The Challenge of Generating Causal Hypotheses Using Network Models

Statistical network models based on Pairwise Markov Random Fields (PMRFs) are popular tools for analyzing multivariate psychological data, in large part due to their perceived role in generating insights into causal relationships: a practice known as causal discovery in the causal modeling literature. However, since network models are not presented as causal discovery tools, the role they play in generating causal insights is poorly understood among empirical researchers. In this paper, we provide a treatment of how PMRFs such as the Gaussian Graphical Model (GGM) work as causal discovery tools, using Directed Acyclic Graphs (DAGs) and Structural Equation Models (SEMs) as causal models. We describe the key assumptions needed for causal discovery and show the equivalence class of causal models that networks identify from data. We clarify four common misconceptions found in the empirical literature relating to networks as causal skeletons; chains of relationships; collider bias; and cyclic causal models

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

Testing both affordability-availability and psychological-coping mechanisms underlying changes in alcohol use during the COVID-19 pandemic

Two theoretical perspectives have been proffered to explain changes in alcohol use during the pandemic: the ‘affordability-availability’ mechanism (i.e., drinking decreases due to changes in physical availability and/or reduced disposable income) and the ‘psychological-coping’ mechanism (i.e., drinking increases as adults attempt to cope with pandemic-related distress). We tested these alternative perspectives via longitudinal analyses of the COVID-19 Psychological Consortium (C19PRC) Study data (spanning three timepoints during March to July 2020). Respondents provided data on psychological measures (e.g., anxiety, depression, posttraumatic stress, paranoia, extraversion, neuroticism, death anxiety, COVID-19 anxiety, intolerance of uncertainty, resilience), changes in socio-economic circumstances (e.g., income loss, reduced working hours), drinking motives, solitary drinking, and ‘at-risk’ drinking (assessed using a modified version of the AUDIT-C). Structural equation modelling was used to determine (i) whether ‘at-risk’ drinking during the pandemic differed from that recalled before the pandemic, (ii) dimensions of drinking motives and the psychosocial correlates of these dimensions, (iii) if increased alcohol consumption was predicted by drinking motives, solitary drinking, and socio-economic changes. The proportion of adults who recalled engaging in ‘at-risk’ drinking decreased significantly from 35.9% pre-pandemic to 32.0% during the pandemic. Drinking to cope was uniquely predicted by experiences of anxiety and/or depression and low resilience levels. Income loss or reduced working hours were not associated with coping, social enhancement, or conformity drinking motives, nor changes in drinking during lockdown. In the earliest stage of the pandemic, psychological-coping mechanisms may have been a stronger driver to changes in adults’ alcohol use than ‘affordability-availability’ alone

Time to get personal? The impact of researchers choices on the selection of treatment targets using the experience sampling methodology:The impact of researchers choices on the selection of treatment targets using the experience sampling methodology

OBJECTIVE: One of the promises of the experience sampling methodology (ESM) is that a statistical analysis of an individual’s emotions, cognitions and behaviors in everyday-life could be used to identify relevant treatment targets. A requisite for clinical implementation is that outcomes of such person-specific time-series analyses are not wholly contingent on the researcher performing them. METHODS: To evaluate this, we crowdsourced the analysis of one individual patient’s ESM data to 12 prominent research teams, asking them what symptom(s) they would advise the treating clinician to target in subsequent treatment. RESULTS: Variation was evident at different stages of the analysis, from preprocessing steps (e.g., variable selection, clustering, handling of missing data) to the type of statistics and rationale for selecting targets. Most teams did include a type of vector autoregressive model, examining relations between symptoms over time. Although most teams were confident their selected targets would provide useful information to the clinician, not one recommendation was similar: both the number (0–16) and nature of selected targets varied widely. CONCLUSION: This study makes transparent that the selection of treatment targets based on personalized models using ESM data is currently highly conditional on subjective analytical choices and highlights key conceptual and methodological issues that need to be addressed in moving towards clinical implementation

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