Multiple Imputation with Factor Scores: A Practical Approach for Handling Simultaneous Missingness Across Items in Longitudinal Designs.

Missingness in intensive longitudinal data triggered by latent factors constitute one type of nonignorable missingness that can generate simultaneous missingness across multiple items on each measurement occasion. To address this issue, we propose a multiple imputation (MI) strategy called MI-FS, which incorporates factor scores, lag/lead variables, and missing data indicators into the imputation model. In the context of process factor analysis (PFA), we conducted a Monte Carlo simulation study to compare the performance of MI-FS to listwise deletion (LD), MI with manifest variables (MI-MV, which implements MI on both dependent variables and covariates), and partial MI with MVs (PMI-MV, which implements MI on covariates and handles missing dependent variables via full-information maximum likelihood) under different conditions. Across conditions, we found MI-based methods overall outperformed the LD; the MI-FS approach yielded lower root mean square errors (RMSEs) and higher coverage rates for auto-regression (AR) parameters compared to MI-MV; and the PMI-MV and MI-MV approaches yielded higher coverage rates for most parameters except AR parameters compared to MI-FS. These approaches were also compared using an empirical example investigating the relationships between negative affect and perceived stress over time. Recommendations on when and how to incorporate factor scores into MI processes were discussed.

Subgrouping with Chain Graphical VAR Models.

Recent years have seen the emergence of an "idio-thetic" class of methods to bridge the gap between nomothetic and idiographic inference. These methods describe nomothetic trends in idiographic processes by pooling intraindividual information across individuals to inform group-level inference or vice versa. The current work introduces a novel "idio-thetic" model: the subgrouped chain graphical vector autoregression (scGVAR). The scGVAR is unique in its ability to identify subgroups of individuals who share common dynamic network structures in both lag(1) and contemporaneous effects. Results from Monte Carlo simulations indicate that the scGVAR shows promise over similar approaches when clusters of individuals differ in their contemporaneous dynamics and in showing increased sensitivity in detecting nuanced group differences while keeping Type-I error rates low. In contrast, a competing approach-the Alternating Least Squares VAR (ALS VAR) performs well when groups were separated by larger distances. Further considerations are provided regarding applications of the ALS VAR and scGVAR on real data and the strengths and limitations of both methods.

Multilevel Latent Differential Structural Equation Model with Short Time Series and Time-Varying Covariates: A Comparison of Frequentist and Bayesian Estimators.

Continuous-time modeling using differential equations is a promising technique to model change processes with longitudinal data. Among ways to fit this model, the Latent Differential Structural Equation Modeling (LDSEM) approach defines latent derivative variables within a structural equation modeling (SEM) framework, thereby allowing researchers to leverage advantages of the SEM framework for model building, estimation, inference, and comparison purposes. Still, a few issues remain unresolved, including performance of multilevel variations of the LDSEM under short time lengths (e.g., 14 time points), particularly when coupled multivariate processes and time-varying covariates are involved. Additionally, the possibility of using Bayesian estimation to facilitate the estimation of multilevel LDSEM (M-LDSEM) models with complex and higher-dimensional random effect structures has not been investigated. We present a series of Monte Carlo simulations to evaluate three possible approaches to fitting M-LDSEM, including: frequentist single-level and two-level robust estimators and Bayesian two-level estimator. Our findings suggested that the Bayesian approach outperformed other frequentist approaches. The effects of time-varying covariates are well recovered, and coupling parameters are the least biased especially using higher-order derivative information with the Bayesian estimator. Finally, an empirical example is provided to show the applicability of the approach.

Automated measurement of infant and mother Duchenne facial expressions in the Face-to-Face/Still-Face.

