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Contains fulltext : 284223.pdf (Publisher’s version ) (Open Access) Background: The adaptive ability performance test (ADAPT) was developed to assess adaptive skills in individuals with intellectual disabilities and borderline intellectual functioning, with or without mental disorders. As a follow-up to earlier research on the ADAPT, a factor analytic study was conducted. Method: One thousand and sixty six ADAPTs from clients with (suspected) intellectual disabilities or borderline intellectual functioning and 129 ADAPTs from participants from the general population were collected along with other characteristics (e.g., IQ, psychiatric classifications, living situation). Results: An exploratory factor analysis (EFA) was performed and resulted in good fit indices. Subsequent confirmatory factor analysis (CFA) and multigroup CFA showed acceptable to good fit indices. This resulted in an instrument with eight factors and 62 items. Conclusion: Factor analytic results suggest that the ADAPT is a valid instrument that measures adaptive skills in individuals with intellectual disabilities or borderline intellectual functioning. 10 p.

Increasing research uses intensive longitudinal designs to examine antecedents and consequences associated with dynamic affective processes. These studies often rely on the Positive and Negative Affect Schedule (PANAS) to measure affect. Studies assessing the structure of the PANAS are largely cross-sectional in nature and cannot always disentangle within-person variability from between-person differences in affect. A paucity of studies examines structural similarities and differences in affect at the between and within-person levels, and few have done so with short-form versions of the PANAS. This study investigates the multilevel factor structure of the 10-item PANAS–short-form in a sample of young adults (n = 272) measured daily consecutively over 1 month. Additionally, dynamic relations between positive and negative affect, depressive symptoms, stress, and physical symptoms were examined. Results support a three factors within and two factors between multilevel structural model. Distinct dynamic relations were observed among positive affect, negative affect, stress, and physical symptoms at the within level. Positive and negative affect were correlated with depressive symptoms, stress, and physical symptoms at the between level. Findings indicate the need to disentangle structural components of positive and negative affect when conducting intensive longitudinal studies to examine correlates linked to dynamic affective processes.

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 modelling 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.

Network analysis of ESM data has become popular in clinical psychology. In this approach, discrete-time (DT) vector auto-regressive (VAR) models define the network structure with centrality measures used to identify intervention targets. However, VAR models suffer from time-interval dependency. Continuous-time (CT) models have been suggested as an alternative but require a conceptual shift, implying that DT-VAR parameters reflect total rather than direct effects. In this paper, we propose and illustrate a CT network approach using CT-VAR models. We define a new network representation and develop centrality measures which inform intervention targeting. This methodology is illustrated with an ESM dataset.

This article describes some potential uses of Bayesian estimation for time-series and panel data models by incorporating information from prior probabilities (i.e., priors) in addition to observed data. Drawing on econometrics and other literatures we illustrate the use of informative "shrinkage" or "small variance" priors (including so-called "Minnesota priors") while extending prior work on the general cross-lagged panel model (GCLM). Using a panel dataset of national income and subjective well-being (SWB) we describe three key benefits of these priors. First, they shrink parameter estimates toward zero or toward each other for time-varying parameters, which lends additional support for an income → SWB effect that is not supported with maximum likelihood (ML). This is useful because, second, these priors increase model parsimony and the stability of estimates (keeping them within more reasonable bounds) and thus improve out-of-sample predictions and interpretability, which means estimated effect should also be more trustworthy than under ML. Third, these priors allow estimating otherwise under-identified models under ML, allowing higher-order lagged effects and time-varying parameters that are otherwise impossible to estimate using observed data alone. In conclusion we note some of the responsibilities that come with the use of priors which, departing from typical commentaries on their scientific applications, we describe as involving reflection on how best to apply modeling tools to address matters of worldly concern. ispartof: FRONTIERS IN PSYCHOLOGY vol:12 ispartof: location:Switzerland status: published

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 ( 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.

The random intercept cross-lagged panel model (RI-CLPM) is rapidly gaining popularity in psychology and related fields as a structural equation modeling (SEM) approach to longitudinal data. It decomposes observed scores into within-unit dynamics and stable, between-unit differences. This paper discusses three extensions of the RI-CLPM that researchers may be interested in, but are unsure of how to accomplish: (a) including stable, person-level characteristics as predictors and/or outcomes; (b) specifying a multiple-group version; and (c) including multiple indicators. For each extension, we discuss which models need to be run in order to investigate underlying assumptions, and we demonstrate the various modeling options using a motivating example. We provide fully annotated code for lavaan (R-package) and Mplus on an accompanying website.

