Selected Publications

With the recent growth in intensive longitudinal designs and 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. Available standard exploratory methods to identify optimal time lags within univariate and multivariate multiple subject time series are greatly under-powered 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 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 compared to other existing approaches.
Behavior Research Methods, 2018

Not only do anxiety and depression diagnoses tend to co-occur, but their symptoms are highly correlated. Although a plethora of research has examined longitudinal associations between anxiety and depression, these data have not yet been effectively synthesized. To address this need, the current study undertook a systematic review and meta-analysis of 66 studies involving 88,336 persons examining the prospective relationship between anxiety and depression at both symptom and disorder levels. Using mixed-effect models, results suggested that all types of anxiety symptoms predicted later depressive symptoms (r = .34), and all types of depressive symptoms predicted later anxiety symptoms (r = .31). Although anxiety symptoms more strongly predicted depressive symptoms than vice versa, the difference in effect size for this analysis was very small and likely not clinically meaningful. Additionally, all types of diagnosed anxiety disorders predicted all types of later depressive disorders (OR = 2.77), and all depressive disorders predicted later anxiety disorders (OR = 2.73). Most anxiety and depressive disorders predicted each other with similar degrees of strength, but depressive disorders more strongly predicted social anxiety disorder (OR = 6.05) and specific phobia (OR = 2.93) than vice versa. Contrary to conclusions of prior reviews, our findings suggest that depressive disorders may be prodromes for social and specific phobia, whereas other anxiety and depressive disorders are bidirectional risk factors for one another.
Psychological Bulletin, 2017

Background. Prior research has shown that anxiety symptoms predict later depression symptoms following bereavement. Nevertheless, no research has investigated mechanisms of the temporal relationship between anxiety and later depressive symptoms or examined the impact of depressive symptoms on later anxiety symptoms following bereavement. Methods. The current study examined perceived emotional social support as a possible mediator between anxiety and depressive symptoms in a bereaved sample of older adults (N =250). Anxiety and depressive symptoms were measured at Wave 1 (immediately after bereavement), social support was measured at Wave 2 (18 months after bereavement), and anxiety and depressive symptoms were also measured at Wave 3 (48 months after bereavement). Results. Using Bayesian structural equation models, when controlling for baseline depression, anxiety symptoms significantly positively predicted depressive symptoms 48 months later, Further, perceived emotional social support significantly mediated the relationship between anxiety symptoms and later depressive symptoms, such that anxiety symptoms significantly negatively predicted later emotional social support, and emotional social support significantly negatively predicted later depressive symptoms. Also, when controlling for baseline anxiety, depressive symptoms positively predicted anxiety symptoms 48 months later. However, low emotional social support failed to mediate this relationship. Conclusions. Low perceived emotional social support may be a mechanism by which anxiety symptoms predict depressive symptoms 48 months later for bereaved individuals.
Journal of Affective Disorders, 2017

This study sought to evaluate the current evolutionary adaptiveness of psychopathology by examining whether these disorders impact the quantity of offspring or the quality of the parent–child relationship across the life span. Using the National Comorbidity Survey, this study examined whether DSM–III–R anxiety, posttraumatic stress, depressive, bipolar, substance use, antisocial, and psychosis disorders predicted later fertility and the quality of parent–child relationships across the life span in a national sample (N = 8,098). Using latent variable and varying coefficient models, the results suggested that anxiety in males and bipolar pathology in males and females were associated with increased fertility at younger ages. The results suggested almost all other psychopathology was associated with decreased fertility in middle to late adulthood. The results further suggested that all types of psychopathology had negative impacts on the parent–child relationship quality (except for antisocial pathology in males). Nevertheless, for all disorders, the impact of psychopathology on both fertility and the parent–child relationship quality was affected by the age of the participant. The results also showed that anxiety pathology is associated with a high-quantity, low-quality parenting strategy followed by a low-quantity, low-quality parenting strategy. Further, the results suggest that bipolar pathology is associated with an early high-quantity and a continued low-quality parenting strategy. Posttraumatic stress, depression, substance use, antisocial personality, and psychosis pathology are each associated with a low-quantity, low-quality parenting strategy, particularly in mid to late adulthood. These findings suggest that the evolutionary impact of psychopathology depends on the developmental context.
Journal of Abnormal Psychology, 2016

Recent Publications

More Publications

(2018). Handling missing data in the modeling of intensive longitudinal data. Structural Equation Modeling.

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(2017). A theoretical and empirical modeling of anxiety integrated with RDoC and temporal dynamics. Journal of Anxiety Disorders.

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(2017). Perceived emotional social support in bereaved spouses mediates the relationship between anxiety and depression. Journal of Affective Disorders.

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(2017). Teaching strategies for personality assessment at the undergraduate level. Journal of Personality Assessment.

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Recent & Upcoming Talks

Depression Variability Predicts Later Anxiety for Those with Depressive Disorders
Jun 1, 2018 1:00 PM

Recent Posts

Loading in the Dataset This code will illustrate the R package (DTVEM) with simulated data available in the DTVEM package. Click here to download and install the DTVEM package. First load the DTVEM package. library(DTVEM) Next load the simulated data included in the DTVEM package, called exampledat1. data(exampledat1) Get a look at the file structure. head(exampledat1) ## Time X ID ## 1 1 -1.076422 1 ## 2 2 -1.904713 1 ## 3 3 1.

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Projects

Mood Triggers

This describes a smartphone application to help users figure out triggers of their anxiety and depression in daily life.

The Differential Time Varying Effect Model

This project describes a novel technique entitled the Differential Time-Varying Effect Model, which is a tool to explore lags in intensive longitudinal data.

Using Intensive Longitudinal Data to Study Affective Dynamics

Studying affective dynamics from intraindividual variability in intensive longitudinal data.

Teaching

I have taught the following courses at Pennsylvania State University:

  • PSYCH 238: Introduction to Personality Psychology
  • PSYCH 301W: Basic Research Methods in Psychology
  • PSYCH 481: Introduction to Clinical Psychology

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