Publications

To explain the overt heterogeneous nature of major depressive disorder (MDD), it could be valuable to focus on individual symptoms [1]. Recent research, for example, showed that MDD symptoms differ in their underlying biology, risk factors and psychosocial impairments [for a review, see [2]]. In addition, the presence of specific symptoms (e.g. psychomotor agitation) may have important clinical implications, such as expectations regarding the response to antidepressants [3].

The network approach to psychological phenomena advances our understanding of the interrelations between autism and well-being. We use the Perceived Causal Relations methodology in order to (i) identify perceived causal pathways in the well-being system, (ii) validate networks based on self-report data, and (iii) quantify and integrate clinical expertise in autism research. Trained clinicians served as raters (N = 29) completing 374 cause-effects ratings of 34 variables on well-being and symptomatology. A subgroup (N = 16) of raters chose intervention targets in the resulting network which we found to match the respective centrality of nodes. Clinicians' perception of causal relations was similar to the interrelatedness found in self-reported client data (N = 323). We present a useful tool for translating clinical expertise into quantitative information enabling future research to integrate this in scientific studies.

Nesselroade, Gerstorf, Hardy, and Ram (this issue) propose to "filter out" idiosyncrasies of dynamic processes at the level of the individual through the application of dynamic factor analysis. The problem that they deal with is that individuals may differ in the items that are "salient" for a given construct, so that the same measurement model does not hold across subjects. The proposed method, however, allows for the same constructs to be measured by different items in different subjects, and therefore appears to bypass this problem. Nesselroade et al. accomplish this by first estimating a dynamic factor model for each individual, where the item-factor relations are free to vary across subjects, and then executing a higher order analysis in which the factor intercorrelations are required to be identical across subjects. They motivate this procedure partly by appealing to the literature on measurement invariance; the only difference, in their own words, is that they define "invariance at the level of construct interrelations rather than at the level of construct-observables relations."

In psychology, regulation of measurement practices relies on the use of psychometric models that relate the targeted psychological construct to a set of observations in empirical research. Recent work has argued for a new conceptualization, in which psychological constructs are viewed as networks. However, in contrast to existing models, network psychometrics has not articulated normative guidelines for measurement. To develop normative criteria for measurement in the network framework, a clearer picture is needed about possible measurement targets in a network. Using the example of depression, we explore five such potential measurement targets in the network framework: the structure of the network, the current state of the network, and three properties of the dynamical landscape, the expected state, vulnerability, and stable state. We discuss strategies for measuring these targets as well as challenges that arise in doing so.

Background Insomnia disorder is the second most prevalent mental disorder, and it is a primary risk factor for depression. Inconsistent clinical and biomarker findings in patients with insomnia disorder suggest that heterogeneity exists and that subtypes of this disease remain unrecognised. Previous top-down proposed subtypes in nosologies have had insufficient validity. In this large-scale study, we aimed to reveal robust subtypes of insomnia disorder by use of data-driven analyses on a multidimensional set of biologically based traits. Methods In this series of studies, we recruited participants from the Netherlands Sleep Registry, a database of volunteers aged 18 years or older, who we followed up online to survey traits, sleep, life events, and health history with 34 selected questionnaires of which participants completed at least one. We identified insomnia disorder subtypes by use of latent class analyses. We evaluated the value of our identified subtypes of insomnia disorder by use of a second, non-overlapping cohort who were recruited through a newsletter that was emailed to a new sample of Netherlands Sleep Registry participants, and by assessment of within-subject stability over several years of follow-up. We extensively tested the clinical validity of these subtypes for the development of sleep complaints, comorbidities (including depression), and response to benzodiazepines; in two subtypes of insomnia disorder, we also assessed the clinical relevance of these subtypes by use of an electroencephalogram biomarker and the effectiveness of cognitive behavioural therapy. To facilitate implementation, we subsequently constructed a concise subtype questionnaire and we validated this questionnaire in the second, non-overlapping cohort. Findings 4322 Netherlands Sleep Registry participants completed at least one of the selected questionnaires, a demographic questionnaire, and an assessment of their Insomnia Severity Index (ISI) between March 2, 2010, and Oct 28, 2016. 2224 (51%) participants had probable insomnia disorder, defined as an ISI score of at least 10, and 2098 (49%) participants with a lower ISI score served as a control group. With a latent class analysis of the questionnaire responses of 2224 participants, we identified five novel insomnia disorder subtypes: highly distressed, moderately distressed but reward sensitive (ie, with intact responses to pleasurable emotions), moderately distressed and reward insensitive, slightly distressed with high reactivity (to their environment and life events), and slightly distressed with low reactivity. In a second, non-overlapping replication sample of 251 new participants who were assessed between June 12, 2017, and Nov 26, 2017, five subtypes were also identified to be optimal. In both the development sample and replication sample, each participant was classified as having only one subtype with high posterior probability (0·91–1·00). In 215 of the original sample of 2224 participants with insomnia who were reassessed 4·8 (SD 1·6) years later (between April 13, 2017, and June 21, 2017), the probability of maintaining their original subtype was 0·87, indicating a high stability of the classification. We found differences between the identified subtypes in developmental trajectories, response to treatment, the presence of an electroencephalogram biomarker, and the risk of depression that was up to five times different between groups, which indicated a clinical relevance of these subtypes. Interpretation High-dimensional data-driven subtyping of people with insomnia has addressed an unmet need to reduce the heterogeneity of insomnia disorder. Subtyping facilitates identification of the underlying causes of insomnia, development of personalised treatments, and selection of patients with the highest risk of depression for inclusion in trials regarding prevention of depression. Funding European Research Council and Netherlands Organization for Scientific Research.

