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Researchers across varied fields increasingly are collecting and analyzing intensive longitudinal data (ILD) to examine processes across time at the individual level. Two types of relations are typically examined: lagged and contemporaneous. Lagged relations capture how variables at a prior time point can be used to explain variance in variables at a later time point. These are always modeled using autoand cross-regressions by means of vector autoregression (VAR). By contrast, there are two types of relations commonly used to model the contemporaneous relations, which model how variables relate instantaneously. Until now, researchers must opt to either model contemporaneous relations as undirected relations among residuals (e.g., partial or full correlations) or as directed relations among the variables (e.g., paths or regressions). The choice for how to model contemporaneous relations has implications for inferences as well as the potential to introduce bias in the VAR lagged relations if the wrong type of relation is used. This article introduces a novel data-driven method, hybrid-group iterative multiple model estimation (GIMME), that provides a solution to the problem of having to choose one or the other type of contemporaneous relation to model. The modeling framework utilized in hybrid-GIMME allows for both types of contemporaneous relations in addition to the standard VAR relations. Both simulated and empirical data were used to test the performance of hybrid-GIMME. Results suggest this is a robust method for recovering contemporaneous relations in an exploratory manner, particularly with an ample number of time points per person. [ABSTRACT FROM AUTHOR]

The present study examined genetic, prenatal, and postnatal environmental pathways in the intergenerational transmission of anxiety and depressive symptoms from parents to early adolescents (when these symptoms start to increase), while considering timing effects of exposure to parent anxiety and depressive symptoms postnatally. The sample was from the Early Growth and Development Study, including 561 adopted children (57% male, 55% White, 13% Black/African American, 11% Hispanic/Latine, 20% multiracial, 1% other; 407 provided data in early adolescence) and their birth (BP) and adoptive parents (AP). Using a trait–state–occasion model with eight assessments from child ages 9 months to 11 years, we partitioned trait-like AP anxiety and depressive symptoms from time-specific fluctuations of AP anxiety and depressive symptoms. Offspring anxiety and depressive symptoms were assessed at 11 years (while controlling for similar symptoms at 4.5 years). Results suggested that time-specific fluctuations of AP1 (mostly mothers) anxiety/depressive symptoms in infancy (9 months) were indirectly associated with offspring anxiety/depressive symptoms at 11 years via offspring anxiety/depressive symptoms at 4.5 years; time-specific fluctuations of AP1 anxiety/depressive symptoms at child age 11 years were concurrently associated with offspring anxiety/depressive symptoms at 11 years. AP2 (mostly fathers) anxiety/depressive symptoms were not associated with offspring symptoms. Genetic and prenatal influences measured by BP internalizing problems were not associated with offspring symptoms. Results suggested infancy and early adolescence as developmental periods when children are susceptible to influences of parent anxiety and depressive symptoms. Preventive interventions should consider time-specific fluctuations in parent anxiety and depressive symptoms during these developmental periods. Public Significance Statement: Anxiety and depressive symptoms run in families mainly through environmental influences. We found that time-specific fluctuations of adoptive parent (mostly mothers) anxiety and depressive symptoms during infancy and early adolescence were associated with early adolescent anxiety and depressive symptoms. Programs aimed at preventing anxiety and depressive symptoms during early adolescence could screen for time-specific fluctuations of parent (mainly mothers) anxiety and depressive symptoms during infancy and early adolescence. [ABSTRACT FROM AUTHOR]

With increasing popularity, growth curve modeling is more and more often considered as the 1st choice for analyzing longitudinal data. Although the growth curve approach is often a good choice, other modeling strategies may more directly answer questions of interest. It is common to see researchers fit growth curve models without considering alterative modeling strategies. In this article we compare 3 approaches for analyzing longitudinal data: repeated measures analysis of variance, covariance pattern models, and growth curve models. As all are members of the general linear mixed model family, they represent somewhat different assumptions about the way individuals change. These assumptions result in different patterns of covariation among the residuals around the fixed effects. In this article, we first indicate the kinds of data that are appropriately modeled by each and use real data examples to demonstrate possible problems associated with the blanket selection of the growth curve model. We then present a simulation that indicates the utility of Akaike information criterion and Bayesian information criterion in the selection of a proper residual covariance structure. The results cast doubt on the popular practice of automatically using growth curve modeling for longitudinal data without comparing the fit of different models. Finally, we provide some practical advice for assessing mean changes in the presence of correlated data. [ABSTRACT FROM AUTHOR]

Objective: The present article aimed to demonstrate that the establishment of dynamic patterns during the course of psychotherapy can create attractor states for continued adaptive change following the conclusion of treatment. Method: This study is a secondary analysis of T. D. Borkovec and E. Costello (1993). Of the 55 participants in the original study, 33 were retained for the present analysis due to the homogeneity of psychotherapy outcome among these participants. Of these 33, the majority were White (88%) and female (70%), and the average age was 35.44 years (SD = 14.46). Participants participated in 12 weeks of either cognitive behavioral therapy or applied relaxation. Daily diary entries from the treatment period were subjected to time series analyses in order to determine the degree of order versus disorder present within individual dynamic systems. These idiographic data were then aggregated for nomothetic analysis of treatment outcome via linear mixed effect models. Results: Spectral power due to daily to intradaily oscillations in thrice-daily diary data significantly moderated reliable change over posttreatment follow-up such that lesser power predicted increases in reliable change over the I-year follow-up period. Additionally, residual variance for dynamic factor models significantly moderated the slope for change over the follow-up period, such that lesser variance--and thus greater order in dynamic systems--predicted increases in reliable change. Conclusions: The degree of order in dynamic systems established during therapy acted as an adaptive attractor state, promoting continued positive gains 1 year after the conclusion of therapy. The present study represents an important innovation in the study of dynamic systems in psychotherapy. [ABSTRACT FROM AUTHOR]

