Dynamic Modeling Lab

Dynamic Modeling Lab

Publications Ellen Hamaker

Peer Reviewed Articles:

  1. Hamaker, E. L. & Ryan, O. (2019). A squared standard error is not a measure of individual differences. PNAS. doi: 10.1073/pnas.1818033116. Full text.
  2. Usami, S., Murayama, K. & Hamaker, E. L. (in press). A unified framework of longitudinal models to examine reciprocal relations. Psychological Methods.
  3. 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. doi: 10.1080/00273171.2018.1446819. Full text.
  4. N. K., & Hamaker, E. L. (2019). Measurement error and person-specific reliability in multilevel autoregressive modeling. Psychological Methods, 24, 70-91. doi: 10.1037/met000018. Full text.
  5. 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, 53, 293-314. doi: 10.1080/00273171.2018.1439722. Full text.
  6. 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. Full text and scripts.
  7. de Haan-Rietdijk, S., Kuppens, P., Bergeman, C. S., Sheeber, L. B., Allen, N. B., & Hamaker, E. L. (2017). On the use of mixed Markov models for intensive longitudinal data. Multivariate Behavioral Research, 52, 747-767. doi: 10.1080/00273171.2017.1370364. Full text.
  8. 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. Full text.
  9. 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. Full text.
  10. Hamaker, E. L., & Wichers, M. (2017). No time like the present: Discovering the hidden dynamics in intensive longitudinal data. Current Directions in Psychological Science, 26, 10–15. doi: 10.1177/0963721416666518. Full text.
  11. Asparouhov, T., Hamaker, E. L., & Muthén, B. (2017). Dynamic latent class analysis. Structural Equation Modeling: A Multidisciplinary Journal, 24, 257-269. doi: 10.1080/10705511.2016.1253479. Full text and scripts.
  12. 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. doi: 10.1080/00273171.2016.1251299. Full text.
  13. 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. doi: 10.1037/met0000085. Full text.
  14. de Haan-Rietdijk, S., Gottman, J. M., Bergeman, S., & Hamaker, E. L. (2016). Get over it! A multilevel threshold autoregressive model for state-dependent affect regulation. Psychometrika, 81, 217-241. doi: 10.1007/s11336-014-9417-x. Full text.
  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. Full text.
  16. 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. Full text.
  17. 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
  18. 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/met0000062. Full text.
  19. 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. doi: 10.1080/00273171.2015.1120182. Full text.
  20. 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. Full text.
  21. 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
  22. 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. doi: 10.1080/00273171.2014.1003772
  23. 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. doi: 10.1037/a0038889
  24. Hamaker, E. L., & Grasman, R. P. P. P. (2015). To center or not to center? Investigating inertia with a multilevel autoregressive model.Frontiers in Psychology, 5, 1492. doi:10.3389/fpsyg.2014.01492
  25. Harlow, L. L., Korendijk, E., Hamaker, E. L., Hox J. & Duerr S. R. (2013). A meta-view of multivariate statistical inference methods in European psychology journals. Multivariate Behavioral Research, 48, 749-774.
  26. Wang, L., Hamaker, E. L. & Bergman, C. (2012). Investigating inter-individual differences in short-term intra-individual variability. Psychological Methods, 17, 567-581.
  27. 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.
  28. 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.
  29. 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.
  30. Scheres, A., & Hamaker, E. L. (2010). What we can and cannot conclude about the relationship between steep temporal reward discounting and Hyperactivity-Impulsivity symptoms in Attention-Deficit/Hyperactivity Disorder. Biological Psychiatry, 68, e17-e18.
  31. 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.
  32. Chow, S.-M., Ho, R. M., Hamaker, E. L., & Dolan, C. V. (2010). Equivalence and differences between structural equation modeling and state-space modeling techniques. Structural Equation Modeling, 17, 303-332.
  33. Hamaker, E. L. (2009). Using information criteria to determine the number of regimes in threshold autoregressive models. Journal of Mathematical Psychology, 53, 518-529.
  34. Hamaker, E. L., Zhang, Z., & Van der Maas, H. L. J. (2009). Using threshold autoregressive models to study dyadic interactions. Psychometrika, 74, 727-745.
  35. Chow, S.-M, Hamaker, E. L., & 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.
  36. Chow, S.-M., Hamaker, E. L., 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, 683-716.
  37. Zhang, Z., Hamaker, E. L., & Nesselroade, J. R. (2008). Comparison of four methods for estimating a dynamic factor model. Structural Equation Modeling, 15, 377-402.
  38. Hamaker, E. L. (2007). How to inspect fruit (invited commentary). Measurement: Interdisciplinary Research and Perspectives, 5 (4), 250-253.
  39. Hamaker, E. L., Nesselroade, J. R., & Molenaar, P. C. M. (2007). The integrated trait-state model. Journal of Research in Personality, 41, 295-315.
  40. Hamaker, E. L., Dolan, C. V., & Molenaar, P. C. M. (2005). Statistical modeling of the individual: Rationale and application of multivariate stationary time series analysis. Multivariate Behavioral Research, 40 (2), 207-233.
  41. Hamaker, E. L. (2005). Conditions for the equivalence of the autoregressive latent trajectory model and a latent growth curve model with autoregressive disturbances. Sociological Methods and Research, 33 (3), 404-418.
  42. Hamaker, E. L., Dolan, C. V., & Molenaar, P. C. M. (2003). ARMA-based SEM when the number of time points T exceeds the number of cases N: Raw data maximum likelihood. Structural Equation Modeling, 10 (3), 352-379.
  43. Hamaker, E. L., Dolan, C. V., & Molenaar, P. C. M. (2002). On the nature of SEM estimates of ARMA parameters. Structural Equation Modeling, 9 (3), 347-368.
  44. Huntjens, R. J. C., Postma, A., Hamaker, E. L., Woertman, L., Van der Hart, O., & Peters, M. (2002). Perceptual and conceptual priming in patients with Dissociative Identity Disorder. Memory and cognition, 30 (7), 1033-1043.

