Integrating Intra- and Interindividual Phenomena in Psychological Theories.

Psychological science is divided into two distinct methodological traditions. One tradition seeks to understand how people function at the individual level, while the other seeks to understand how people differ from each other. Methodologies that have grown out of these traditions typically rely on different sources of data. While both use statistical models to understand the structure of the data, and these models are often similar, Molenaar (2004) showed that results from one type of analysis rarely transfer to the other, unless unrealistic assumptions hold. This raises the question how we may integrate these approaches. In this paper, we argue that formalized theories can be used to connect intra- and interindividual levels of analysis. This connection is indirect, in the sense that the relationship between theory and data is best understood through the intermediate level of phenomena: robust statistical patterns in empirical data. To illustrate this, we introduce a distinction between intra- and interindividual phenomena, and argue that many psychological theories will have implications for both types of phenomena. Formalization provides us with a methodological tool for investigating what kinds of intra- and interindividual phenomena we should expect to find if the theory under consideration were true.

Productive explanation: A framework for evaluating explanations in psychological science.

The explanation of psychological phenomena is a central aim of psychological science. However, the nature of explanation and the processes by which we evaluate whether a theory explains a phenomenon are often unclear. Consequently, it is often unknown whether a given psychological theory indeed explains a phenomenon. We address this shortcoming by proposing a productive account of explanation: a theory explains a phenomenon to some degree if and only if a formal model of the theory produces the statistical pattern representing the phenomenon. Using this account, we outline a workable methodology of explanation: (a) explicating a verbal theory into a formal model, (b) representing phenomena as statistical patterns in data, and (c) assessing whether the formal model produces these statistical patterns. In addition, we provide three major criteria for evaluating the goodness of an explanation (precision, robustness, and empirical relevance), and examine some cases of explanatory breakdowns. Finally, we situate our framework within existing theories of explanation from philosophy of science and discuss how our approach contributes to constructing and developing better psychological theories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Network Inference With the Lasso.

Calculating confidence intervals and p-values of edges in networks is useful to decide their presence or absence and it is a natural way to quantify uncertainty. Since lasso estimation is often used to obtain edges in a network, and the underlying distribution of lasso estimates is discontinuous and has probability one at zero when the estimate is zero, obtaining p-values and confidence intervals is problematic. It is also not always desirable to use the lasso to select the edges because there are assumptions required for correct identification of network edges that may not be warranted for the data at hand. Here, we review three methods that either use a modified lasso estimate (desparsified or debiased lasso) or a method that uses the lasso for selection and then determines p-values without the lasso. We compare these three methods with popular methods to estimate Gaussian Graphical Models in simulations and conclude that the desparsified lasso and its bootstrapped version appear to be the best choices for selection and quantifying uncertainty with confidence intervals and p-values.

Differential impact of preventive cognitive therapy while tapering antidepressants versus maintenance antidepressant treatment on affect fluctuations and individual affect networks and impact on relapse: a secondary analysis of a randomised controlled trial.

Background: There is an urgent need to better understand and prevent relapse in major depressive disorder (MDD). We explored the differential impact of various MDD relapse prevention strategies (pharmacological and/or psychological) on affect fluctuations and individual affect networks in a randomised setting, and their predictive value for relapse. Methods: We did a secondary analysis using experience sampling methodology (ESM) data from individuals with remitted recurrent depression that was collected alongside a randomised controlled trial that ran in the Netherlands, comparing: (I) tapering antidepressants while receiving preventive cognitive therapy (PCT), (II) combining antidepressants with PCT, or (III) continuing antidepressants without PCT, for the prevention of depressive relapse, as well as ESM data from 11 healthy controls. Participants had multiple past depressive episodes, but were remitted for at least 8 weeks and on antidepressants for at least six months. Exclusion criteria were: current (hypo)mania, current alcohol or drug abuse, anxiety disorder that required treatment, psychological treatment more than twice per month, a diagnosis of organic brain damage, or a history of bipolar disorder or psychosis. Fluctuations (within-person variance, root mean square of successive differences, autocorrelation) in negative and positive affect were calculated. Changes in individual affect networks during treatment were modelled using time-varying vector autoregression, both with and without applying regularisation. We explored whether affect fluctuations or changes in affect networks over time differed between treatment conditions or relapse outcomes, and predicted relapse during 2-year follow-up. This ESM study was registered at ISRCTN registry, ISRCTN15472145. Findings: Between Jan 1, 2014, and Jan 31, 2015, 72 study participants were recruited, 42 of whom were included in the analyses. We found no indication that affect fluctuations differed between treatment groups, nor that they predicted relapse. We observed large individual differences in affect network structure across participants (irrespective of treatment or relapse status) and in healthy controls. We found no indication of group-level differences in how much networks changed over time, nor that changes in networks over time predicted time to relapse (regularised models: hazard ratios [HR] 1063, 95% CI <0.0001->10 000, p = 0.65; non-regularised models: HR 2.54, 95% CI 0.23-28.7, p = 0.45) or occurrence of relapse (regularised models: odds ratios [OR] 22.84, 95% CI <0.0001->10 000, p = 0.90; non-regularised models: OR 7.57, 95% CI 0.07-3709.54, p = 0.44) during complete follow-up. Interpretation: Our findings should be interpreted with caution, given the exploratory nature of this study and wide confidence intervals. While group-level differences in affect dynamics cannot be ruled out due to low statistical power, visual inspection of individual affect networks also revealed no meaningful patterns in relation to MDD relapse. More studies are needed to assess whether affect dynamics as informed by ESM may predict relapse or guide personalisation of MDD relapse prevention in daily practice. Funding: The Netherlands Organisation for Health Research and Development, Dutch Research Council, University of Amsterdam.

