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Our aim in this chapter is to discuss structural equation modeling (SEM) as it is applied in human quantitative genetics. Just as SEM is often referred to as covariance structure modeling, the application of SEM in genetics is often referred to as genetic covariance structure modeling (GCSM). Taking the seminal paper by Martin and Eaves (1977) as a starting point, the genetic analysis of covariance structures now spans a period of nearly 50 years (see Hottenga & Boomsma, 2008, for a brief history). Martin and Eaves (1977) were the first to publish an account of GCSM of twin data using Maximum Likelihood (ML) estimation based on the work of Jöreskog (1973). While Martin and Eaves developed their own program, several authors working in the field of behavior genetics and twin research realized that the user-friendly LISREL program, developed by Jöreskog and Sörbom from the 1970s onwards, could be used for genetic model fitting (Boomsma & Molenaar, 1986; Fulker, Baker, & Bock, 1983). The adoption of the LISREL program cemented the view of quantitative genetic modeling as an instance of SEM of data observed in relatives. The general aim was to obtain estimates of genetic and environmental variance components making up the phenotypic variance. The adoption of the LISREL program also encouraged applications of multivariate SEM, e.g., the common factor model, autoregressive (simplex), cross-lagged and growth curve models in genetically informative designs. It inspired approaches specific to genetics, such as Direction of Causation models, common vs. independent pathway models, and moderation models. The advent of measured genetic variables has inspired SEM for genotype-environment correlation and interaction, and intergenerational transmission). Here we introduce GCSM as it is applied in the classical twin design, and its extensions to other designs comprising larger pedigrees. We first present the basic method of exploiting familial relationships to infer the effects of unmeasured genetic and environmental factors. We explain that any SEM can be incorporated in GCSM of family data to estimate and model genetic and environmental covariance matrices. Next, we discuss several extensions that include the analysis of multi-generation data, causality, moderation of genetic influences to assess genotype-environment interaction, and the analysis of genotype-environment covariance. Finally, we consider some developments that focus on measured genetic variables in GCSM analyses and genomic SEM, which focuses on the SEM of genetic covariance matrices based on results from genetic association studies (Grotzinger et al., 2019).

Item does not contain fulltext This chapter discusses theoretical foundations of network analysis by outlining different ways in which networks can be used. In the broadest of these ways, often denoted the network approach, the researcher views a substantive phenomenon through the lens of networks. In this case, networks mainly function to organize observations and to suggest theoretical ideas. The network approach can subsequently be specified in at least two ways. First, by constructing psychometric network models, which formulate a probability distribution for a set of observations, typically by representing variables as nodes and conditional associations between these variables as edges. Second, by constructing network theories, which offer putative explanations of empirical phenomena. Thus, where network theories are tied to a particular empirical domain, network models are generic, i.e., independent of any particular domain. Finally, the chapter discusses ways in which the relation between network approaches, network theories, and network models can be understood.

De rijksoverheid heeft verschillende maatregelen genomen om de verspreiding van het coronavirus SARS-CoV-2 tegen te gaan. Het was onder andere belangrijk dat mensen thuisbleven als zij corona-achtige klachten hadden. Als mensen in contact waren geweest met een besmet persoon, werd hen geadviseerd in quarantaine te gaan. En als zij zelf corona hadden, moesten ze thuis in isolatie. Uit eerder landelijk onderzoek van de Corona Gedragsunit van het RIVM bleek dat een op de drie deelnemers niet altijd thuisbleven als zij in isolatie hoorden te zijn. Het RIVM onderzocht daarom in het voorjaar 2021 in de gemeente Dronten en gemeente Bunschoten wat mensen zou helpen om zich beter aan de maatregelen te houden. Deelnemers zijn hiervoor in contact gebracht met een lokale hulporganisatie die hen bij de isolatie ondersteunde. Onderzocht is of het uitmaakt hoe zij met deze ondersteuning in contact zijn gekomen (actief of passief) en welke ondersteuning hen is aangeboden. Ongeveer de helft van de deelnemers maakte graag gebruik van isolatie-ondersteuning. Deelnemers kwamen vaker in contact met de hulporganisaties als deze organisaties hen – met hun toestemming – daarover belden (actief). Als mensen alleen op het bestaan van de ondersteuning werden gewezen (passief), maakten ze nauwelijks gebruik daarvan. De organisaties boden de hulp in twee vormen aan: een telefonisch ondersteuningsgesprek met een thuisblijfplanner, en een thuisblijftas. In deze tas zat bijvoorbeeld praktische informatie over online-activiteiten, een bon voor een gratis thuisbezorgd ontbijt en een folder met tips. Drie op de vier mensen in isolatie wilden graag deze thuisblijftas ontvangen. Een op de vier deelnemers gaf aan een ondersteuningsgesprek te willen met een hulporganisatie. Tijdens dit gesprek werd besproken welke situaties het lastig maakten om thuis te blijven en welke oplossingen daarvoor mogelijk waren. Bijvoorbeeld hoe mensen kunnen organiseren dat er boodschappen thuis worden bezorgd en de hond wordt uitgelaten. Uiteindelijk deden aan dit onderzoek te weinig mensen mee om te kunnen bewijzen dat zij zich door de ondersteuning beter aan de maatregel hielden. De Corona Gedragsunit van het RIVM voerde dit onderzoek samen met de Universiteit van Amsterdam en betrokken gemeenten en ggd’en uit.

