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Peter Molenaar
Peter Molenaar
Distinguished Professor of Human Development and Family Studies
  • Human Development and Family Studies - HDFS
  • Research
  • Methodology
  • QuantDev Methodology
  • Graduate Program
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  • Ph.D., 1981, Social Sciences, University of Utrecht, The Netherlands
  • M.A., 1976, Mathematical Psychology, cum laude, University of Utrecht, The Netherlands
  • M.A., 1976, Psychophysiology, cum laude, University of Utrecht , The Netherlands
  • B.A., 1972, Psychology, cum laude, University of Utrecht, The Netherlands
  • B.A., 1976, Philosophical Logic, University of Utrecht, The Netherlands
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Currently Accepting Graduate Students
Office Address
415 BBH Building
The Pennsylvania State University
University Park, PA 16802
Professional Experience

1970-1976: Research Assistant, Department of Psychology, University of Utrecht

1976-1985: Assistant Professor of Psychology, Department of Psychology, University of Amsterdam

1985-1993: Associate Professor of Psychology, University of Amsterdam

1993-1996: Professor of Developmental Psychology, University of Amsterdam and The Pennsylvania State University

1996-1998: Professor of Developmental Psychology, University of Amsterdam, Head of Department of Cognitive Developmental Psychology

1999-2001: Professor of Mathematical and Developmental Psychology University of Amsterdam, Head of Methodology Department and Head of Department of Cognitive Developmental Psychology

2001- 2005: Professor of Psychological Methodology, Mathematical Psychology and Psychometrics, University of Amsterdam, Head Methodology Department

2005-Present: Professor of Human Development, Department of Human Development and Family Studies, The Pennsylvania State University

Grants and Research Projects

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

1. An important aim of Psychology is to describe, explain and guide processes occurring at the level of individual subjects. I have proven that the appropriate methodology required for realizing this aim has to be based on person-specific analyses of intra-individual variation, i.e., time series analysis. The new person-specific methodology is being applied to a variety of psychological processes, including mother-child interaction, personality development, and cognitive aging. Additional applications to individual psycho-therapeutic processes and person-specific brain imaging are in preparation.

2. An important feature of person-specific methodology is the possibility to apply state-of-the-art engineering techniques, in particular computational control theory, in order to optimally guide learning and developmental processes as well as disease processes. Applications of control theory to patient-specific optimal treatment of diabetes type I and asthma patients are in progress.

3. Additional applications of mathematical theories to solve substantive psychological issues in my work include a) the use of artificial neural networks to investigate nonlinear epigenetic processes, b) innovative structural equation modeling techniques to analyze longitudinal data, and c) the use of nonlinear dynamical models of developmental stage transitions.

  • Molenaar, P. C. M. (2003). State Space Techniques in Structural Equation Modeling: Transformation of latent variables in and out of latent variable models.State Space Techniques

  • Molenaar, P.C.M. (2010). Note on optimization of psychotherapeutic processes. Journal of Mathematical Psychology, 54, 208-213.

  • Molenaar, P.C.M., & Newell, K.M. (Eds.) (2010). Individual pathways of change: Statistical models for analyzing learning and development. Washington, DC: American Psychological Association.

  • Gates, K.M., & Molenaar, P.C.M. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage, 63, 310-319.

  • Molenaar, P.C.M., Smit, D.J.A., Boomsma, D.I., & Nesselroade, J.R. (2012). Estimation of subject-specific heritabilities from intra-individual variation: iFACE. Twin Research and Human Genetics, 15, 393-400.

  • Molenaar, P.C.M., Wang, Z., & Newell, K.M. (2013). Compressing movement information via principal component analysis (PCA): Contrasting outcomes from the time and frequency domains. Human Movement Science, 32, 1495-1511.

  • Molenaar, P.C.M., Lerner, R.M., & Newell, K.M. (Eds.) (2014). Handbook of developmental systems theory & methodology. New York: The Guilford Press.

  • Lerner, R.M., Overton, W.F., & Molenaar, P.C.M. (Eds.) (2015). Handbook of child psychology and developmental science. Volume 1: Theory and method. Hoboken, NJ: Wiley.

  • Liu, S., & Molenaar, P.C.M. (2014). iVAR: A Program for Imputing Missing Data in Multivariate Time Series using Vector Autoregressive Models. Behavior Research Methods, 46, 1138-1148.

  • Wang, Q., Molenaar, P.C.M., Harsh, S., Freeman, K., Xie, J., Zhou, J., Gold, C., & Rovine, M.J. (2014). Personalized state-space modeling of glucose dynamics for Type 1 diabetes using continuously monitored glucose, insulin dose and meal intake: An extended Kalman filter approach. Journal of Diabetes Technology and Science, 8, 331-345.

Additional Information

Single-subject time series analysis, optimal guidance of developmenal processes, optimal control of disease processes, structural equation modeling, dynamic factor analysis and P-technique.