Drawing Conclusions from Cross-Lagged Relationships: Re-Considering the Role of the Time-Interval

The cross-lagged panel model (CLPM), a discrete-time (DT) SEM model, is frequently used to gather evidence for (reciprocal) Granger-causal relationships when lacking an experimental design. However, it is well known that CLPMs can lead to different parameter estimates depending on the time-interval of observation. Consequently, this can lead to researchers drawing conflicting conclusions regarding the sign and/or dominance of relationships. Multiple authors have suggested the use of continuous-time models to address this issue. In this article, we demonstrate the exact circumstances under which such conflicting conclusions occur. Specifically, we show that such conflicts are only avoided in general in the case of bivariate, stable, nonoscillating, first-order systems, when comparing models with uniform time-intervals between observations. In addition, we provide a range of tools, proofs, and guidelines regarding the comparison of discrete- and continuous-time parameter estimates

A systematic review of Bayesian articles in psychology: The last 25 years

Although the statistical tools most often used by researchers in the field of psychology over the last 25 years are based on frequentist statistics, it is often claimed that the alternative Bayesian approach to statistics is gaining in popularity. In the current article, we investigated this claim by performing the very first systematic review of Bayesian psychological articles published between 1990 and 2015 (n = 1,579). We aim to provide a thorough presentation of the role Bayesian statistics plays in psychology. This historical assessment allows us to identify trends and see how Bayesian methods have been integrated into psychological research in the context of different statistical frameworks (e.g., hypothesis testing, cognitive models, IRT, SEM, etc.). We also describe take-home messages and provide “big-picture” recommendations to the field as Bayesian statistics becomes more popular. Our review indicated that Bayesian statistics is used in a variety of contexts across subfields of psychology and related disciplines. There are many different reasons why one might choose to use Bayes (e.g., the use of priors, estimating otherwise intractable models, modeling uncertainty, etc.). We found in this review that the use of Bayes has increased and broadened in the sense that this methodology can be used in a flexible manner to tackle many different forms of questions. We hope this presentation opens the door for a larger discussion regarding the current state of Bayesian statistics, as well as future trends.https://deepblue.lib.umich.edu/bitstream/2027.42/136925/1/A Systematic Review of Bayesian Articles in Psychology The Last 25 Years.pdfDescription of A Systematic Review of Bayesian Articles in Psychology The Last 25 Years.pdf : Main Articl

Comorbidity between depression and anxiety:assessing the role of bridge mental states in dynamic psychological networks

Background: Comorbidity between depressive and anxiety disorders is common. A hypothesis of the network perspective on psychopathology is that comorbidity arises due to the interplay of symptoms shared by both disorders, with overlapping symptoms acting as so-called bridges, funneling symptom activation between symptom clusters of each disorder. This study investigated this hypothesis by testing whether (i) two overlapping mental states "worrying"and "feeling irritated"functioned as bridges in dynamic mental state networks of individuals with both depression and anxiety as compared to individuals with either disorder alone, and (ii) overlapping or non-overlapping mental states functioned as stronger bridges. Methods: Data come from the Netherlands Study of Depression and Anxiety (NESDA). A total of 143 participants met criteria for comorbid depression and anxiety (65%), 40 participants for depression-only (18.2%), and 37 for anxiety-only (16.8%) during any NESDA wave. Participants completed momentary assessments of symptoms (i.e., mental states) of depression and anxiety, five times a day, for 2 weeks (14,185 assessments). First, dynamics between mental states were modeled with a multilevel vector autoregressive model, using Bayesian estimation. Summed average lagged indirect effects through the hypothesized bridge mental states were compared between groups. Second, we evaluated the role of all mental states as potential bridge mental states. Results: While the summed indirect effect for the bridge mental state "worrying"was larger in the comorbid group compared to the single disorder groups, differences between groups were not statistically significant. The difference between groups became more pronounced when only examining individuals with recent diagnoses (< 6 months). However, the credible intervals of the difference scores remained wide. In the second analysis, a non-overlapping item ("feeling down") acted as the strongest bridge mental state in both the comorbid and anxiety-only groups. Conclusions: This study empirically examined a prominent network-approach hypothesis for the first time using longitudinal data. No support was found for overlapping mental states "worrying"and "feeling irritable"functioning as bridge mental states in individuals vulnerable for comorbid depression and anxiety. Potentially, bridge mental state activity can only be observed during acute symptomatology. If so, these may present as interesting targets in treatment, but not prevention. This requires further investigation

