Peter C.M. Molenaar, PhD

Distinguished Professor of Human Development & Psychology

The general theme of my work concerns the application of mathematical theories to solve substantive psychological issues. Some more specific elaborations of this theme are:

  1. Application of mathematical singularity theory (in particular catastrophe theory) to solve the longstanding debate about the reality of developmental stage transitions. This work (mathematical and stochastic modeling techniques, experimental designs and user-friendly software) has found wide acceptance and has been successfully applied in a series of ongoing research projects.
  2. Application of nonlinear multivariate statistical signal analysis techniques to solve the problem of mapping theoretical models of cognitive information-processing onto dynamically interacting EEG/MEG neural sources embedded in spatio-temporally coherent backgrounds. These techniques have been adapted and extended to connectivity mapping based on fMRI BOLD time series obtained with heterogeneous subjects.
  3. Application of mathematical-statistical ergodic theory to study the relationships between intra-individual (idiographic) analyses and inter-individual (nomothetic) analyses of psychological processes, triggering the development of innovative statistical multivariate time series techniques for the analysis of intra-individual processes (e.g., dynamic factor analysis) which now are applied in several research centers throughout the world. I have proven, based on the classical ergodic theorems, that for non-stationary processes such as learning and developmental processes it is necessary to focus on intra-individual variation (person-specific time series analysis). This proof necessitates a major re-orientation of psychometrics (e.g., test theory) and psychological methodology, which until now have been largely focused on analyses of inter-individual variation.
  4. Application of advanced multivariate analysis techniques in quantitative genetics and developmental psychology.
  5. Application of adaptive resonance theory (ART neural networks) to study the effects of nonlinear epigenetic processes, complemented by the use of mathematical biological models of self-organization.
  6. Application of engineering control techniques to optimally guide psychological and disease processes of individual subjects in real time. In particular I focus on real-time optimal treatment of individual patients with type 1 diabetes and asthma under normal living conditions.

Contributed Tutorials


Molenaar, P. C. M., & Lo, L. L.. (2016). Alternative Forms of Granger Causality, Heterogeneity, and Nonstationarity. In W. Wiedermann & von Eye, A. (Eds.), Statistics and Causality: Methods for Applied Empirical Research (pp. 203 - 229). Hoboken, NJ, USA: John Wiley & Sons, Inc. doi:10.1002/978111894707410.1002/9781118947074.ch9
Rovine, M. J., & Molenaar, P. C. M.. (2016). Person-Specific Approaches to the Modeling of Intraindividual Variation in Developmental Psychopathology. In D. Cicchetti (Ed.), Developmental psychopathology (Vol. Volume 1: Theory and method, pp. 1 - 20). Hoboken, NJ, USA: John Wiley & Sons, Inc. doi:10.1002/978111912555610.1002/9781119125556.devpsy119
Molenaar, P. C. M. (2016). Person-Oriented and Subject-Specific Methodology: Some Additional Remarks. Journal for Person-Oriented Research, 2(1-2), 16 - 19. doi:10.17505/jpor10.17505/jpor.2016.03
Campbell, C. G., Bierman, K. L., & Molenaar, P. C. M.. (2016). Individual Day-to-Day Process of Social Anxiety in Vulnerable College Students. Applied Developmental Science, 20(1), 1 - 15. presented at the Feb-01-2016. doi:10.1080/10888691.2015.1026594
Nesselroade, J. R., & Molenaar, P. C. M.. (2016). A Rejoinder. Multivariate Behavioral Research, 51, 428 - 431. presented at the Mar-05-2016. doi:10.1080/00273171.2015.1101368
Lo, L. L., Molenaar, P. C. M., & Rovine, M.. (2016). Determining the number of factors in P-technique factor analysis. Applied Developmental Science, 1 - 12. presented at the Jul-05-2017. doi:10.1080/10888691.2016.1173549
Liu, S., & Molenaar, P. C. M.. (2016). Testing for Granger Causality in the Frequency Domain: A Phase Resampling Method. Multivariate Behavioral Research, 51(1), 53 - 66. presented at the Feb-01-2016. doi:10.1080/00273171.2015.1100528
Nesselroade, J. R., & Molenaar, P. C. M.. (2016). Some Behaviorial Science Measurement Concerns and Proposals. Multivariate Behavioral Research, 51(2-3), 396 - 412. presented at the Mar-05-2016. doi:10.1080/00273171.2015.1050481
Molenaar, P. C. M., Beltz, A. M., Gates, K. M., & Wilson, S. J.. (2016). State space modeling of time-varying contemporaneous and lagged relations in connectivity maps. NeuroImage, 125, 791 - 802. presented at the Jan-01-2016. doi:10.1016/j.neuroimage.2015.10.088
Ou, L., Chow, S. - M., Ji, L., & Molenaar, P. C. M.. (2016). (Re)evaluating the Implications of the Autoregressive Latent Trajectory Model Through Likelihood Ratio Tests of Its Initial Conditions. Multivariate Behavioral Research, 1 - 22. presented at the Apr-12-2017. doi:10.1080/00273171.2016.1259980
Beltz, A. M., & Molenaar, P. C. M.. (2016). Dealing with Multiple Solutions in Structural Vector Autoregressive Models. Multivariate Behavioral Research, 51(2-3), 357 - 373. presented at the Mar-05-2016. doi:10.1080/00273171.2016.1151333