Understanding suicidality in Pacific adolescents in New Zealand using network analysis.

INTRODUCTION: Pacific adolescents in New Zealand (NZ) are three to four times more likely than NZ European adolescents to report suicide attempts and have higher rates of suicidal plans. Suicidal thoughts, plans, and attempts, termed suicidality in this study, result from a complex dynamic interplay of factors, which emerging methodologies like network analysis aim to capture. METHODS: This study used cross-sectional network analysis to model the relationships between suicidality, self-harm, and individual depression symptoms, whilst conditioning on a multi-dimensional set of variables relevant to suicidality. A series of network models were fitted to data from a community sample of New Zealand-born Pacific adolescents (n = 550; 51% male; Mean age (SD) = 17 (0.35)). RESULTS: Self-harm and the depression symptom measuring pessimism had the strongest associations with suicidality, followed by symptoms related to having a negative self-image about looks and sadness. Nonsymptom risk factors for self-harm and suicidality differed markedly. CONCLUSIONS: Depression symptoms varied widely in terms of their contribution to suicidality, highlighting the valuable information gained from analysing depression at the symptom-item level. Reducing the sources of pessimism and building self-esteem presented as potential targets for alleviating suicidality amongst Pacific adolescents in New Zealand. Suicide prevention strategies need to include risk factors for self-harm.

Multimodality and skewness in emotion time series.

The ability to measure emotional states in daily life using mobile devices has led to a surge of exciting new research on the temporal evolution of emotions. However, much of the potential of these data still remains untapped. In this paper, we reanalyze emotion measurements from seven openly available experience sampling methodology studies with a total of 835 individuals to systematically investigate the modality (unimodal, bimodal, and more than two modes) and skewness of within-person emotion measurements. We show that both multimodality and skewness are highly prevalent. In addition, we quantify the heterogeneity across items, individuals, and measurement designs. Our analysis reveals that multimodality is more likely in studies using an analog slider scale than in studies using a Likert scale; negatively valenced items are consistently more skewed than positive valenced items; and longer time series show a higher degree of modality in positive and a higher skew in negative items. We end by discussing the implications of our results for theorizing, measurement, and time series modeling. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Network intervention analysis of anxiety-related outcomes and processes of acceptance and commitment therapy (ACT) for anxious cancer survivors.

OBJECTIVE: Psychotherapies like Acceptance and Commitment Therapy (ACT) are thought to target multiple clinical outcomes by intervening on multiple mechanistic process variables. However, the standard mediation approach does not readily model the potentially complex associations among multiple processes and outcomes. The current study is one of the first to apply network intervention analysis to examine the putative change processes of a psychotherapy. METHODS: Using data from a randomized trial of ACT versus minimally-enhanced usual care for anxious cancer survivors, we computed pre-to post-intervention (n = 113) residualized change scores on anxiety-related outcomes (general anxiety symptoms, cancer-related trauma symptoms, and fear of cancer recurrence) and putative processes of the intervention (experiential avoidance, self-compassion, and emotional approach coping). We estimated a network model with intervention condition and residualized change scores as nodes. RESULTS: Contrary to the expectation that intervention effects would pass indirectly to outcomes via processes, network analysis indicated that two anxiety-related outcomes of the trial may have acted as primary mechanisms of the intervention on other outcome and process variables. CONCLUSIONS: Network intervention analysis facilitated flexible evaluation of ACT's change processes, and offers a new way to test whether change occurs as theorized in psychotherapies.