This chapter discusses time-varying network models, which allow for network models with parameters that change across time. These models are relevant when one is interested in how individuals change across time. The chapter introduces the reader to time-varying models and discusses various methods to estimate them, which make different assumptions about the true time-varying nature of parameters. Finally, the chapter illustrates one of the methods by fitting a time-varying Gaussian graphical model (GGM) to a time series of mood related measurements.

This commentary reflects on the articles included in the Psychometrika Special Issue on Network Psychometrics in Action. The contributions to the special issue are related to several possible future paths for research in this area. These include the development of models to analyze and represent interventions, improvement in exploratory and inferential techniques in network psychometrics, the articulation of psychometric theories in addition to psychometric models, and extensions of network modeling to novel data sources. Finally, network psychometrics is part of a larger movement in psychology that revolves around the analysis of human beings as complex systems, and it is timely that psychometricians start extending their rich modeling tradition to improve and extend the analysis of systems in psychology.

This chapter introduces the reader to studying psychological dynamics operating on network models. The chapter will illustrate such dynamics using the Ising model, an undirected network model for binary variables in which nodes mutually influence one-another. First, the chapter discusses the basics of the Ising model. Second, the chapter introduces the reader to dynamics emerging from the Ising model, such as polarization and hysteresis. These dynamics will be illustrated using the example of attitude networks. Third, the chapter will illustrate how the Ising model can be used to model cross-sectional phenomena as an alternative to latent trait theories. General intelligence will be used as an example for this illustration to show how the positive manifold and block structures in correlation matrices can arise from a network implemented in the Ising model.

This chapter introduces pairwise Markov random fields (PMRFs), a class of models of which the parameters can be represented as an undirected network. In this undirected network nodes represent variables and edges represent the strength of association between two variables after conditioning on all other variables included in the model. The chapter focuses on specific classes of PMRFs often used in network psychometrics: Gaussian graphical models (GGM; a network of partial correlations) for continuous data, Ising models for binary data, and mixed graphical models (MGM) for data with different types of variables. PMRFs can be interpreted in multiple ways: the models can be used as a general statistical modeling framework, as an exploratory tool to investigate predictive relationships between variables, as a tool to generate causal hypotheses, as a causal model itself, and as an exploratory tool to uncover latent variables. The chapter concludes with an introduction to estimating PMRFs from data using the bootnet and psychonetrics packages.

Het ministerie van VWS neemt verschillende maatregelen om de verspreiding van het coronavirus (SARS-CoV-2) onder controle te krijgen. Zo is het belangrijk dat mensen zich laten testen bij klachten of na contact met een besmet persoon. Een onderdeel van de aanpak van VWS is dat grote groepen mensen zich meerdere keren laten testen, ook als ze geen klachten hebben (grootschalig testen). Hiervoor kunnen bijvoorbeeld alle inwoners van een dorp of buurt worden uitgenodigd als er daar veel mensen besmet zijn. VWS wil weten welke omstandigheden stimuleren dat zoveel mogelijk mensen zich laten testen. In twee gemeenten – Dronten en Bunschoten - is onderzocht welk effect twee factoren hebben op het aantal mensen dat zich laat testen en hoe vaak ze dat doen: de afstand tot een testlocatie en de informatie in een uitnodigingsbrief. Het blijkt dat meer mensen zich laten testen als de testlocatie dichtbij is, op minder dan 2 kilometer van hun woning. Ook laten ze zich dan vaker testen. Het effect van de informatie in de uitnodigingsbrieven is niet goed te bepalen. Dat komt omdat er naast de brieven ook in de media (radio, sociale media, reclameborden) veel aandacht aan de pilots is besteed. In beide gemeenten zijn twee soorten brieven gestuurd met een verschillende inhoud. Een deel van de inwoners ontving een brief met alleen basisinformatie over grootschalig testen. Het andere deel ontving een brief waarin de basisinformatie werd aangevuld met informatie over het risico op een besmetting met het coronavirus en de verspreiding ervan. De opkomst was bij beide groepen inwoners bijna even hoog. De Corona Gedragsunit van het RIVM heeft dit onderzoek in samenwerking met de Universiteit van Amsterdam, GGD’en en gemeenten uitgevoerd.