Although still-face effects are well-studied, little is known about the degree to which the Face-to-Face/Still-Face (FFSF) is associated with the production of intense affective displays. Duchenne smiling expresses more intense positive affect than non-Duchenne smiling, while Duchenne cry-faces express more intense negative affect than non-Duchenne cry-faces. Forty 4-month-old infants and their mothers completed the FFSF, and key affect-indexing facial Action Units (AUs) were coded by expert Facial Action Coding System coders for the first 30 s of each FFSF episode. Computer vision software, automated facial affect recognition (AFAR), identified AUs for the entire 2-min episodes. Expert coding and AFAR produced similar infant and mother Duchenne and non-Duchenne FFSF effects, highlighting the convergent validity of automated measurement. Substantive AFAR analyses indicated that both infant Duchenne and non-Duchenne smiling declined from the FF to the SF, but only Duchenne smiling increased from the SF to the RE. In similar fashion, the magnitude of mother Duchenne smiling changes over the FFSF were 2-4 times greater than non-Duchenne smiling changes. Duchenne expressions appear to be a sensitive index of intense infant and mother affective valence that are accessible to automated measurement and may be a target for future FFSF research.

Fitting Bayesian Stochastic Differential Equation Models with Mixed Effects through a Filtering Approach.

Recent advances in technology contribute to a fast-growing number of studies utilizing intensive longitudinal data, and call for more flexible methods to address the demands that come with them. One issue that arises from collecting longitudinal data from multiple units in time is nested data, where the variability observed in such data is a mixture of within-unit changes and between-unit differences. This article aims to provide a model-fitting approach that simultaneously models the within-unit changes with differential equation models and accounts for between-unit differences with mixed effects. This approach combines a variant of the Kalman filter, the continuous-discrete extended Kalman filter (CDEKF), and the Markov chain Monte Carlo method often employed in the Bayesian framework through the platform Stan. At the same time, it utilizes Stan's functionality of numerical solvers for the implementation of CDEKF. For an empirical illustration, we applied this method in the context of differential equation models to an empirical dataset to explore the physiological dynamics and co-regulation between couples.

Evaluating Discrete Time Methods for Subgrouping Continuous Processes.

Rapid developments over the last several decades have brought increased focus and attention to the role of time scales and heterogeneity in the modeling of human processes. To address these emerging questions, subgrouping methods developed in the discrete-time framework-such as the vector autoregression (VAR)-have undergone widespread development to identify shared nomothetic trends from idiographic modeling results. Given the dependence of VAR-based parameters on the measurement intervals of the data, we sought to clarify the strengths and limitations of these methods in recovering subgroup dynamics under different measurement intervals. Building on the work of Molenaar and collaborators for subgrouping individual time-series by means of the subgrouped chain graphical VAR (scgVAR) and the subgrouping option in the group iterative multiple model estimation (S-GIMME), we present results from a Monte Carlo study aimed at addressing the implications of identifying subgroups using these discrete-time methods when applied to continuous-time data. Results indicate that discrete-time subgrouping methods perform well at recovering true subgroups when the measurement intervals are large enough to capture the full range of a system's dynamics, either via lagged or contemporaneous effects. Further implications and limitations are discussed therein.

A Square-Root Second-Order Extended Kalman Filtering Approach for Estimating Smoothly Time-Varying Parameters.

Researchers collecting intensive longitudinal data (ILD) are increasingly looking to model psychological processes, such as emotional dynamics, that organize and adapt across time in complex and meaningful ways. This is also the case for researchers looking to characterize the impact of an intervention on individual behavior. To be useful, statistical models must be capable of characterizing these processes as complex, time-dependent phenomenon, otherwise only a fraction of the system dynamics will be recovered. In this paper we introduce a Square-Root Second-Order Extended Kalman Filtering approach for estimating smoothly time-varying parameters. This approach is capable of handling dynamic factor models where the relations between variables underlying the processes of interest change in a manner that may be difficult to specify in advance. We examine the performance of our approach in a Monte Carlo simulation and show the proposed algorithm accurately recovers the unobserved states in the case of a bivariate dynamic factor model with time-varying dynamics and treatment effects. Furthermore, we illustrate the utility of our approach in characterizing the time-varying effect of a meditation intervention on day-to-day emotional experiences.

Spousal Influence on Diabetes Self-care: Moderating Effects of Distress and Relationship Quality on Glycemic Control.

BACKGROUND: Spouses often attempt to influence patients' diabetes self-care. Spousal influence has been linked to beneficial health outcomes in some studies, but to negative outcomes in others. PURPOSE: We aimed to clarify the conditions under which spousal influence impedes glycemic control in patients with type 2 diabetes. Spousal influence was hypothesized to associate with poorer glycemic control among patients with high diabetes distress and low relationship quality. METHODS: Patients with type 2 diabetes and their spouses (N = 63 couples) completed self-report measures before patients initiated a 7-day period of continuous glucose monitoring. Mean glucose level and coefficient of variation (CV) were regressed on spousal influence, diabetes distress, relationship quality, and their two- and three-way interactions. RESULTS: The three-way interaction significantly predicted glucose variability, but not mean level. Results revealed a cross-over interaction between spousal influence and diabetes distress at high (but not low) levels of relationship quality, such that spousal influence was associated with less variability among patients with low distress, but more among those with high distress. Among patients with high distress and low relationship quality, a 1 SD increase in spousal influence predicted a difference roughly equivalent to the difference between the sample mean CV and a CV in the unstable glycemia range. CONCLUSIONS: This was the first study to examine moderators of the link between spousal influence and glycemic control in diabetes. A large effect was found for glucose variability, but not mean levels. These novel results highlight the importance of intimate relationships in diabetes management.

A Person- and Time-Varying Vector Autoregressive Model to Capture Interactive Infant-Mother Head Movement Dynamics.

Head movement is an important but often overlooked component of emotion and social interaction. Examination of regularity and differences in head movements of infant-mother dyads over time and across dyads can shed light on whether and how mothers and infants alter their dynamics over the course of an interaction to adapt to each others. One way to study these emergent differences in dynamics is to allow parameters that govern the patterns of interactions to change over time, and according to person- and dyad-specific characteristics. Using two estimation approaches to implement variations of a vector-autoregressive model with time-varying coefficients, we investigated the dynamics of automatically-tracked head movements in mothers and infants during the Face-Face/Still-Face Procedure (SFP) with 24 infant-mother dyads. The first approach requires specification of a confirmatory model for the time-varying parameters as part of a state-space model, whereas the second approach handles the time-varying parameters in a semi-parametric ("mostly" model-free) fashion within a generalized additive modeling framework. Results suggested that infant-mother head movement dynamics varied in time both within and across episodes of the SFP, and varied based on infants' subsequently-assessed attachment security. Code for implementing the time-varying vector-autoregressive model using two R packages, dynr and mgcv, is provided.

Child Effects on Parental Negativity: The Role of Heritable and Prenatal Factors.

This study examined two possible mechanisms, evocative gene-environment correlation and prenatal factors, in accounting for child effects on parental negativity. Participants included 561 children adopted at birth, and their adoptive parents and birth parents within a prospective longitudinal adoption study. Findings indicated child effects on parental negativity, such that toddlers' negative reactivity at 18 months was positively associated with adoptive parents' over-reactive and hostile parenting at 27 months. Furthermore, we found that child effects on parental negativity were partially due to heritable (e.g., birth mother [BM] internalizing problems and substance use) and prenatal factors (e.g., BM illicit drug use during pregnancy) that influence children's negative reactivity at 18 months. This study provides critical evidence for "child on parent" effects.

A Diagnostic Procedure for Detecting Outliers in Linear State-Space Models.

Outliers can be more problematic in longitudinal data than in independent observations due to the correlated nature of such data. It is common practice to discard outliers as they are typically regarded as a nuisance or an aberration in the data. However, outliers can also convey meaningful information concerning potential model misspecification, and ways to modify and improve the model. Moreover, outliers that occur among the latent variables (innovative outliers) have distinct characteristics compared to those impacting the observed variables (additive outliers), and are best evaluated with different test statistics and detection procedures. We demonstrate and evaluate the performance of an outlier detection approach for multi-subject state-space models in a Monte Carlo simulation study, with corresponding adaptations to improve power and reduce false detection rates. Furthermore, we demonstrate the empirical utility of the proposed approach using data from an ecological momentary assessment study of emotion regulation together with an open-source software implementation of the procedures.

Affect and Personality: Ramifications of Modeling (Non-)Directionality in Dynamic Network Models.

The use of dynamic network models has grown in recent years. These models allow researchers to capture both lagged and contemporaneous effects in longitudinal data typically as variations, reformulations, or extensions of the standard vector autoregressive (VAR) models. To date, many of these dynamic networks have not been explicitly compared to one another. We compare three popular dynamic network approaches-GIMME, uSEM, and LASSO gVAR-in terms of their differences in modeling assumptions, estimation procedures, statistical properties based on a Monte Carlo simulation, and implications for affect and personality researchers. We found that all three approaches dynamic networks provided yielded group-level empirical results in partial support of affect and personality theories. However, individual-level results revealed a great deal of heterogeneity across approaches and participants. Reasons for discrepancies are discussed alongside these approaches' respective strengths and limitations.

Practical Tools and Guidelines for Exploring and Fitting Linear and Nonlinear Dynamical Systems Models.

A dynamical system is a system of variables that show some regularity in how they evolve over time. Change concepts described in most dynamical systems models are by no means novel to social and behavioral scientists, but most applications of dynamic modeling techniques in these disciplines are grounded on a narrow subset of-typically linear-theories of change. I provide practical guidelines, recommendations, and software code for exploring and fitting dynamical systems models with linear and nonlinear change functions in the context of four illustrative examples. Cautionary notes, challenges, and unresolved issues in utilizing these techniques are discussed.

The Differential Time-Varying Effect Model (DTVEM): A tool for diagnosing and modeling time lags in intensive longitudinal data.

With the recent growth in intensive longitudinal designs and the corresponding demand for methods to analyze such data, there has never been a more pressing need for user-friendly analytic tools that can identify and estimate optimal time lags in intensive longitudinal data. The available standard exploratory methods to identify optimal time lags within univariate and multivariate multiple-subject time series are greatly underpowered at the group (i.e., population) level. We describe a hybrid exploratory-confirmatory tool, referred to herein as the Differential Time-Varying Effect Model (DTVEM), which features a convenient user-accessible function to identify optimal time lags and estimate these lags within a state-space framework. Data from an empirical ecological momentary assessment study are then used to demonstrate the utility of the proposed tool in identifying the optimal time lag for studying the linkages between nervousness and heart rate in a group of undergraduate students. Using a simulation study, we illustrate the effectiveness of DTVEM in identifying optimal lag structures in multiple-subject time-series data with missingness, as well as its strengths and limitations as a hybrid exploratory-confirmatory approach, relative to other existing approaches.

Age-related changes in the dynamics of fear-related regulation in early childhood.

Self-regulation is a dynamic process wherein executive processes (EP) delay, minimize or desist prepotent responses (PR) that arise in situations that threaten well-being. It is generally assumed that, over the course of early childhood, children expand and more effectively deploy their repertoire of EP-related strategies to regulate PR. However, longitudinal tests of these assumptions are scarce in part because self-regulation has been mostly studied as a static construct. This study engages dynamic systems modeling to examine developmental changes in self-regulation between ages 2 and 5 years. Second-by-second time-series data derived from behavioral observations of 112 children (63 boys) faced with novel laboratory-based situations designed to elicit wariness, hesitation, and fear were modeled using differential equation models designed to capture age-related changes in the intrinsic dynamics and bidirectional coupling of PR (fear/wariness) and EP (strategy use). Results revealed that dynamic models allow for the conceptualization and measurement of fear regulation as intrinsic processes as well as direct and indirect coupling between PR and EP. Several patterns of age-related changes were in line with developmental theory suggesting that PR weakened and was regulated more quickly and efficiently by EP at age 5 than at age 2. However, most findings were in the intrinsic dynamics and moderating influences between PR and EP rather than direct influences. The findings illustrate the precision with which specific aspects of self-regulation can be articulated using dynamic systems models, and how such models can be used to describe the development of self-regulation in nuanced and theoretically meaningful ways.

National Institutes of Health Pathways to Prevention Workshop: Advancing Research to Prevent Youth Suicide.

The National Institutes of Health (NIH) Pathways to Prevention Workshop "Advancing Research to Prevent Youth Suicide" was cosponsored by the NIH Office of Disease Prevention, National Institute of Mental Health, National Institute on Drug Abuse, and National Center for Complementary and Integrative Health. A multidisciplinary working group developed the agenda, and an evidence-based practice center prepared an evidence report that addressed data systems relevant to suicide prevention efforts through a contract with the Agency for Healthcare Research and Quality. During the workshop, experts discussed the evidence and participants commented during open forums. After considering the data from the evidence report, expert presentations, and public comments, an independent panel prepared a draft report that was posted on the NIH Office of Disease Prevention Web site for 5 weeks for public comment. This abridged version of the final report provides a road map for optimizing youth suicide prevention efforts by highlighting strategies for guiding the next decade of research in this area. These strategies include recommendations for improving data systems, enhancing data collection and analysis methods, and strengthening the research and practice community.

Dynamical Systems Modeling of Couple Interaction: a New Method for Assessing Intervention Impact Across the Transition to Parenthood.

This study explored the use of dynamical systems modeling techniques to evaluate self- and co-regulation of affect in couples' interactions before and after the transition to parenthood, and the impact of the Family Foundations program on these processes. Thirty-four heterosexual couples, randomized to intervention and control conditions, participated in videotaped dyadic interaction tasks at pretest (during pregnancy) and posttest (1 year after birth). Husbands' and wives' positivity and negativity were micro-coded throughout interactions. Individual negativity set-points, self-regulation, and partner co-regulatory processes during interactions were examined using a coupled oscillators model. Regarding self-regulatory processes, men exhibited amplification of negativity at the prenatal assessment that did not change at the postnatal assessment; women demonstrated no significant damping or amplification at pretest and a marginally significant change towards greater amplification at the postnatal assessment. In terms of partner-influenced regulatory dynamics, men's positive behaviors changed from damping to amplifying women's negative behaviors in the control group following the transition to parenthood, but exerted an even stronger damping effect on women's negative behaviors in the intervention group. The study highlights the advantages of dynamic modeling approaches in testing specific hypotheses in the study of self- and co-regulatory couple dynamics and demonstrates the potential of studying dynamic processes to further understanding of developmental and intervention-related change mechanisms.

(Re)evaluating the Implications of the Autoregressive Latent Trajectory Model Through Likelihood Ratio Tests of Its Initial Conditions.

The autoregressive latent trajectory (ALT) model synthesizes the autoregressive model and the latent growth curve model. The ALT model is flexible enough to produce a variety of discrepant model-implied change trajectories. While some researchers consider this a virtue, others have cautioned that this may confound interpretations of the model's parameters. In this article, we show that some-but not all-of these interpretational difficulties may be clarified mathematically and tested explicitly via likelihood ratio tests (LRTs) imposed on the initial conditions of the model. We show analytically the nested relations among three variants of the ALT model and the constraints needed to establish equivalences. A Monte Carlo simulation study indicated that LRTs, particularly when used in combination with information criterion measures, can allow researchers to test targeted hypotheses about the functional forms of the change process under study. We further demonstrate when and how such tests may justifiably be used to facilitate our understanding of the underlying process of change using a subsample (N = 3,995) of longitudinal family income data from the National Longitudinal Survey of Youth.

Trajectories of mothers' emotional availability: relations with infant temperament in predicting attachment security.

The present study examined observations of parenting quality (mothers' emotional availability - EA) during infant bedtimes at 4 points across the infants' first year, assessing relations between levels and trajectories of EA and infant attachment at 12 months and the role of infant temperament in moderating these associations. The sample (N = 128) was predominantly Euro-American (82.5%) and at low socioeconomic risk. Latent growth curve modeling with latent basis coefficients indicated substantial individual differences in initial levels and slopes in EA trajectories across the first year. Both levels of maternal EA and EA trajectories across the first year predicted 12-month infant attachment security. Although maternal EA tended to decrease across the first year in the full sample, EA trajectories that showed a "bounce-back" between 6 and 12 months, suggesting more successful maternal adaptation to an expanding infant developmental repertoire, predicted greater infant security at 12 months. In addition, linkages between latent EA trajectories and 12-month attachment were moderated by 3-month infant temperamental reactivity and regulation. These findings indicate that infant attachment security is sensitive to both static and dynamic aspects of parenting quality across the first year, and that infant temperament can interact with both in predicting infant attachment.