Dynamical Systems Modeling: An Application to the Regulation of Intimacy and Disclosure in Marriage.
By Steven M. Boker, Jean-Philippe Laurenceau. 2006
122 citations recorded by the source corpus.
Subfields: Demography, Experimental and Cognitive Psychology, Social Psychology
View Paper: https://www.semanticscholar.org/paper/b2bd174750428424b401aeafc73f04de2077e913
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Demography
In Experimental and Cognitive Psychology
- B. Muthén
- T. Asparouhov
- R. Stawski
- Emily Butler
- E. Hamaker
- Annette Brose
- Florian Schmiedek
- Lesa Hoffman
In Social Psychology
- B. Muthén
- T. Asparouhov
- Emily Butler
- Tom Hollenstein
- Annette Brose
- Ashley K. Randall
- James J. Gross
- Daniel J. Bauer
Citations in the corpus
- Persons as Contexts: Evaluating Between-Person and Within-Person Effects in Longitudinal Analysis (2009, background): “Questions regarding the timing and cycling of interdependent phenomena about a point of equilibrium can be addressed by dynamic systems models (e.g., Boker & Laurenceau, 2006 ).”
- Emotional Coregulation in Close Relationships (2013, methodology): “The statistical models suggested by Boker, Butner, and colleagues could be adapted to address all these issues (Boker & Laurenceau, 2006, 2007; Boker & Nesselroade, 2002; Butner, Amazeen, & Mulvey, 2005; Butner et al., 2007).”
- State space grids: Analyzing dynamics across development (2007, methodology): “One approach to studying recurrent, stable patterns of behavior is to start with an a priori definition of an 3 Recently, however, Boker and Laurenceau (2006) have made differential equation modeling more accessible through commonly used SEM software. attractor state and measure the degree to which…”
- At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study (2018, background): “…effects for a particular interval as we do in discrete time models, continuous time models result in a drift matrix that represents the expected change (i.e., the first-order derivatives) as a function of the current values of the variables in the system (Boker & Laurenceau, 2006; Oud, 2007).”