Abstract
State–space modeling techniques provide a convenient modeling platform for representing systematic trends as well as patterns of intraindividual variability around these trends. Their flexibility in accommodating multivariate processes renders them particularly suited to studying dyadic and family processes that show complex ebbs and flows over time. Using dyadic data collected during the Face-to-Face/Still-Face (FFSF) procedure, examples are provided to explicate the use of state–space models to capture two kinds of changes: systematic trends that are relatively smooth and slow-varying, and transient patterns of intraindividual variability that are manifested on a moment-to-moment basis.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adamson, L. B., & Frick, J. E. (2003). The still face: A history of a shared experimental paradigm. Infancy , 4, 451–473.
Allaire, J. C., & Marsiske, M. (2005). Intraindividual variability may not always indicate vulnerability in elders’ cognitive performance. Psychology and Aging, 20, 390–401.
Almeida, D. M., Piazza, J. R., & Stawski, R. S. (2009). Interindividual differences and intraindividual variability in the cortisol awakening response: An examination of age and gender. Psychology and Aging, 24(4), 819–827.
Beebe, B., Jaffe, J., Buck, K., Chen, H., Cohen, P., Blatt, S., Kaminer, T., Feldstein, S., & Andrews, H. (2007). Six–week postpartum maternal self-criticism and dependency and 4–month mother-infant self- and interactive contigencies. Developmental Psychology, 43(6):1360–1376.
Bolger, N., Davis, A., & Rafaeli, E. (2003). Diary methods: Capturing life as it is lived. Annual Review of Psychology, 54, 579–616.
Bollen, K. A., & Curran, P. J. (2004). Autoregressive latent trajectory (ALT) models: A synthesis of two traditions. Sociological Methods & Research, 32, 336–383.
Browne, M. W. (1993). Structured latent curve models. In C. M. Cuadras & C. R. Rao (Eds.), Multivariate analysis: Future directions 2 (pp. 171–198). Amsterdam: North–Holland.
Browne, M. W., & Nesselroade, J. R. (2005). Representing psychological processes with dynamic factor models: Some promising uses and extensions of autoregressive moving average time series models. In A. Maydeu-Olivares & J. J. McArdle (Eds.), Contemporary psychometrics: A Festschrift for Roderick P. McDonald (pp. 415–452). Mahwah: Erlbaum.
Bryk, A. S., & Raudenbush, S. W. (1987). Application of hierarchical linear models to assessing change. Psychological Bulletin, 101, 147–158.
Chow, S. -M., Haltigan, J. D., & Messinger, D. S. (2010). Dynamic patterns of infant-parent interactions during face-to-face and still-face episodes. Emotion, 10(1), 101–114.
Chow, S. -M., Ram, N., Boker, S. M., Fujita, F., & Clore, G. (2005). Emotion as thermostat: Representing emotion regulation using a damped oscillator model. Emotion, 5(2), 208–225.
Chow, S. -M., Ho, M. -H. R., Hamaker, E. J., & Dolan, C. V. (2010b). Equivalences and differences between structural equation and state–space modeling frameworks. Structural Equation Modeling , 17 , 303–332.
Collins, J. J., & De Luca, C. J. (1994). Random walking during quiet standing. Physical Review Letters, 73(5), 764–767.
Craigmile, P. F., Peruggia, M., & Van Zandt, T. (2009). Detrending 2 time series. In S. -M. Chow, E. Ferrer, & F. Hsieh. (Eds.). Statistical methods for modeling human dynamics: An interdisciplinary dialogue (pp. 213–240). Taylor & Francis, New York.
Davidian, M., & Giltinan, D. M. (1995). Nonlinear models for repeated measurement data. London: Chapman & Hall.
Doornik, J. A. (1998). Object –oriented matrix programming using Ox 2.0. Timberlake Consultants Press, London.
Durbin, J., & Koopman, J. S. (2001). Time series analysis by state space methods. New York: Oxford University Press.
Eid, M., & Diener, E. (1999). Intraindividual variabiliy in affect: Reliability, validity and personality correlates. Journal of Personality and Social Psychology , 76(4), 662–676.
