407 publications from this institution
Reliable and accurate measurement of well-delineated constructs is essential for rigorous and replicable research in social psychology, regardless of the research question, research design, method of data collection, or data analytic strategy.
Background: Studies on the association between depression and dementia risk mostly use sum scores on depression questionnaires to model symptomatology severity. Since individual items may contribute differently to this association, this approach has limited validity. Methods: We used network analysis to investigate the functioning of individual Geriatric Depression Scale (GDS-15) items, of which, based on studies that used factor analysis, 3 are generally considered to measure apathy (GDS-3A) and 12 depression (GDS-12D). Functional disability and future dementia were also included in our analysis. Data were extracted from 3229 participants of the Prevention of Dementia by Intensive Vascular care trial (preDIVA), analyzed as a single cohort, yielding 20,542 person-years of observation. We estimated a sparse network by only including connections between variables that could not be accounted for by variance in other variables. For this, we used a repeated L1 regularized regression procedure. Results: This procedure resulted in a selection of 59/136 possible connections. GDS-3A items were strongly connected to each other and with varying strength to several GDS-12D items. Functional disability was connected to all three GDS-3A items and the GDS-12D items “helplessness” and “worthlessness”. Future dementia was only connected to the GDS-12D item “memory problems”, which was in turn connected to the GDS-12D items “unhappiness” and “helplessness” and all three GDS-3A items. Conclusion: Network analysis reveals interesting relationships between GDS items, functional disability and dementia risk. We discuss what implications our results may have for (future) research on the associations between depression and/or apathy with dementia.
In recent years, there has been a growing call to advance psychological theorizing through formal modeling. We answer this by introducing a methodology for developing psychological theories using probabilistic network models (PNMs). Originating in statistical mechanics, PNMs describe networks of interacting elements and have already shaped prominent theories in attitude, emotion, and decision research. We present a systematic guide on how to develop, analyze, and validate PNMs. Central to our framework is a survey of six base models which researchers can start from, extend, and adapt to their context. For each of these models we discuss existing applications and analyze them using two newly developed tools: a NetLogo model for simulations and an R package for visualizing mean-field dynamics. As a case study, we demonstrate the application of PNMs in theory development, before discussing the assumptions and limitations of the framework.
Importance Dynamical systems theory is widely used to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. It has been suggested that the same theory may be used to explain the nature and dynamics of psychiatric disorders, which may come and go with symptoms changing over a lifetime. Here we review evidence for the practical applicability of this theory and its quantitative tools in psychiatry. Observations Emerging results suggest that time series of mood and behavior may be used to monitor the resilience of patients using the same generic dynamical indicators that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforest and tipping elements of the climate system. Other dynamical systems tools used in ecology and climate science open ways to infer personalized webs of causality for patients that may be used to identify targets for intervention. Meanwhile, experiences in ecological restoration help make sense of the occasional long-term success of short interventions. Conclusions and Relevance Those observations, while promising, evoke follow-up questions on how best to collect dynamic data, infer informative timescales, construct mechanistic models, and measure the effect of interventions on resilience. Done well, monitoring resilience to inform well-timed interventions may be integrated into approaches that give patients an active role in the lifelong challenge of managing their resilience and knowing when to seek professional help.
In the present article, multivariate genetic item analyses were employed to address questions regarding the ontology and the genetic and environmental etio
It may be that the task of the new psychometrics is impossible; that fundamental measures will never be constructed. If this is the case, then the truth must be faced that perhaps psychology can never be a science …Paul Kline, 1998
Southward, Cheavens, and Coccaro (2022, Psychological Medicine ) conducted an ambitious investigation aimed at determining the nature of the general p factor of psychopathology by considering the correlation between the p factor and five candidate constructs. Generally, in this area of research, the bifactor model is preferred to the second order common factor model. In this commentary, we identify several interpretational issues concerning the bifactor model, which are based on a realistic psychometric view of latent variables. These issues may hamper the study of the nature of p factor model using the bifactor model.