In recent years, a growing chorus of researchers have argued that psychological theory is in a state of crisis: theories are rarely developed in a way that indicates an accumulation of knowledge and they are often absent from our research entirely. More than 40 years ago, Paul Meehl raised these very concerns. Yet, in the ensuing decades, little has improved. We aim to chart a better path forward for psychological theory by revisiting Meehl's criticisms, his proposed solution, and the reasons his solution failed to meaningful change the status of psychological theory. We argue that Meehl identified serious shortcomings in our evaluation of psychological theories and that his proposed solution would substantially strengthen theory testing. However, we also argue that he failed to provide researchers a set of tools for theory construction. To advance psychological theory, we must equip researchers with tools to better generate, evaluate, and develop their theories. We argue that formal theories provide this much needed set of tools, equipping researchers with tools for thinking, evaluating explanation, informing theory development, strengthening measurement, and moving toward collaborative construction of psychological theories that allow us to explain, predict, and control psychological phenomena.

There has been a strong increase in the number of studies based on intensive longitudinal data, such as those obtained with experience sampling and daily diaries. These data contain a wealth of information regarding the dynamics of processes as they unfold within individuals over time. In this article, we discuss how combining intensive longitudinal data with either time-series analysis, which consists of modeling the temporal dependencies in the data for a single individual, or dynamic multilevel modeling, which consists of using a time-series model at Level 1 to describe the within-person process while allowing for individual differences in the parameters of these processes at Level 2, has led to new insights in clinical psychology. In addition, we discuss several methodological and statistical challenges that researchers face when they are interested in studying the dynamics of psychological processes using intensive longitudinal data.

Complexity science methods offer new opportunities for prognosis and treatment in healthcare and clinical psychology because of the increasing need for integration of the detailed knowledge of physiological and psychological subsystems and the increasing prevalence of multiple disease conditions in our aging societies. This chapter explains how the frequently occurring acute transitions and related tipping points in physical and mental processes in these populations can be monitored with time series and dynamical indicators of resilience. The authors introduce slowing down of recovery, increase in variance and autocorrelation, and increasing cross-correlation between subsystem time series as valid predictors of the proximity of tipping points in diseases such as depression, heart failure and syncope. Using wearable devices, together with these complex systems analyses, yields new methods of forecasting and may improve resilience of individual persons and their mental or physical (organ) subsystems

Scientific research can be categorized into: a) descriptive research, with the main goal to summarize characteristics of a group (or person); b) predictive research, with the main goal to forecast future outcomes that can be used for screening, selection, or monitoring; and c) explanatory research, with the main goal to understand the underlying causal mechanism, which can then be used to develop interventions. Since each goal requires different research methods in terms of design, operationalization, model building and evaluation, it should form an important basis for decisions on how to set up and execute a study. To determine the extent to which developmental research is motivated by each goal and how this aligns with the research designs that are used, we evaluated 100 publications from the Consortium on Individual Development (CID). This analysis shows that the match between research goal and research design is not always optimal. We discuss alternative techniques, which are not yet part of the developmental scientist's standard toolbox, but that may help bridge some of the lurking gaps that developmental scientists encounter between their research design and their research goal. These include unsupervised and supervised machine learning, directed acyclical graphs, Mendelian randomization, and target trials.

One of the promises of the experience sampling methodology (ESM) is that it could be used to identify relevant targets for treatment, based on a statistical analysis of an individual’s emotions, cognitions and behaviors in everyday-life. A requisite for clinical implementation is that outcomes of person-centered analyses are not wholly contingent on the researcher performing them. To evaluate how much researchers vary in their analytical approach and to what degree outcomes vary based on analytical choices, 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. The dataset was from a 25-year-old male with a primary diagnosis of major depressive disorder and comorbid generalized anxiety disorder, who completed momentary assessments related to depression and anxiety psychopathology prior to psychotherapy. 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. Most teams did include a type of vector autoregressive model, which examines relations between variables (e.g., symptoms) over time. Although most teams were confident their selected targets would provide useful information to the clinician, not one advice was similar: both the number (0-16) and nature of selected targets varied widely. 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 methodological issues that need to be addressed in moving toward clinical implementation. Research proposal, data and materials: osf.io/h3djy/

It is useful to investigate factors that could predict treatment outcomes for PTSD. The current study aims to investigate the relationship between daily measured PTSD symptoms during an intensive six-day treatment program and overall post-treatment outcomes. The treatment program combines eye movement desensitization with reprocessing and prolonged exposure, as well as physical activity and psychoeducation. It was expected that for the entire duration of treatment, as well as the first half of the treatment, a greater decline in daily PTSD symptoms would be a predictor for a greater decline in PTSD symptoms at a four-week follow-up. Data from 109 PTSD-patients (87.2% female, mean age = 36.9, SD = 11.5) were used. PTSD symptoms were measured with the CAPS-5 and the self-reported PTSD checklist for DSM-5 (PCL-5). Daily PTSD symptoms were measured with an abbreviated version of the PCL-5 (8-item PCL). Latent growth curve models were used to describe changes in daily PTSD symptoms and predict treatment outcome. Results show that a greater decline in daily PTSD symptoms measured by the 8-item PCL predicts better treatment outcome (CAPS-5 and PCL-5), but that a patient’s PTSD symptoms on the first day of treatment has no predictive effect. A decline in PTSD symptoms only during the first half of treatment was also found to predict treatment outcomes. Future research should be focused on replicating the results of the current study.

