The Pennsylvania State University


Emotions and Dynamic Systems Lab (EDSLab)

The research projects in our lab fall into two broad categories: methodological work aimed at developing tools for fitting and evaluating linear and nonlinear dynamic models, and substantive projects focusing on modeling the dynamics of human emotions and cognitive processes. The overarching goal of these projects is to develop a broader repertoire of data-driven tools tailored toward analyzing the kinds of longitudinal data typically available in the social and behavioral sciences.

(I) Methods for Fitting Linear and Nonlinear Dynamic Models

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). Standard modeling frameworks such as structural equation modeling are not well suited for handling the myriad numerical problems that arise with intensive repeated measurements data. One of my key research foci resides in developing methods for handling such methodological issues. Some of my representative work in this area includes

  • Chow, S-M. , *Zu, J., Shifren, K. & Zhang, G. (2011). Dynamic factor analysis models with time-varying parameters. Multivariate Behavioral Research, 46(2), 303-339.
  • Chow, S-M. , Tang, N., *Yuan, Y., Song, X & Zhu, H. (2011). Bayesian estimation of semiparametric dynamic latent variable models using the Dirichlet process prior.  British Journal of Mathematical and Statistical Psychology, 64(1), 69-106.
  • Chow, S-M. , Ho, M. H. R., Hamaker, E. & Dolan, C. (2010). Equivalence and differences between structural equation modeling and state—space modeling techniques. Structural Equation Modeling, 17, 303-332.
  • Yang, M-S. & Chow, S-M. (2010). Using state-space models with regime switching to represent the dynamics of facial electromyography (EMG) data. Psychometrika, 74(4), 744-771
  • Chow, S-M. , Hamaker, E. & Allaire, J. C. (2009). Using innovative outliers to detect discrete shifts in dynamics in group-based state-space models. Multivariate Behavioral Research , 44, 465-496.
  • Chow, S-M. , Hamaker, E., Fujita, F., & Boker, S. M. (2009). Representing time-varying cyclic dynamics using multiple-subject state-space models. British Journal of Mathematical and Statistical Psychology, 62, 683-712.
  • Chow, S-M. , Ferrer, E. & Nesselroade J. R. (2007). An unscented Kalman filter approach to the estimation of nonlinear dynamical systems models. Multivariate Behavioral Research, 42 (2), 283-321.

Selected Software codes for fitting these models

II. Dynamic Models of Affect and Related Changes

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. For instance, we (Chow, Nesselroade, Shifren, & McArdle, 2004) found that the latent factors associated with positive and negative affects (as measured using the Positive and Negative Affect Schedule; PANAS) only became independent, as they were originally conceived by Watson, Clark and Tellegen (1988), after the time dependencies within and between PA and NA were accounted for. Taken together, new substantive findings have often emerged in the context of new methodological work substantive and these findings have suggested that our understanding of emotional processes may be distorted if supposedly dynamic processes are represented and analyzed as though they do not show changes over time.

  • Chow, S-M. , Haltigan, J. D. & Messinger, D. S. (2010). Dynamic patterns of infant-parent interactions during Face-to-Face and Still-Face episodes. Emotion, 10(1), 101-114.
  • Schermerhorn, A. C., Chow, S-M, & Cummings, E. M. (2010). Developmental family processes and interparental conflict: Patterns of micro-level influences. Developmental Psychology, 46(4), 869-885.
  • Chow, S-M. , Hamagami, F & Nesselroade, J. R (2007). Age differences in dynamical emotion-cognition linkages. Psychology and Aging, 22(4), 765-780.
  • Chow, S-M. , Ram, N., Boker, S. M., Fujita, F. Clore, G. (2005). Emotion as a thermostat: Representing emotion regulation using a damped oscillator model. Emotion. 5(2), 208-225.
  • Chow, S-M. , Nesselroade, J. R., Shifren, K. & McArdle J. J. (2004). Dynamic structure of emotions among individuals with Parkinson’s disease. Structural Equation Modeling , 11(4), 560-582.
  • Chow, S-M. & Nesselroade, J.  R. (2004) General slowing or decreased inhibition? Mathematical models of age differences in cognitive functioning. Journals of Gerontology: Psychological Science s, 59B(3), 101-109.

Selected Software codes for fitting these models