A network approach to dyslexia: Mapping the reading network.

Research on the etiology of dyslexia typically uses an approach based on a single core deficit, failing to understand how variations in combinations of factors contribute to reading development and how this combination relates to intervention outcome. To fill this gap, this study explored links between 28 cognitive, environmental, and demographic variables related to dyslexia by employing a network analysis using a large clinical database of 1,257 elementary school children. We found two highly connected subparts in the network: one comprising reading fluency and accuracy measures, and one comprising intelligence-related measures. Interestingly, phoneme awareness was functionally related to the controlled and accurate processing of letter-speech sound mappings, whereas rapid automatized naming was more functionally related to the automated convergence of visual and speech information. We found evidence for the contribution of a variety of factors to (a)typical reading development, though associated with different aspects of the reading process. As such, our results contradict prevailing claims that dyslexia is caused by a single core deficit. This study shows how the network approach to psychopathology can be used to study complex interactions within the reading network and discusses future directions for more personalized interventions.

Reporting standards for psychological network analyses in cross-sectional data.

Statistical network models describing multivariate dependency structures in psychological data have gained increasing popularity. Such comparably novel statistical techniques require specific guidelines to make them accessible to the research community. So far, researchers have provided tutorials guiding the estimation of networks and their accuracy. However, there is currently little guidance in determining what parts of the analyses and results should be documented in a scientific report. A lack of such reporting standards may foster researcher degrees of freedom and could provide fertile ground for questionable reporting practices. Here, we introduce reporting standards for network analyses in cross-sectional data, along with a tutorial and two examples. The presented guidelines are aimed at researchers as well as the broader scientific community, such as reviewers and journal editors evaluating scientific work. We conclude by discussing how the network literature specifically can benefit from such guidelines for reporting and transparency. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The impact of ordinal scales on Gaussian mixture recovery.

Gaussian mixture models (GMMs) are a popular and versatile tool for exploring heterogeneity in multivariate continuous data. Arguably the most popular way to estimate GMMs is via the expectation-maximization (EM) algorithm combined with model selection using the Bayesian information criterion (BIC). If the GMM is correctly specified, this estimation procedure has been demonstrated to have high recovery performance. However, in many situations, the data are not continuous but ordinal, for example when assessing symptom severity in medical data or modeling the responses in a survey. For such situations, it is unknown how well the EM algorithm and the BIC perform in GMM recovery. In the present paper, we investigate this question by simulating data from various GMMs, thresholding them in ordinal categories and evaluating recovery performance. We show that the number of components can be estimated reliably if the number of ordinal categories and the number of variables is high enough. However, the estimates of the parameters of the component models are biased independent of sample size. Finally, we discuss alternative modeling approaches which might be adopted for the situations in which estimating a GMM is not acceptable.

Estimating the number of factors in exploratory factor analysis via out-of-sample prediction errors.

Exploratory factor analysis (EFA) is one of the most popular statistical models in psychological science. A key problem in EFA is to estimate the number of factors. In this article, we present a new method for estimating the number of factors based on minimizing the out-of-sample prediction error of candidate factor models. We show in an extensive simulation study that our method slightly outperforms existing methods, including parallel analysis, Bayesian information criterion (BIC), Akaike information criterion (AIC), root mean squared error of approximation (RMSEA), and exploratory graph analysis. In addition, we show that, among the best performing methods, our method is the one that is most robust across different specifications of the true factor model. We provide an implementation of our method in the R-package fspe. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Modeling psychopathology: From data models to formal theories.

Over the past decade, there has been a surge of empirical research investigating mental disorders as complex systems. In this article, we investigate how to best make use of this growing body of empirical research and move the field toward its fundamental aims of explaining, predicting, and controlling psychopathology. We first review the contemporary philosophy of science literature on scientific theories and argue that fully achieving the aims of explanation, prediction, and control requires that we construct formal theories of mental disorders: theories expressed in the language of mathematics or a computational programming language. We then investigate three routes by which one can use empirical findings (i.e., data models) to construct formal theories: (a) using data models themselves as formal theories, (b) using data models to infer formal theories, and (c) comparing empirical data models to theory-implied data models in order to evaluate and refine an existing formal theory. We argue that the third approach is the most promising path forward. We conclude by introducing the abductive formal theory construction (AFTC) framework, informed by both our review of philosophy of science and our methodological investigation. We argue that this approach provides a clear and promising way forward for using empirical research to inform the generation, development, and testing of formal theories both in the domain of psychopathology and in the broader field of psychological science. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Estimating group differences in network models using moderation analysis.