School closures due to the COVID-19 pandemic raised concerns about increases in educational inequality. We examined the magnitude of the impact of the first school closure for vulnerable student groups in particular. This study was conducted among 886 Grade 3 - 5 students in the Netherlands in schools serving a high percentage of students from disadvantaged backgrounds. Piecewise growth analyses indicated that the school closures caused discontinuity in students’ achievement growth on national standardized tests and led to an average learning loss of 2.47 months in mathematics and 2.35 in reading comprehension, exceeding the duration of the school closure. Findings suggest that school closures contribute to educational inequality and indicate which students may particularly need additional support to overcome the adverse consequences of the lockdowns.

Background: The adaptive ability performance test (ADAPT) was developed to assess adaptive skills in individuals with intellectual disabilities and borderline intellectual functioning, with or without mental disorders. As a follow-up to earlier research on the ADAPT, a factor analytic study was conducted. Method: One thousand and sixty six ADAPTs from clients with (suspected) intellectual disabilities or borderline intellectual functioning and 129 ADAPTs from participants from the general population were collected along with other characteristics (e.g., IQ, psychiatric classifications, living situation). Results: An exploratory factor analysis (EFA) was performed and resulted in good fit indices. Subsequent confirmatory factor analysis (CFA) and multigroup CFA showed acceptable to good fit indices. This resulted in an instrument with eight factors and 62 items. Conclusion: Factor analytic results suggest that the ADAPT is a valid instrument that measures adaptive skills in individuals with intellectual disabilities or borderline intellectual functioning.

Network analysis of ESM data has become popular in clinical psychology. In this approach, discrete-time (DT) vector autoregressive (VAR) models define the network structure with centrality measures used to identify intervention targets. However, VAR models suffer from time-interval dependency. Continuous Time (CT) models have been suggested as an alternative but require a conceptual shift, implying that DT-VAR parameters reflect total rather than direct effects. In this paper we propose and illustrate a CT network approach using CT-VAR models. We define a new network representation and develop centrality measures which inform intervention targeting. This methodology is illustrated with an ESM dataset.

Negation is a complex grammatical phenomenon that has received considerable attention in the biomedical natural language processing domain. While neural network-based methods are the state-of-the-art in negation scope resolution, they often use the unrealistic assumption that negation cue information is completely accurate. Even if this assumption holds, there remains a dependency on engineered features from state-of-the-art machine learning methods. To tackle this issue, in this study, we adopted a two-step negation resolving approach to assess whether a neural network-based model, here a bidirectional long short-term memory, can be a an alternative for cue detection. Furthermore, we investigate how inaccurate cue predictions would affect the scope resolution performance. We ran various experiments on the open access Bio-Scope corpus. Experimental results suggest that word embeddings alone can detect cues reasonably well, but there still exist better alternatives for this task. As expected, scope resolution performance suffers from imperfect cue information, but remains acceptable on the Abstracts subcorpus. We also found that the scope resolution performance is most robust against inaccurate information for models with a recurrent layer only, compared to extensions with a conditional random field layer and extensions with a post-processing algorithm. We advocate for more research into the application of automated deep learning on the effect of imperfect information on scope resolution.

Idiographic modeling is rapidly gaining popularity, promising to tap into the within-person dynamics underlying psychological phenomena. To gain theoretical understanding of these dynamics, we need to make inferences from time series models about the underlying system. Such inferences are subject to two challenges: time series models will arguably always be misspecified, meaning it is unclear how to make inferences to the underlying system; and second, the sampling frequency must be sufficient to capture the dynamics of interest. We discuss both problems with the following approach: we specify a toy model for emotion dynamics as the true system, generate time series data from it, and then try to recover that system with the most popular time series analysis tools. We show that making straightforward inferences from time series models about an underlying system is difficult. We also show that if the sampling frequency is insufficient, the dynamics of interest cannot be recovered. However, we also show that global characteristics of the system can be recovered reliably. We conclude by discussing the consequences of our findings for idiographic modeling and suggest a modeling methodology that goes beyond fitting time series models alone and puts formal theories at the center of theory development.