Theories are among the most important tools of science. Lewin (1943) already noted “[t]here is nothing as practical as a good theory”. Although psychologists discussed problems of theory in their discipline for a long time, weak theories are still widespread in most subfields. One possible reason for this is that psychologists lack the tools to systematically assess the quality of their theories. Thagard (1989) developed a computational model for formal theory evaluation based on the concept of explanatory coherence. However, there are possible improvements to Thagard’s (1989) model and it is not available in software that psychologists typically use. Therefore, we developed a new implementation of explanatory coherence based on the Ising model. We demonstrate the capabilities of this new Ising Model of Explanatory Coherence (IMEC) on several examples from psychology and other sciences. It is also available in the R-package IMEC so that it can help scientists to evaluate the quality of their theories in practice.

Well-informed psychometrics textbooks do not appear with clockwork regularity. Despite the importance of psychometric techniques to a wide variety of scientific disciplines and societal applications, many existing treatments are either too difficult for an introductory purpose or too superficial to support a full course on psychometrics.Given this situation, the appearance of Patrick Mair’s Psychometrics with R is a breath of fresh air in the landscape of psychometrics teaching. The book provides a mostly accessible treatment of a wide variety of techniques that are relevant to psychometrics, and illustrates these by means of analytic examples that are supported by freely downloadable R-code.

Psychometrische gegevens spelen een steeds belangrijker rol in onze maatschappij. Van IQ-score tot Citotoets en van persoonlijkheidstest tot psychodiagnostiek; iedereen krijgt ergens in zijn of haar leven wel eens met een psychologische test te maken. De statistische analyse van testscores, die op deze manier verzameld worden, is het domein vande psychometrie. De centrale taakstelling van de psychometrie is het ontwikkelen vanmodellen die hiervoor optimaal geschikt zijn. In dit artikel geven Denny Borsboom en Maarten Marsman een overzicht van de belangrijkste ideeën en wiskundige representaties die geassocieerd zijn met netwerkbenaderingen van de psychometrie, en zij illustreren de hieruit voortkomende technieken met empirische data. Daarnaast bespreken zij openstaande kwesties en te verwachten ontwikkelingen.

Background One of the oldest and most influential theories of psychosis formation states that delusions arise in an attempt to explain unusual experiences, including perceptual aberrations. The White Noise Task by Galdos et al (2011) was developed as an experimental task to assess the tendency to attribute meaning to random perceptual stimuli: speech illusions in white noise. Studies to date have demonstrated that speech illusions as assessed with the White Noise Task are associated with a composite measure of positive symptoms in patients with psychotic disorders (Galdos et al, 2011; Catalan et al, 2014). However, findings in non-clinical samples have been inconsistent: one study found an association with a composite measure of subclinical positive symptoms, including support for a relation with familial psychosis liability (Galdos et al, 2011), whereas other studies did not find any association in non-clinical samples or only partly (Catalan et al, 2014; Rimvall et al, 2016; Pries et al, 2017). The current study aims to further examine whether speech illusions as assessed with the White Noise Task are indicative of psychosis liability and to explore specific symptomatic pathways. Methods We conducted symptom-based network analyses in Ultra-High Risk (UHR) subjects participating in the European network of national networks studying gene-environment interactions in schizophrenia project (EU-GEI, 2014; www.eu-gei.eu). Psychotic symptoms were assessed with the Brief Psychiatric Rating Scale (BPRS). Transition to clinical psychosis was assessed with the Comprehensive Assessment of At Risk Mental State (CAARMS). We used a conservative measure of speech illusions, as described in Catalan et al (2014). Results The current sample consisted of 339 UHR subjects, of which 9.1% (N=31) experienced speech illusions. Preliminary network analyses in cross-sectional baseline data showed potential pathways from speech illusions to delusional ideation, through hallucinatory experiences. We also found evidence of prospective relations between speech illusions at baseline and transition to clinical psychosis. Pathways ran via baseline psychotic symptoms and affective symptoms, as well as a ‘direct’ pathway. Discussion As far as we are aware, this is the first study combining an experimental measure of aberrant experiences with symptom-based network analysis. Although the current reported findings are preliminary and exploratory, they tentatively support a relation between speech illusions as assessed with the White Noise Task and psychosis liability. This relation may be dependent on sample composition, and not generalizable to the general population as a whole. Future studies may benefit from focusing on more detailed trajectories of both susceptibility to speech illusions and course of (sub)clinical psychotic symptom severity in subjects with increased risk for psychosis, with use of more frequent, short assessment periods and inclusion of environmental risk factors for transition to clinical disorder.

