of dynamic modeling techniques to represent affective processes (Chow, Ram, Boker, Fujita, & Clore, 2005; Chow, Nesselroade, Shifren, & McArdle, 2004; Yang, & Chow, 2010), interaction between parent-infant dyads (Chow, Haltigan, & Messinger, 2010; Messinger, Mahoor, Chow, & Cohn, 2009), family dynamics (Feinberg, Xia, Fosco, & Chow, under review; Schermerhorn, Chow, & Cummings, 2010) and lifespan development (Chow, Hamagami, & Nesselroade, 2008; Cole, Bendezú, Ram, & Chow, under review).

* Research in collaboration with graduate and undergraduate students.

  • Chow, S-M., & *Lee, J., *Park, J. J., Kuruppumullage P. D., Hammel T., Hallquist, M. N., Nord, E. A., Oravecz, Z., Perry, H. L., Lesser, L. M., & Pearl, D. K.(in press, 2024). Personalized Education through Individualized Pathways and Resources to Adaptive Control Theory-Inspired Scientific Education (iPRACTISE): Proof-of-Concept Studies for Designing and Evaluating Personalized Education. Journal of Statistics and Data Science Education.
  • *Ou, L. Hunter, M. D., Lu, Z., Stifter, C. A., & Chow, S-M. (in press, 2004). Estimation of nonlinear mixed-effects continuous-time models using the continuous-discrete Extended Kalman filter. British Journal of Mathematical and Statistical Psychology
  • *Park, J. J., Chow, S. M., Epskamp, S., & Molenaar, P. (in press, 2023). Subgrouping with chain graphical VAR models. Multivariate Behavioral Research, Preprint:10.31234/
  • *Park, J. J., Fisher, Z. F., Chow, S. M., & Molenaar, P. C. (2023). Evaluating discrete time methods for subgrouping continuous processes. Multivariate Behavioral Research, 1-13. DOI: 10.1080/00273171.2023.2235685
  • *Chen, M., Chow, S-M., Oravecz, Z., & Ferrer, E. (2023). Fitting Bayesian Stochastic Differential Equation Models with Mixed Effects through A Filtering Approach. Multivariate Behavioral Research. DOI: 10.1080/00273171.2023.2171354
  • Chow, S-M., Losardo, D., *Park, J., & Molenaar, P. C. M. (2023). Continuous-Time Dynamic Models: Connections to Structural Equation Models and Other Discrete-Time Models. In Hoyle, R. H, Structural Equation Modeling: Concepts, Issues, and Applications (2nd Ed.). New York: Guilford
  • Chow, S-M., Nahum-Shani, I, Baker, J. T, Spruijt-Metz, D, Allen, N. B, Auerbach, R. P.,Dunton, G. F., Friedman, N. P., Intille, S., Klasnja, P., Marlin, B., Nock, M. K., Rauch, S. L., Pavel, M., Vrieze, S., Wetter, D.W., Kleiman, E.M., Brick, T.R., Perry, H., Wolff-Hughes, D.L. & Intensive Longitudinal Health Behavior Network (ILHBN). (2023). The Intensive Longitudinal Health Behavior Network (ILHBN): Challenges, opportunities, and solutions from harmonizing data under heterogeneous study designs, target populations, and measurement protocols. Translational Behavioral Medicine, 13, 7-16. DOI:
  • Chow, S-M. (2019). Practical Tools and Guidelines for Exploring and Fitting Linear and Nonlinear Dynamical Systems Models. Multivariate Behavioral Research.
  • *Ou, L., Hunter, M. D., & Chow, S-M. (2019). What’s for dynr: A package for linear and nonlinear dynamic modeling in R. The R Journal; DOI: 10.32614/RJ-2019-012.
  • *Ji, L., Chow, S-M., Schermerhorn, A., Jacobson*, N. C, & Cummings, E. M. (2018). Handling Missing Data in the Modeling of Intensive Longitudinal Data. Structural Equation Modeling, 25(5), 715-736. doi: 10.1080/10705511.2017.1417046
  • *Li, Y., *Ji, L., Oravecz, Z., Brick, Timothy R., Hunter, M. D., & Chow, S-M. (2019). dynr.mi: An R Program for Multiple Imputation in Dynamic Modeling. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 13(5), 302-311., eISSN:1307-6892. World Academy of Science, Engineering and Technology, International Science Index 149, 2019.