 

Book Chapters (Peer Reviewed)

  1. Ryan, O., Kuiper, R. M., & Hamaker, E. L. (in press). A continuous time approach to intensive longitudinal data: The what, why and how. In: K. van Montfort, J. Oud, & M. Voelkle (Eds.). Continuous Time Modeling in the Behavioral and Related Sciences, 27-54. Springer, Cham, doi: 10.1007/978-3-319-77219-6_2
  2. 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.
  3. Hamaker, E. L., Van Hattum, P., Kuiper, R. M. & Hoijtink, H. (2011). Model selection based on information criteria in multilevel modeling. In K. Roberts & J. Hox (Eds). Handbook of Advanced Multilevel Analysis, 231-255. Taylor and Francis.
  4. Hamaker, E. L., & Klugkist, I. (2011). Bayesian estimation of multilevel models. In K. Roberts & J. Hox (Eds). Handbook of Advanced Multilevel Analysis, 137-161. Taylor and Francis.
  5. 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.
  6. Hamaker, E. L., & Dolan, C. V. (2009). Idiographic data analysis: Quantitative methods – from simple to advanced. In J. Valsiner, P. C. M. Molenaar, M. C. D. P. Lyra and N. Chaudhary (Eds). Dynamic Process Methodology in the Social and Developmental Sciences, 191-216. New York, NY: Springer-Verlag.
  7. Molenaar, P. C. M., Van Rijn, P. & Hamaker, E. L. (2007). A new class of SEM model equivalences and its implications. In S. M. Boker & M. J. Wenger (Eds). Data Analytic Techniques for Dynamical Systems, 189-211. Mahwah, NJ: Lawrence Erlbaum Associates.
  8. Dolan, C. V. & Hamaker, E. L. (2001). Investigating black-white differences in psychometric IQ: Multi-group confirmatory factor analyses of the WISC-R and K-ABC and a critique of the method of correlated vectors. In F. Columbus (Ed), Advances in psychological research, 6, 31-59. Huntington, NY: Nova Science Publishers.

 

Dutch Journals (Peer Reviewed)

  1. Prins, P. & Hamaker, E. L. (2000). De nog niet gelopen race tussen medicatie en gedragstherapie bij ADHD: Achter de schermen van de MTA-studie. Gedragstherapie, 33 (4), 267-282.
  2. Scheres, A., Hamaker, E. L., & Oosterlaan, J. (2000). Nieuw voer voor ongefundeerde oordelen over ADHD. Boekbespreking van “ADHD in kort bestek. Achtergronden, diagnostiek en hulpverlening”. Gedrag en Gezondheid, 28 (3), 75-76.