The network approach to clinical psychology is a relatively new approach and diverges on various aspects from existing models and theories. The hallmark of the theory is that there is no common cause that underlies a set of symptoms. Instead, the network approach starts out by assuming that symptoms causally interact with each other. In this chapter, we first explain the conceptualization of psychological phenomena as a network in the introduction. Second, we provide an overview of the methods that are used to construct network models from data; both Gaussian and binary data, as well as cross‐sectional and longitudinal data are covered. Third, we describe how a given network can be analyzed to uncover important symptoms in the network, to predict behavior of the network, and to compare network structures. Fourth, we discuss current state‐of‐the‐art research in clinical psychology and psychiatry, to see what these networks taught us about psychopathology. Finally, we discuss the promising prospects for clinical psychology research that the network approach has to offer and some of the challenges a researcher might face in applying this approach to clinical psychology data.

Spousal bereavement can cause a rise in depressive symptoms. This study empirically evaluates 2 competing explanations concerning how this causal effect is brought about: (a) a traditional latent variable explanation, in which loss triggers depression which then leads to symptoms; and (b) a novel network explanation, in which bereavement directly affects particular depression symptoms which then activate other symptoms. We used data from the Changing Lives of Older Couples (CLOC) study and compared depressive symptomatology, assessed via the 11-item Center for Epidemiologic Studies Depression Scale (CES-D), among those who lost their partner (N = 241) with a still-married control group (N = 274). We modeled the effect of partner loss on depressive symptoms either as an indirect effect through a latent variable, or as a direct effect in a network constructed through a causal search algorithm. Compared to the control group, widow(er)s' scores were significantly higher for symptoms of loneliness, sadness, depressed mood, and appetite loss, and significantly lower for happiness and enjoyed life. The effect of partner loss on these symptoms was not mediated by a latent variable. The network model indicated that bereavement mainly affected loneliness, which in turn activated other depressive symptoms. The direct effects of spousal loss on particular symptoms are inconsistent with the predictions of latent variable models, but can be explained from a network perspective. The findings support a growing body of literature showing that specific adverse life events differentially affect depressive symptomatology, and suggest that future studies should examine interventions that directly target such symptoms. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Mental disorders have traditionally been conceptualized as latent variables, which impact observable symptomatology. Recent alternative approaches, however, view mental disorders as systems of mutually reinforcing symptoms, and utilize network models to analyze the structure of these symptom-symptom interactions. This paper gives an introduction to and overview of the network approach to psychopathology, as it has developed over the past years

About 17% of humanity goes through an episode of major depression at some point in their lifetime. Despite the enormous societal costs of this incapacitating disorder, it is largely unknown how the likelihood of falling into a depressive episode can be assessed. Here, we show for a large group of healthy individuals and patients that the probability of an upcoming shift between a depressed and a normal state is related to elevated temporal autocorrelation, variance, and correlation between emotions in fluctuations of autorecorded emotions. These are indicators of the general phenomenon of critical slowing down, which is expected to occur when a system approaches a tipping point. Our results support the hypothesis that mood may have alternative stable states separated by tipping points, and suggest an approach for assessing the likelihood of transitions into and out of depression.

This chapter presents a commentary on representation and explanation in psychometric modelling, as discussed in the previous chapter. It explores Turkheimer’s arguments that standard psychometric techniques involve assumptions, conventions, and definitions that introduce an arbitrary component into the models used.

The International Classification of Diseases and Related Health Problems (ICD) and the Diagnostic and Statistical Manual of Mental Disorders (DSM) represent dominant approaches to diagnosis of mental disorders. However, it is unclear how these alternative systems relate to each other when taking into account the symptoms that make up the disorders. This study uses a network approach to investigate the overlap in structure between diagnostic networks pertaining to ICD-10 and DSM-IV-TR. Networks are constructed by representing individual symptoms as nodes, and connecting nodes whenever the corresponding symptoms feature as diagnostic criteria for the same mental disorder. Results indicate that, relative to the DSM-IV-TR network, the ICD-10 network contains (a) more nodes, (b) lower level of clustering, and (c) a higher level of connectivity. Both networks show features of a small world, and have similar (of “the same”) high centrality nodes. Comparison to empirical data indicates that the DSM-IV-TR network structure follows comorbidity rates more closely than the ICD-10 network structure. We conclude that, despite their apparent likeness, ICD-10 and DSM-IV-TR harbour important structural differences, and that both may be improved by matching diagnostic categories more closely to empirically determined network structures.