  • Chow, S.-M., *Ou. L, Ciptadi, A., Prince, E., You, D., Hunter, M. D., Rehg, J. M., Rozga, A., & Messinger, D. S. (2018). Differential equation modeling approaches to representing sudden shifts in intensive dyadic interaction data. Psychometrika.
  • Maisto, S. A., Xie, F. C., Witkiewitz, K., Ewart, C. K., Connors, G. J., Zhu, H., Elder, G., Ditmar, M., & Chow, S-M. (2017). How chronic self-regulatory stress, poor anger regulation, and momentary affect undermine treatment for alcohol use disorder: Integrating social action theory and the dynamic model of relapse. Journal of Social and Clinical Psychology, 36, 238-263.
  • Feinberg, M. E., *Xia, M., Fosco, G. M., & Chow, S-M. (2017). Dynamical systems modeling of couple interaction: A new method for assessing intervention impact across the transition to parenthood. Prevention Science, 18(8), 887–898.
  • Lu, Z-H., Chow, S-M., & Loken, E. (2017). A Comparison of Bayesian and Frequentist Model Selection Methods for Factor Analysis Models. Psychological Methods, 22, 361-381.
  • Cole, P. M., *Bendezú, J. J., Ram, N., & Chow, S-M. (2017). Dynamical Systems Modeling of Early Childhood Self-Regulation. Emotion, 17(4), 684-699
  • *Ou, L., Chow, S-M., Ji, L., & Molenaar, P. C. M. (2017). (Re)evaluating the Implications of the autoregressive latent trajectory model through likelihood ratio tests of its initial conditions.Multivariate Behavioral Research, 52(2), 178-199.
  • Lu, Z-H., Chow, S-M., & Loken, E. (2016). Bayesian factor analysis as a variable selection problem. Multivariate Behavioral Research, 51(4), 519-539.
  • *Helm, J. L., Ram, N., Cole, P., Chow, S. M. (2016). Modeling self-regulation as a process using a multiple time scale multiphase latent basis growth model. Structural Equation Modeling, 23(5), 635-648. DOI:10.1080/10705511.2016.1178580
  • Chow, S-M., *Bendezú, J. J., Cole, P. M., & Ram, N. (2016). A Comparison of Two- Stage Approaches for Fitting Nonlinear Ordinary Differential Equation (ODE) Models with Mixed Effects. Multivariate Behavioral Research, 51, 154-184. Doi: 10.1080/00273171.2015.1123138.
  • Chow, S-M., Lu, Z., Sherwood, A. & Zhu, H. (2016). Fitting linear and nonlinear differential equation models with random effects and unknown initial conditions using the stochastic approximation expectation-maximization (SAEM) algorithm. Psychometrika, 81(1), 102-134. Doi:10.1007/s11336-014-9431-z. PubMed #: 25416456
  • Lu, Z-H., Chow, S-M., Sherwood, A, & Zhu, H. (2015). Bayesian analysis of ambulatory cardiovascular dynamics with application to irregularly spaced sparse data. Annals of Applied Statistics, 9(3), 1601-1620. Doi: 10.1214/15-AOAS846.
  • Chow, S-M., Witkiewitz, K. Grasman, R., & Maisto, S. (2015). The Cusp Catastrophe Model as Cross-Sectional and Longitudinal Mixture Structural Equation Models. Psychological Methods, 20, 142-164. PubMed # 25822209 NIHMSID 667553
  • *Hutton, R. S., & Chow, S-M. (2014). Longitudinal Multi-Trait-State-Method model using ordinal data. Multivariate Behavioral Research, 49, 269-282.
  • Zhang, G., Browne, W. M., Ong, A. D., & Chow, S.-M. (2014). Analytic standard errors for exploratory process factor analysis. Psychometrika,79(3), 444-469.
  • Chow, S-M., Grimm, K. J., *Guillaume, F., Dolan, C. V, & McArdle, J. J. (2013). Regime-switching bivariate dual change score model. Multivariate Behavioral Research, 48(4), 463-502.
  • Chow, S-M, & Zhang, G. (2013). Regime-switching nonlinear dynamic factor analysis models. Psychometrika, 78(4), 740-768.
Additional Information

Software repositories with collaborators and students: