Optimal Instrument Selection using Bayesian Model Averaging for Model Implied Instrumental Variable Two Stage Least Squares Estimators.

Model-Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) is a limited information, equation-by-equation, non-iterative estimator for latent variable models. Associated with this estimator are equation specific tests of model misspecification. One issue with equation specific tests is that they lack specificity, in that they indicate that some instruments are problematic without revealing which specific ones.

Daily reciprocal relationships between affect, physical activity, and sleep in middle and later life.

Background: The daily dynamics among affect, physical activity, and sleep are often explored by taking a unidirectional approach. Yet, obtaining a comprehensive understanding of the reciprocal dynamics among affect and health behaviors is crucial for promoting daily well-being.

Purpose: This study examined the reciprocal associations among affect, physical activity, and sleep in daily life in a U.

Structured Estimation of Heterogeneous Time Series.

How best to model structurally heterogeneous processes is a foundational question in the social, health and behavioral sciences. Recently, Fisher et al. introduced the multi-VAR approach for simultaneously estimating multiple-subject multivariate time series characterized by common and individualizing features using penalized estimation.

Group Integrative Dynamic Factor Models With Application to Multiple Subject Brain Connectivity.

This work introduces a novel framework for dynamic factor model-based group-level analysis of multiple subjects time-series data, called GRoup Integrative DYnamic factor (GRIDY) models. The framework identifies and characterizes intersubject similarities and differences between two predetermined groups by considering a combination of group spatial information and individual temporal dynamics. Furthermore, it enables the identification of intrasubject similarities and differences over time by employing different model configurations for each subject.

What ergodicity means for you.

This paper explores the relation between within-person and between-person research designs using the concept of ergodicity from statistical mechanics in physics. We demonstrate the consequences of ergodicity using several real data examples from previously published studies. We then create several simulated examples that illustrate the independence of within-person processes from between-person differences, and pair these examples with analytic results that reinforce our conclusions.

Contamination bias in the estimation of child maltreatment causal effects on adolescent internalizing and externalizing behavior problems.

Background: When unaddressed, contamination in child maltreatment research, in which some proportion of children recruited for a nonmaltreated comparison group are exposed to maltreatment, downwardly biases the significance and magnitude of effect size estimates. This study extends previous contamination research by investigating how a dual-measurement strategy of detecting and controlling contamination impacts causal effect size estimates of child behavior problems.

Methods: This study included 634 children from the LONGSCAN study with 63 cases of confirmed child maltreatment after age 8 and 571 cases without confirmed child maltreatment.

Associations between somatomotor-putamen resting state connectivity and obsessive-compulsive symptoms vary as a function of stress during early adolescence: Data from the ABCD study.

Obsessive-compulsive symptoms (OCS) are relatively common during adolescence although most individuals do not meet diagnostic criteria for obsessive-compulsive disorder (OCD). Nonetheless, OCS during adolescence are associated with comorbid psychopathologies and behavioral problems. Heightened levels of environmental stress and greater functional connectivity between the somatomotor network and putamen have been previously associated with elevated OCS in OCD patients relative to healthy controls.

Contamination in Observational Research on Child Maltreatment: A Conceptual and Empirical Review With Implications for Future Research.

Contamination is a methodological phenomenon occurring in child maltreatment research when individuals in an established comparison condition have, in reality, been exposed to maltreatment during childhood. The current paper: (1) provides a conceptual and methodological introduction to contamination in child maltreatment research, (2) reviews the empirical literature demonstrating that the presence of contamination biases causal estimates in both prospective and retrospective cohort studies of child maltreatment effects, (3) outlines a dual measurement strategy for how child maltreatment researchers can address contamination, and (4) describes modern statistical methods for generating causal estimates in child maltreatment research after contamination is controlled. Our goal is to introduce the issue of contamination to researchers examining the effects of child maltreatment in an effort to improve the precision and replication of causal estimates that ultimately inform scientific and clinical decision-making as well as public policy.

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.

Metformin-Induced Receptor Turnover Alters Antibody Accumulation in HER-Expressing Tumors.

Metformin has effects beyond its antihyperglycemic properties, including altering the localization of membrane receptors in cancer cells. Metformin decreases human epidermal growth factor receptor (HER) membrane density. Depletion of cell-surface HER decreases antibody-tumor binding for imaging and therapeutic approaches.

Blind Subgrouping of Task-based fMRI.

Significant heterogeneity in network structures reflecting individuals' dynamic processes can exist within subgroups of people (e.g., diagnostic category, gender).

Estimating both directed and undirected contemporaneous relations in time series data using hybrid-group iterative multiple model estimation.

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.

Penalized Estimation and Forecasting of Multiple Subject Intensive Longitudinal Data.

Intensive longitudinal data (ILD) is an increasingly common data type in the social and behavioral sciences. Despite the many benefits these data provide, little work has been dedicated to realize the potential such data hold for forecasting dynamic processes at the individual level. To address this gap in the literature, we present the multi-VAR framework, a novel methodological approach allowing for penalized estimation of ILD collected from multiple individuals.

An introduction to model implied instrumental variables using two stage least squares (MIIV-2SLS) in structural equation models (SEMs).

Structural equation models (SEMs) are widely used to handle multiequation systems that involve latent variables, multiple indicators, and measurement error. Maximum likelihood (ML) and diagonally weighted least squares (DWLS) dominate the estimation of SEMs with continuous or categorical endogenous variables, respectively. When a model is correctly specified, ML and DWLS function well.

Detecting Task-Dependent Functional Connectivity in Group Iterative Multiple Model Estimation with Person-Specific Hemodynamic Response Functions.

Group iterative multiple model estimation (GIMME) has proven to be a reliable data-driven method to arrive at functional connectivity maps that represent associations between brain regions across time in groups and individuals. However, to date, GIMME has not been able to model time-varying task-related effects. This article introduces an extension of GIMME that enables the modeling of the direct and modulatory effects of a task on functional magnetic resonance imaging data collected by using event-related designs.

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.

An Instrumental Variable Estimator for Mixed Indicators: Analytic Derivatives and Alternative Parameterizations.

Methodological development of the model-implied instrumental variable (MIIV) estimation framework has proved fruitful over the last three decades. Major milestones include Bollen's (Psychometrika 61(1):109-121, 1996) original development of the MIIV estimator and its robustness properties for continuous endogenous variable SEMs, the extension of the MIIV estimator to ordered categorical endogenous variables (Bollen and Maydeu-Olivares in Psychometrika 72(3):309, 2007), and the introduction of a generalized method of moments estimator (Bollen et al., in Psychometrika 79(1):20-50, 2014).

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.

Toward the Development of a Wearable Optical Respiratory Sensor for Real-Time Use.

Respiration rate is an important vital sign that can provide insight into a patient's status and health progression. This information is used from critical care to sports and human performance evaluation. The current state of the art has demonstrated effectiveness in monitoring respiration rate with the use of wearable sensors.

Latent variable GIMME using model implied instrumental variables (MIIVs).

Researchers across many domains of psychology increasingly wish to arrive at personalized and generalizable dynamic models of individuals' processes. This is seen in psychophysiological, behavioral, and emotional research paradigms, across a range of data types. Errors of measurement are inherent in most data.

A Limited Information Estimator for Dynamic Factor Models.

Structural equation modeling (SEM) is an increasingly popular method for examining multivariate time series data. As in cross-sectional data analysis, structural misspecification of time series models is inevitable, and further complicated by the fact that errors occur in both the time series and measurement components of the model. In this article, we introduce a new limited information estimator and local fit diagnostic for dynamic factor models within the SEM framework.

Assessing the robustness of cluster solutions obtained from sparse count matrices.

Psychological researchers often seek to obtain cluster solutions from sparse count matrices (e.g., social networks; counts of symptoms that are in common for 2 given individuals; structural brain imaging).