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Dynamic Modeling Lab


  • Schuurman, N. K., & Hamaker, E. L. (in press). Measurement error and person-specific reliability in multilevel autoregressive modeling Psychological Methods.
  • Hamaker, E. L., Asparouhov, T., Brose, A., Schmiedek, F. & Muthén, B. (in press). At the frontiers of modeling intensive longitudinal data: Dynamic structural equation models for the affective measurements from the COGITO study. Multivariate Behavioral Research.
  • Van Emmerik, A. A. P., & Hamaker, E. L. (2017). Paint it black: Using change-point analysis to investigate the increasing vulnerability to depression towards the end of Vincent van Gogh’s life. Healthcare, 5, 35. doi:10.3390/healthcare5030053
  • de Haan-Rietdijk, S., Voelkle, M. C., Keijsers, L., & Hamaker, E. L. (2017). Discrete- vs. continuous-time modeling of unequally spaced experience sampling method data. Frontiers in Psychology, 8:1849. doi: 10.3389/fpsyg.2017.01849
  • Asparouhov, T., Hamaker, E. L., & Muthén, B. (2017). Dynamic latent class analysis. Structural Equation Modeling: A Multidisciplinary Journal, 24, 257-269.
  • Hamaker, E. L., Schuurman, N. K., & Zijlmans, E. A. O. (2017). Using a few snapshots to distinguish mountains from waves: Weak factorial invariance in the context of trait-state research. Multivariate Behavioral Research, 52, 47-60.
  • Hamaker, E. L., & Wichers, M. (2017). No time like the present: Discovering the hidden dynamics in intensive longitudinal data. (invited) Current Directions in Psychological Science, 26, 10-15.
  • de Haan-Rietdijk, S., Kuppens, P., & Hamaker, E. L. (2016). What’s in a day? A guide to decomposing the variance in intensive longitudinal data. Frontiers in Psychology, 7, 00891, doi: 10.3389/fpsyg.2016.00891
  • Schuurman, N. K., Grasman, R. P. P. P., & Hamaker, E. L. (2016). A comparison of Inverse-Wishart prior specifications for covariance matrices in multilevel autoregressive models. Multivariate Behavioral Research, doi: 10.1080/00273171.2015.1065398
  • Hamaker, E. L., Grasman, R. P. P. P., & Kamphuis, J. H. (2016). Modeling BAS dysregulation in Bipolar Disorder: Illustrating the potential of time series analysis (invited). Assessment (Special issue: Assessing Dynamic Psychological Processes), 23, 436-446. doi: 10.1177/1073191116632339
  • Schuurman, N. K., Ferrer, E., de Boer-Sonnenschein, M., & Hamaker, E. L. (2016). How to compare cross-lagged associations in a multilevel autoregressive model. Psychological Methods, 21, 206-221. doi: 10.1037/met000006
  • Schuurman, N. K., Houtveen, J. H., & Hamaker, E. L. (2015). Incorporating measurement error in n=1 psychological autoregressive modeling. Frontiers in Psychology, 6. doi: 10.3389/fpsyg.2015.01038
  • Hamaker, E. L., Ceulemans, E., Grasman, R. P. P. P., & Tuerlinckx, F. (2015). Modeling affect dynamics : State-of-the-art and future challenges. Emotion Review (Special issue: Affect Dynamics), 7, 316-322. doi: 10.1177/1754073915590619
  • Jongerling, J., Laurenceau, J.-P., & Hamaker, E. L. (2015). A multilevel AR(1) model: Allowing for inter-individual differences in trait-scores, inertia, and innovation variance. Multivariate Behavioral Research, 50, 334-349.
  • Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20, 102-116.
  • De Haan-Rietdijk, S., Gottman, J. M., Bergeman, S., & Hamaker, E. L. (2014). Get over it! A multilevel threshold autoregressive model for state-dependent affect regulation. Psychometrika, 81, 217-241. doi: 10.1007/s11336-014-9417-x
  • Hamaker, E. L., & Grasman, R. P. P. P. (2014). To center or not to center? Investigating inertia with a multilevel autoregressive model.Frontiers in Psychology5, 1492. doi:10.3389/fpsyg.2014.01492
  • Hamaker, E. L. & Grasman, R. P. P. P. (2012). Regime switching state-space model applied to psychological processes: Handling missing data and making inferences. Psychometrika, 77, 400-422.
  • Hamaker, E. L. (2012). Why researchers should think “within-person”: A paradigmatic rationale. Invited chapter for: M. R. Mehl & T. S. Conner (Eds.). Handbook of Research Methods for Studying Daily Life, 43-61, New York, NY: Guilford Publications.
  • Jongerling, J. & Hamaker E. L. (2011). On the trajectories of the predetermined ALT model: What are we really modeling? Structural Equation Modeling, 18, 370-382.


Related publications

  • Bringmann, L. F., Ferrer, E., Hamaker, E. L., Borsboom, D., & Tuerlinckx, F. (2018). Modeling non-stationary emotion dynamics in dyads using a time-varying vector-autoregressive model. Multivariate Behavioral Research (Epub ahead of print). doi: 10.1080/00273171.2018.1439722
  • Asparouhov, T., Hamaker, E. L., & Muthén, B. (2018). Dynamic structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 25, 359-388. doi: 10.1080/10705511.2017.1406803
  • Bringmann, L. F., Hamaker, E. L., Vigo, D. E., Aubert, A., Borsboom, D., & Tuerlinckx, F. (2017). Changing dynamics: Time-varying autoregressive models using generalized additive modeling. Psychological Methods, 22, 409-425. https://www.ncbi.nlm.nih.gov/pubmed/27668421
  • Bringmann, L. Ferrer, E., Hamaker, E. L., Borsboom, D., & Tuerlinckx, F. (2015). Modeling nonstationary emotion dynamics in dyads using a semiparametric time-varying vector autoregressive model. Multivariate Behavioral Research, 50, 730-731.
  • Wang, L., Hamaker, E. L. & Bergman, C. (2012). Investigating inter-individual differences in short-term intra-individual variability. Psychological Methods, 17, 567-581.
  • Madhyastha, T., Hamaker, E. L., & Gottman, J. (2011). Investigating spousal influence using moment-to-moment affect data from marital conflict. Journal of Family Psychology, 25, 292-300.
  • Houtveen, J. H., Hamaker, E. L., & Van Doornen, L. J. P. (2010). Using multilevel path analysis in analyzing 24-hour ambulatory physiological recordings applied to medically unexplained symptoms. Psychophysiology, 47,570-578.
  • Hamaker, E. L., Grasman, R. P. P. P., & Kamphuis, J. H. (2010). Regime-switching models to study psychological processes. In P. C. M. Molenaar & K. M. Newell (Eds.).Individual Pathways of Change: Statistical Models for Analyzing Learning and Development,155-168. Washington, DC: American Psychological Association.