This article addresses three issues germane to experimental design and statistical analysis of intraindividual variability such as the articles contained within this special section. First, the time scale of the measurement of a process can have profound effects on the outcome of analyses of the resulting time series. Measurement in time poses special problems in the design of experiments: the time scale of the measurements must be appropriate for the time scale of the process. Second, deterministic and stochastic models should be fit at the individual level and only at a second level should individual differences in parameters be modeled. Third, one must consider the possibility that nomothetic relations may be exposed by the invariance of covariance between latent variables rather than within a factor analytic measurement model. [ABSTRACT FROM AUTHOR]

Individuals change over time, often in complex ways. Generally, studies of change over time have combined individuals into groups for analysis, which is inappropriate in most, if not all, studies of development. The authors explain how to identify appropriate levels of analysis (individual vs. group) and demonstrate how to estimate changes in developmental processes over time using a multivariate nonstationary time series model. They apply this model to describe the changing relationships between a biological son and father and a stepson and stepfather at the individual level. The authors also explain how to use an extended Kalman filter with iteration and smoothing estimator to capture how dynamics change over time. Finally, they suggest further applications of the multivariate nonstationary time series model and detail the next steps in the development of statistical models used to analyze individual-level data. [ABSTRACT FROM AUTHOR]

Researchers across varied fields increasingly are collecting and analyzing intensive longitudinal data (ILD) to examine processes across time at the individual level. Two types of relations are typically examined: lagged and contemporaneous. Lagged relations capture how variables at a prior time point can be used to explain variance in variables at a later time point. These are always modeled using auto- and cross-regressions by means of vector autoregression (VAR). By contrast, there are two types of relations commonly used to model the contemporaneous relations, which model how variables relate instantaneously. Until now, researchers must opt to either model contemporaneous relations as undirected relations among residuals (e.g., partial or full correlations) or as directed relations among the variables (e.g., paths or regressions). The choice for how to model contemporaneous relations has implications for inferences as well as the potential to introduce bias in the VAR lagged relations if the wrong type of relation is used. This article introduces a novel data-driven method, hybrid-group iterative multiple model estimation (GIMME), that provides a solution to the problem of having to choose one or the other type of contemporaneous relation to model. The modeling framework utilized in hybrid-GIMME allows for both types of contemporaneous relations in addition to the standard VAR relations. Both simulated and empirical data were used to test the performance of hybrid-GIMME. Results suggest this is a robust method for recovering contemporaneous relations in an exploratory manner, particularly with an ample number of time points per person. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

An individual's emotions system can be conceived of as a synchronized, coordinated, and/or emergent combination of physiology, experience, and behavioral components. Together, the interplay among these components produce emotional experiences through coordinated excitatory positive feedback (i.e., the mutual amplification of emotion concordance) and/or inhibitory negative feedback (i.e., the damping of emotion regulation) processes. Different system configurations produce differential psychophysiological reactivity profiles, and by implication, differential moment-to-moment emotional experience and long-term development. Applying dynamic systems models to second-by-second psychophysiological and experience time-series data collected from 130 adolescents (age 12.0 to 16.7 years) completing a social stress-inducing speech task, we describe the configuration of adolescents' emotion systems, and examine how differences in the dynamic outputs of those systems (psychophysiological reactivity profile) are related to individual differences in trait anxiety. We found substantial heterogeneity in the coordination patterns of these adolescents. Some individuals' emotion systems were characterized by negative feedback loops (emotion regulation processes), many by unidirectionally connected or independent components, and a few by positive feedback loops (emotion concordance). The reactivity dynamics of respiratory sinus arrhythmia were related to adolescents' level of trait anxiety. Results highlight how dynamic systems models may contribute to our understanding of interindividual and developmental differences. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

With increasing popularity, growth curve modeling is more and more often considered as the 1st choice for analyzing longitudinal data. Although the growth curve approach is often a good choice, other modeling strategies may more directly answer questions of interest. It is common to see researchers fit growth curve models without considering alterative modeling strategies. In this article we compare 3 approaches for analyzing longitudinal data: repeated measures analysis of variance, covariance pattern models, and growth curve models. As all are members of the general linear mixed model family, they represent somewhat different assumptions about the way individuals change. These assumptions result in different patterns of covariation among the residuals around the fixed effects. In this article, we first indicate the kinds of data that are appropriately modeled by each and use real data examples to demonstrate possible problems associated with the blanket selection of the growth curve model. We then present a simulation that indicates the utility of Akaike information criterion and Bayesian information criterion in the selection of a proper residual covariance structure. The results cast doubt on the popular practice of automatically using growth curve modeling for longitudinal data without comparing the fit of different models. Finally, we provide some practical advice for assessing mean changes in the presence of correlated data.