Climate-induced changes in the suitable habitat of cold-water corals and commercially important deep-sea fishes in the North Atlantic

The deep sea plays a critical role in global climate regulation through uptake and storage of heat and carbon dioxide. However, this regulating service causes warming, acidification and deoxygenation of deep waters, leading to decreased food availability at the seafloor. These changes and their projections are likely to affect productivity, biodiversity and distributions of deep-sea fauna, thereby compromising key ecosystem services. Understanding how climate change can lead to shifts in deep-sea species distributions is critically important in developing management measures. We used environmental niche modelling along with the best available species occurrence data and environmental parameters to model habitat suitability for key cold-water coral and commercially important deep-sea fish species under present-day (1951–2000) environmental conditions and to project changes under severe, high emissions future (2081–2100) climate projections (RCP8.5 scenario) for the North Atlantic Ocean. Our models projected a decrease of 28%–100% in suitable habitat for cold-water corals and a shift in suitable habitat for deep-sea fishes of 2.0°–9.9° towards higher latitudes. The largest reductions in suitable habitat were projected for the scleractinian coral Lophelia pertusa and the octocoral Paragorgia arborea, with declines of at least 79% and 99% respectively. We projected the expansion of suitable habitat by 2100 only for the fishes Helicolenus dactylopterus and Sebastes mentella (20%–30%), mostly through northern latitudinal range expansion. Our results projected limited climate refugia locations in the North Atlantic by 2100 for scleractinian corals (30%–42% of present-day suitable habitat), even smaller refugia locations for the octocorals Acanella arbuscula and Acanthogorgia armata (6%–14%), and almost no refugia for P. arborea. Our results emphasize the need to understand how anticipated climate change will affect the distribution of deep-sea species including commercially important fishes and foundation species, and highlight the importance of identifying and preserving climate refugia for a range of area-based planning and management tools.S

Dynamic Systems and Causal Structures in Psychology: Connecting Data and Theory

Psychological phenomena are inherently dynamic in nature. Our cognitions, emotions, dispositions and abilities all evolve and vary over time within an individual. This perspective has become mainstream in the fields of clinical psychiatry and psychology in recent years, where the overarching aim of research is to understand the causal mechanisms which underlie psychological disorder. Typically, researchers aim to study those mechanisms by collecting and analyzing non-experimental data. These data may consist of measurements of many individuals at a single time-point (so-called cross-sectional data) or one or more individuals at many closely spaced points in time (so-called intensive longitudinal data). Researchers generally fit relatively simple statistical models to those data and interpret the parameters of those models as reflecting direct causal relationships between psychological processes. There are two problems with this approach. The first of these is that current popular statistical modeling approaches are relatively limited in how they treat the passage of time. For example, the relationship between aspirin intake and headache strength will depend on whether we measure headache levels a minute, an hour, three hours, or a day after an aspirin pill is taken. However, the dependency of statistical relationships on the time-interval between measurements is typically ignored in current practice. The second problem is that of inferring causal relationships from non-experimental data: If we cannot randomly assign some individuals to take aspirin and others not, how do we know if a statistical dependency between aspirin and headache levels is really causal or not? Luckily psychology is not the first field to grapple with these problems. First, dynamical systems theory and specifically differential equation models have been used in fields as diverse as physics, ecology, climatology and biology to understand and describe phenomena that vary over time. Second, the interventionist causal inference framework has been developed by researchers in fields such as epidemiology, econometrics and computer science to help us understand if, how, when and why causal relationships can be inferred from non-experimental data. Inspired by these methodological frameworks, in my dissertation I develop new tools to address these problems in psychological research. In chapter 2 I develop a tool which allows researchers to explore a range of causal structures which produce the same statistical model in cross-sectional data. In chapter 3 I describe how a simple differential equation model for intensive longitudinal data can be used to explore time-interval dependency, and in chapter 4 I show how this approach could potentially be used to better identify targets for psychological interventions. In chapter 5 I explore how existing statistical tools can best be utilized to recover an underlying dynamical system. Finally, in chapter 6 I argue that formal theories of psychological phenomena are urgently needed if we are to understand the dynamic mechanisms underlying psychopathology. This last chapter synthesizes the challenges and benefits of the approaches described in the previous chapters, proposing a framework for the generation, development and testing of formal theories, and detailing the role that statistical models play at each step of this process