Pairwise network models such as the Gaussian Graphical Model (GGM) are a powerful and intuitive way to analyze dependencies in multivariate data. A key assumption of the GGM is that each pairwise interaction is independent of the values of all other variables. However, in psychological research this is often implausible. In this paper, we extend the GGM by allowing each pairwise interaction between two variables to be moderated by (a subset of) all other variables in the model, and thereby introduce a Moderated Network Model (MNM). We show how to construct the MNM and propose an L1-regularized nodewise regression approach to estimate it. We provide performance results in a simulation study and show that MNMs outperform the split-sample based methods Network Comparison Test (NCT) and Fused Graphical Lasso (FGL) in detecting moderation effects. Finally, we provide a fully reproducible tutorial on how to estimate MNMs with the R-package mgm and discuss possible issues with model misspecification.
In network approaches to psychopathology, disorders result from the causal interplay between symptoms (e.g., worry → insomnia → fatigue), possibly involving feedback loops (e.g., a person may engage in substance abuse to forget the problems that arose due to substance abuse). The present review examines methodologies suited to identify such symptom networks and discusses network analysis techniques that may be used to extract clinically and scientifically useful information from such networks (e.g., which symptom is most central in a person's network). The authors also show how network analysis techniques may be used to construct simulation models that mimic symptom dynamics. Network approaches naturally explain the limited success of traditional research strategies, which are typically based on the idea that symptoms are manifestations of some common underlying factor, while offering promising methodological alternatives. In addition, these techniques may offer possibilities to guide and evaluate therapeutic interventions.
Three umpires are discussing their mode of operation and defending their integrity as umpires. ‘I call 'em as I see 'em,’ said the first. The second replied, ‘I call 'em as they are.’ The third said, ‘What I call 'em makes 'em what they are.’R. L. Ebel, 1956
Psychological measurement plays an important role in modern society. Teachers have schoolchildren tested for dyslexia or hyperactivity, parents have their children's interests and capacities assessed by commercial research bureaus, countries test entire populations of pupils to decide who goes to which school or university, and corporate firms hire other corporate firms to test the right person for the job. The diversity of psychological characteristics measured in such situations is impressive. There exist tests for measuring an enormous range of capacities, abilities, attitudes, and personality factors; these tests are said to measure concepts as diverse as intelligence, extraversion, quality of life, client satisfaction, neuroticism, schizophrenia, and amnesia. The ever increasing popularity of books of the test-your-emotional-intelligence variety has added to the acceptance of psychological testing as an integral element of society.
All camera data. The full layer 2 and 3 tables contain all unfiltered and filtered detections of visitors respectively. The “id” column is only present in the layer 3 tables, as detections have not been linked to unique visitors yet in layer 2. Additionally, files contenting each experimental time and a file of the map which provides the coordinates of the 32 walls in the area of the art fair where camera data was collected.See each codebook for explanations of files. <br><br>
The use of idiographic research techniques has gained popularity within psychological research and network analysis in particular. Idiographic research has been proposed as a promising avenue for future research, with differences between idiographic results highlighting evidence for radical heterogeneity. However, in the quest to address the individual in psychology, some classic statistical problems, such as those arising from sampling variation and power limitations, should not be overlooked. This article aims to determine to what extent current tools to compare idiographic networks are suited to disentangle true from illusory heterogeneity in the presence of sampling error. To this end, we investigate the performance of tools to inspect heterogeneity (visual inspection, comparison of centrality measures, investigating standard deviations of random effects, and GIMME) through simulations. Results show that power limitations hamper the validity of conclusions regarding heterogeneity and that the power required to assess heterogeneity adequately is often not realized in current research practice. Of the tools investigated, inspecting standard deviations of random effects and GIMME proved the most suited. However, all tools evaluated leave the door wide open to misinterpret all observed variability in terms of individual differences. Hence, the current paper calls for caution in the use and interpretation of new time-series techniques when it comes to heterogeneity.
An effective restructuring of the social sciences around the evolutionary model requires that evolutionary theory has explanatory power with respect to the spread of cultural traits: The causal mechanisms involved should be structurally analogous to those of biological evolution. I argue that this is implausible because phenotypical consequences of cultural traits are not causally relevant to their chances of “survival.”
