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Steele, Joel S.; Ferrer, Emilio – Multivariate Behavioral Research, 2011
This article presents our response to Oud and Folmer's "Modeling Oscillation, Approximately or Exactly?" (2011), which criticizes aspects of our article, "Latent Differential Equation Modeling of Self-Regulatory and Coregulatory Affective Processes" (2011). In this response, we present a conceptual explanation of the derivative-based estimation…
Descriptors: Calculus, Responses, Simulation, Models
Ferrer, Emilio; Steele, Joel S.; Hsieh, Fushing – Multivariate Behavioral Research, 2012
There are many compelling accounts of the ways in which the emotions of 1 member of a romantic relationship should influence and be influenced by the partner. However, there are relatively few methodological tools available for representing the alleged complexity of dyad level emotional experiences. In this article, we present an algorithm for…
Descriptors: Interpersonal Relationship, Psychological Patterns, Emotional Experience, Mathematics
Song, Hairong; Ferrer, Emilio – Multivariate Behavioral Research, 2012
Dynamic factor models (DFMs) have typically been applied to multivariate time series data collected from a single unit of study, such as a single individual or dyad. The goal of DFMs application is to capture dynamics of multivariate systems. When multiple units are available, however, DFMs are not suited to capture variations in dynamics across…
Descriptors: Bayesian Statistics, Computation, Factor Analysis, Models
Steele, Joel S.; Ferrer, Emilio – Multivariate Behavioral Research, 2011
We examine emotion self-regulation and coregulation in romantic couples using daily self-reports of positive and negative affect. We fit these data using a damped linear oscillator model specified as a latent differential equation to investigate affect dynamics at the individual level and coupled influences for the 2 partners in each couple.…
Descriptors: Affective Behavior, Calculus, Models, Females
Chow, Sy-Miin; Ferrer, Emilio; Nesselroade, John R. – Multivariate Behavioral Research, 2007
In the past several decades, methodologies used to estimate nonlinear relationships among latent variables have been developed almost exclusively to fit cross-sectional models. We present a relatively new estimation approach, the unscented Kalman filter (UKF), and illustrate its potential as a tool for fitting nonlinear dynamic models in two ways:…
Descriptors: Item Response Theory, Models