Dynamic Systems and Causal Structures in Psychology: Connecting Data and Theory

Psychological phenomena are inherently dynamic in nature. Our cognitions, emotions, dispositions and abilities all evolve and vary over time within an individual. This perspective has become mainstream in the fields of clinical psychiatry and psychology in recent years, where the overarching aim of research is to understand the causal mechanisms which underlie psychological disorder. Typically, researchers aim to study those mechanisms by collecting and analyzing non-experimental data. These data may consist of measurements of many individuals at a single time-point (so-called cross-sectional data) or one or more individuals at many closely spaced points in time (so-called intensive longitudinal data). Researchers generally fit relatively simple statistical models to those data and interpret the parameters of those models as reflecting direct causal relationships between psychological processes. There are two problems with this approach. The first of these is that current popular statistical modeling approaches are relatively limited in how they treat the passage of time. For example, the relationship between aspirin intake and headache strength will depend on whether we measure headache levels a minute, an hour, three hours, or a day after an aspirin pill is taken. However, the dependency of statistical relationships on the time-interval between measurements is typically ignored in current practice. The second problem is that of inferring causal relationships from non-experimental data: If we cannot randomly assign some individuals to take aspirin and others not, how do we know if a statistical dependency between aspirin and headache levels is really causal or not? Luckily psychology is not the first field to grapple with these problems. First, dynamical systems theory and specifically differential equation models have been used in fields as diverse as physics, ecology, climatology and biology to understand and describe phenomena that vary over time. Second, the interventionist causal inference framework has been developed by researchers in fields such as epidemiology, econometrics and computer science to help us understand if, how, when and why causal relationships can be inferred from non-experimental data. Inspired by these methodological frameworks, in my dissertation I develop new tools to address these problems in psychological research. In chapter 2 I develop a tool which allows researchers to explore a range of causal structures which produce the same statistical model in cross-sectional data. In chapter 3 I describe how a simple differential equation model for intensive longitudinal data can be used to explore time-interval dependency, and in chapter 4 I show how this approach could potentially be used to better identify targets for psychological interventions. In chapter 5 I explore how existing statistical tools can best be utilized to recover an underlying dynamical system. Finally, in chapter 6 I argue that formal theories of psychological phenomena are urgently needed if we are to understand the dynamic mechanisms underlying psychopathology. This last chapter synthesizes the challenges and benefits of the approaches described in the previous chapters, proposing a framework for the generation, development and testing of formal theories, and detailing the role that statistical models play at each step of this process

Meta-analysis of Lagged Regression Models: A Continuous-time Approach

In science, the gold standard for evidence is an empirical result which is consistent across multiple studies. Meta-analysis techniques allow researchers to combine the results of different studies. Due to the increasing availability of longitudinal data, studying lagged effects is increasingly popular also in meta-analytic studies. However, in current practice, little attention is paid to the unique challenges of meta-analyzing these lagged effects. Namely, it is well known that lagged effects estimates change depending on the time that elapses between measurement waves. This means that studies that use different uniform time intervals between observations (e.g., 1 hour vs 3 hours or 1 month vs 2 months) can come to very different parameter estimates, and seemingly contradictory conclusions, about the same underlying process. In this article, we introduce, describe, and illustrate a new meta-analysis method (CTmeta) which assumes an underlying continuous-time process, and compare it with current practice