Ekas, N., Haltigan, J., & Messinger, D. S. (2013). The dynamic still-face effect: Do infants decrease bidding over time when parents are not responsive? Developmental Psychology , 49(6), 1027–1035.
Frazier-Wood, A., Bralten, J., Arias-Vasquez, A., Luman, M., Ooterlaan, J., Sergeant, J., Rommelse, N. N. (2012). Neuropsychological intra-individual variability explains unique genetic variance of ADHD and shows suggestive linkage to chromosomes. American Journal of Medical Genetics B: Neuropsychiatric Genetic, 159B(2), 131–140.
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.
Harvey, A. C., & Souza, R. C. (1987). Assessing and modelling the cyclical behaviour of rainfall in northeast Brazil. Journal of Climate and Applied Meteorology, 26, 1317–1322.
Kishton, J. M., & Widaman, K. F. (1994). Unidimensional versus domain representative parceling of questionnaire items: An empirical example. Educational and Psychological Measurement, 54, 757–765.
Koopman, S. J., Shephard, N., & Doornik, J. A. (1999). Statistical algorithms for models in state space using ssfpack 2.2. Econometrics Journal, 2, 113–166.
Kopp, C. B. (1982). Antecedents of self-regulation: A developmental perspective. Developmental Psychology, 18, 199–214.
Kurdek, L. A. (2005). Gender and marital satisfaction early in marriage: A growth curve approach. Journal of Marriage and Family, 67(1), 68–84. doi:10.1111/j.0022-2445.2005.00006.x.
Larsen, R. J. (2000). Toward a science of mood regulation. Psychological Inquiry, 11, 129–141.
McArdle, J. J., & Nesselroade, J. R. (2003). Growth curve analysis in developmental research. In J. Schinka & W. F. Velicer (Eds.). Handbook of psychology: Volume 2, Research methods in psychology, pages (pp. 447–480). New York: Pergamon Press.
Meredith, W., & Tisak, J. (1990). Latent curve analysis. Psychometrika, 55, 107–122.
Molenaar, P. C. M. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific pyschology–this time forever. Measurement: Interdisciplinary Research and Perspectives, 2, 201–218. doi:10.1207/s15366359mea0204_1.
Nesselroade, J. R., & Ford, D. H. (1985). P–technique comes of age: Multivariate, replicated, single–subject designs for research on older adults. Research on Aging, 7, 46–80.
Schermerhorn, A. C., Chow, S. -M., & Cummings, M. E. (2010). Dynamics of family influence processes during interparental conflict. Child Development, 46(4), 869–885.
Sliwinski, M. J., Almeida, D. M., Smyth, J., & Stawski, R. S. (2009). Intraindividual change and variability in daily stress processes: Findings from two measurement-burst diary studies. Psychology and Aging, 24(4), 848–840.
Stoller, S., & Field, T. (1982). Alteration of mother and infant behavior and heart rate during a still-face perturbation of face-to-face interaction. In T. Field & A. Fogel (Eds.). Emotion and early interaction (pp. 57–82). Erlbaum, Hillsdale.
Tronick, E., Als, H., Adamson, L., Wise, S., & Brazelton, T. B. (1978). The infant’s response to entrapment between contradictory messages in face-to-face interaction. Journal of the American Academy of Child Psychiatry, 17, 1–13.
Verbeke, G., & Molenberghs, G. (2000). Linear mixed models for longitudinal data. Springer–Verlag, New York.
Walls, T. H., & Schafer, J. L. (2006). Models for intensive longitudinal data. Oxford: University Press.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Chow, SM., Mattson, W., Messinger, D. (2014). Representing Trends and Moment-to-Moment Variability in Dyadic and Family Processes Using State-Space Modeling Techniques. In: McHale, S., Amato, P., Booth, A. (eds) Emerging Methods in Family Research. National Symposium on Family Issues, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-01562-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-01562-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-01561-3
Online ISBN: 978-3-319-01562-0
eBook Packages: Behavioral ScienceBehavioral Science and Psychology (R0)