Statistical network models such as the Gaussian Graphical Model and the Ising model have become popular tools to analyze multivariate psychological datasets. In many applications, the goal is to compare such network models across groups. In this paper, I introduce a method to estimate group differences in network models that is based on moderation analysis. This method is attractive because it allows one to make comparisons across more than two groups for all parameters within a single model and because it is implemented for all commonly used cross-sectional network models. Next to introducing the method, I evaluate the performance of the proposed method and existing approaches in a simulation study. Finally, I provide a fully reproducible tutorial on how to use the proposed method to compare a network model across three groups using the R-package mgm.

Recovering Within-Person Dynamics from Psychological Time Series.

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: first, 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.

The sum of all fears: Comparing networks based on symptom sum-scores.

Researchers are often interested in comparing statistical network models estimated from groups that are defined by the sum-score of the modeled variables. A prominent example is an analysis that compares networks of individuals with and without a diagnosis of a certain disorder. Recently, several authors suggested that this practice may lead to invalid inferences by introducing Berkson's bias. In this article, we show that whether bias is present or not depends on which research question one aims to answer. We review five possible research questions one may have in mind when separately estimating network models in groups that are based on sum-scores. For each research question, we provide an illustration with a simulated bivariate example and discuss the nature of the bias, if present. We show that if one is indeed interested in the network models of the groups defined by the sum-score, no bias is introduced. However, if one is interested in differences across groups defined by a variable other than the sum-score, detecting population heterogeneity, the network model in the general population, or inferring causal relations, then bias will be introduced in most situations. Finally, we discuss for each research question how bias can be avoided. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Does depression moderate the relationship between pain and suicidality in adolescence? A moderated network analysis.

BACKGROUND: Whilst growing research suggests that pain is associated with suicidality in adolescence, it remains unclear whether this relationship is moderated by co-morbid depressive symptoms. The present study aimed to investigate whether the pain-suicidality association is moderated by depressive symptoms. METHODS: We performed secondary analyses on cross-sectional, pre-intervention data from the 'My Resilience in Adolescence' [MYRIAD] trial (ISRCTN ref: 86619085; N=8072, 11-15 years). Using odds ratio tests and (moderated) network analyses, we investigated the relationship between pain and suicidality, after controlling for depression, anxiety, inhibitory control deficits and peer problems. We investigated whether depression moderates this relationship and explored gender differences. RESULTS: Overall, 20% of adolescents reported suicidality and 22% reported pain, whilst nine percent of adolescents reported both. The experience of pain was associated with a four-fold increased risk of suicidality and vice versa (OR=4.00, 95%-CI=[3.54;4.51]), with no gender differences. This cross-sectional association remained significant after accounting for depression, anxiety, inhibitory control deficits and peer problems (aOR=1.39). Depression did not moderate the pain-suicidality association. LIMITATIONS: The item-based, cross-sectional assessment of pain and suicidality precludes any conclusions about the direction of the effects and which aspects of suicidality and pain may drive this association. CONCLUSIONS: Our findings underscore the need to consider pain as an independent risk correlate of suicidality in adolescents. Longitudinal research should examine how this relationship develops during adolescence. Clinically, our findings emphasise the need to assess and address suicidality in adolescents with pain, even in the absence of depressive symptoms.

The Mental Health Ecosystem: Extending Symptom Networks With Risk and Protective Factors.

Inspired by modeling approaches from the ecosystems literature, in this paper, we expand the network approach to psychopathology with risk and protective factors to arrive at an integrated analysis of resilience. We take a complexity approach to investigate the multifactorial nature of resilience and present a system in which a network of interacting psychiatric symptoms is targeted by risk and protective factors. These risk and protective factors influence symptom development patterns and thereby increase or decrease the probability that the symptom network is pulled toward a healthy or disorder state. In this way, risk and protective factors influence the resilience of the network. We take a step forward in formalizing the proposed system by implementing it in a statistical model and translating different influences from risk and protective factors to specific targets on the node and edge parameters of the symptom network. To analyze the behavior of the system under different targets, we present two novel network resilience metrics: Expected Symptom Activity (ESA, which indicates how many symptoms are active or inactive) and Symptom Activity Stability (SAS, which indicates how stable the symptom activity patterns are). These metrics follow standard practices in the resilience literature, combined with ideas from ecology and physics, and characterize resilience in terms of the stability of the system's healthy state. By discussing the advantages and limitations of our proposed system and metrics, we provide concrete suggestions for the further development of a comprehensive modeling approach to study the complex relationship between risk and protective factors and resilience.