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Time-series analysis

from Part II - Methods in child development research

Published online by Cambridge University Press:  26 October 2017

Brian Hopkins
Affiliation:
Lancaster University
Elena Geangu
Affiliation:
Lancaster University
Sally Linkenauger
Affiliation:
Lancaster University
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Publisher: Cambridge University Press
Print publication year: 2017

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References

Further reading

Box, G., Jenkins, G.M., & Reinsel, G.C. (2013). Time series analysis: Forecasting and control (4th ed.). New York, NY: Wiley.Google Scholar
Chow, S.-M., Ferrer, E., & Hsieh, F. (Eds.) (2012). Statistical methods for modeling human dynamics: An interdisciplinary dialogue. London, UK: Taylor & Francis.Google Scholar
Shumway, R.H., & Stoffer, D.S. (2013). Time series analysis and its applications (3rd ed.). New York, NY: Springer.Google Scholar
Valsiner, J., Molenaar, P.C.M., Lyra, M.C.D.P., & Chaudhary, N. (Eds.) (2009). Dynamic process methodology in the social and developmental sciences. New York, NY: Springer.Google Scholar

References

Boker, S.M., & Laurenceau, J.P. (2005). Dynamical systems modeling: An application to the regulation of intimacy and disclosure in marriage. In Walls, T.A. & Schafer, J.L. (Eds.), Models for intensive longitudinal data (pp. 195218). Oxford, UK: Oxford University Press.Google Scholar
Boker, S.M., & Wenger, M.J. (Eds.) (2012). Data analytic techniques for dynamical systems. New York, NY: Psychology Press.Google Scholar
Brick, T.R., & Boker, S.M. (2011). Correlational methods for analysis of dance movements. Dance Research, 29 (supplement), 283304.Google Scholar
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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, 283321.CrossRefGoogle Scholar
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, 208.CrossRefGoogle ScholarPubMed
Chow, S.-M., & Zhang, G. (2008). Continuous-time modelling of irregularly spaced panel data using a cubic spline model. Statistica Neerlandica, 62, 131154.Google Scholar
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Molenaar, P.C.M. (2004). A manifesto on psychology as ideographic science: Bringing the person back into science psychology, this time forever. Measurement, 2, 201218.Google Scholar
Molenaar, P.C.M., Sinclair, K.O., Rovine, M.J., Ram, N., & Corneal, S.E. (2009). Analyzing developmental processes on an individual level using nonstationary time series modeling. Developmental Psychology, 45, 260271.CrossRefGoogle Scholar
Visser, I., Raijmakers, M.E., & Molenaar, P.C. (2002). Fitting hidden Markov models to psychological data. Scientific Programming, 10, 185199.Google Scholar
Voelkle, M.C., Oud, J.H.L., von Oertzen, T., & Lindenberger, U. (2012). Maximum likelihood dynamic factor modeling for arbitrary n and t using SEM. Structural Equation Modeling: A Multidisciplinary Journal, 19, 329350.CrossRefGoogle Scholar

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