Ecological Momentary Assessment (EMA) in which participants report on their moment-to-moment experiences in their natural environment, is a hot topic. An emerging field in clinical psychology based on either EMA, or what we term Ecological Retrospective Assessment (ERA) as it requires retrospectivity, is the field of personalized feedback. In this field, EMA/ERA-data-driven summaries are presented to participants with the goal of promoting their insight in their experiences. Underlying this procedure are some fundamental assumptions about (i) the relation between true moment-to-moment experiences and retrospective evaluations of those experiences, (ii) the translation of these experiences and evaluations to different types of data, (iii) the comparison of these different types of data, and (iv) the impact of a summary of moment-to-moment experiences on retrospective evaluations of those experiences. We argue that these assumptions deserve further exploration, in order to create a strong evidence-based foundation for the personalized feedback procedure.

It is useful to investigate factors that could predict treatment outcomes for PTSD. The current study aims to investigate the relationship between daily measured PTSD symptoms during an intensive six-day treatment program and overall post-treatment outcomes. The treatment program combines eye movement desensitization with reprocessing and prolonged exposure, as well as physical activity and psychoeducation. It was expected that for the entire duration of treatment, as well as the first half of the treatment, a greater decline in daily PTSD symptoms would be a predictor for a greater decline in PTSD symptoms at a four-week follow-up. Data from 109 PTSD-patients (87.2% female, mean age = 36.9, SD = 11.5) were used. PTSD symptoms were measured with the CAPS-5 and the self-reported PTSD checklist for DSM-5 (PCL-5). Daily PTSD symptoms were measured with an abbreviated version of the PCL-5 (8-item PCL). Latent growth curve models were used to describe changes in daily PTSD symptoms and predict treatment outcome. Results show that a greater decline in daily PTSD symptoms measured by the 8-item PCL predicts better treatment outcome (CAPS-5 and PCL-5), but that a patient’s PTSD symptoms on the first day of treatment has no predictive effect. A decline in PTSD symptoms only during the first half of treatment was also found to predict treatment outcomes. Future research should be focused on replicating the results of the current study.

This article compares a general cross-lagged model (GCLM) to other panel data methods based on their coherence with a causal logic and pragmatic concerns regarding modeled dynamics and hypothesis testing. We examine three “static” models that do not incorporate temporal dynamics: random- and fixed-effects models that estimate contemporaneous relationships; and latent curve models. We then describe “dynamic” models that incorporate temporal dynamics in the form of lagged effects: cross-lagged models estimated in a structural equation model (SEM) or multilevel model (MLM) framework; Arellano-Bond dynamic panel data methods; and autoregressive latent trajectory models. We describe the implications of overlooking temporal dynamics in static models and show how even popular cross-lagged models fail to control for stable factors over time. We also show that Arellano-Bond and autoregressive latent trajectory models have various shortcomings. By contrasting these approaches, we clarify the benefits and drawbacks of common methods for modeling panel data, including the GCLM approach we propose. We conclude with a discussion of issues regarding causal inference, including difficulties in separating different types of time-invariant and time-varying effects over time. Australian Research Council https://doi.org/10.13039/501100000923

his is the first paper in a series of two that synthesizes, compares, and extends methods for causal inference with longitudinal panel data in a structural equation modeling (SEM) framework. Starting with a cross-lagged approach, this paper builds a general cross-lagged panel model (GCLM) with parameters to account for stable factors while increasing the range of dynamic processes that can be modeled. We illustrate the GCLM by examining the relationship between national income and subjective well-being (SWB), showing how to examine hypotheses about short-run (via Granger-Sims tests) versus long-run effects (via impulse responses). When controlling for stable factors, we find no short-run or long-run effects among these variables, showing national SWB to be relatively stable, whereas income is less so. Our second paper addresses the differences between the GCLM and other methods. Online Supplementary Materials offer an Excel file automating GCLM input for Mplus (with an example also for Lavaan in R) and analyses using additional data sets and all program input/output. We also offer an introductory GCLM presentation at https://youtu.be/tHnnaRNPbXs. We conclude with a discussion of issues surrounding causal inference. All authors: Michael J. Zyphur, Paul D. Allison, Louis Tay, Manuel C. Voelkle, Kristopher J. Preacher, Zhen Zhang, Ellen L. Hamaker, Ali Shamsollahi, Dean C. Pierides, Peter Koval, Ed Diener

Complexity science methods offer new opportunities for prognosis and treatment in healthcare and clinical psychology because of the increasing need for integration of the detailed knowledge of physiological and psychological subsystems and the increasing prevalence of multiple disease conditions in our aging societies. This chapter explains how the frequently occurring acute transitions and related tipping points in physical and mental processes in these populations can be monitored with time series and dynamical indicators of resilience. The authors introduce slowing down of recovery, increase in variance and autocorrelation, and increasing cross-correlation between subsystem time series as valid predictors of the proximity of tipping points in diseases such as depression, heart failure and syncope. Using wearable devices, together with these complex systems analyses, yields new methods of forecasting and may improve resilience of individual persons and their mental or physical (organ) subsystems