In their provocative and interesting article, Baird, Andrich, Hopfeneck, and Stobart (2017) suggest the possibility to articulate a post-modern account of psychometric testing. As a conceptual fram...

Ces dernières années, l’approche réseau de la psychopathologie a été proposée comme moyen alternatif de conceptualiser les troubles mentaux. Dans cette approche, les troubles mentaux résultent d’interactions directes entre les symptômes. Bien que l’approche réseau ait conduit à de nombreuses nouvelles méthodologies et à des applications notables, elle n’a pas encore été entièrement formulée en tant que théorie scientifique des troubles mentaux. L’article présent vise à développer une telle théorie, en postulant un ensemble limité de principes théoriques concernant la structure et la dynamique des réseaux de symptômes. La notion de lien causal, constituée par les symptômes de la psychopathologie, est au centre de cette théorie par le biais d’un certain nombre de mécanismes biologiques, psychologiques et sociaux. Si ces relations de cause à effet sont suffisamment fortes, les symptômes peuvent générer un niveau de rétroaction qui les auto-entretient. Dans ce cas, le réseau peut rester bloqué dans un état pathologique. La théorie des réseaux considère qu’il s’agit d’une caractéristique générale pour les troubles mentaux et que l’on peut donc comprendre ces derniers comme des états alternatifs stables de réseaux de symptômes fortement connectés. Cette idée conduit naturellement à un modèle général de la psychopathologie, intégrant à la fois un modèle explicatif commun pour les troubles mentaux et une nouvelle définition des concepts associés comme ceux de santé mentale, de résilience, de vulnérabilité et de handicap. En outre, la théorie des réseaux a des implications directes sur la manière de comprendre le diagnostic et le traitement, et elle suggère des pistes prometteuses pour les recherches futures en psychiatrie et dans les disciplines qui lui sont associées. In recent years, the network approach to psychopathology has been advanced as an alternative way of conceptualising mental disorders. In this approach, mental disorders arise from direct interactions between symptoms. Although the network approach has led to many novel methodologies and substantive applications, it has not yet been fully articulated as a scientific theory of mental disorders. The present paper aims to develop such a theory by postulating a limited set of theoretical principles regarding the structure and dynamics of symptom networks. At the heart of the theory lies the notion that symptoms of psychopathology are causally connected through myriads of biological, psychological and societal mechanisms. If these causal relations are sufficiently strong, symptoms can generate a level of feedback that renders them self-sustaining. In this case, the network can get stuck in a disorder state. The network theory holds that this is a general feature of mental disorders, which can therefore be understood as alternative stable states of strongly connected symptom networks. This idea naturally leads to a comprehensive model of psychopathology, encompassing a common explanatory model for mental disorders, as well as novel definitions of associated concepts such as mental health, resilience, vulnerability and liability. In addition, the network theory has direct implications for how to understand diagnosis and treatment, and suggests a clear agenda for future research in psychiatry and associated disciplines.

Previous research and methodological advice has focused on the importance of accounting for measurement error in psychological data. That perspective assumes that psychological variables conform to a common factor model, such that they consist of construct variance plus error. In this paper, we explore what happens when a set of items that are not generated from a common factor construct model are nonetheless modeled as reflecting a common factor. Through a series of hypothetical examples and an empirical re-analysis, we show that (1) common factor models tend to produce extremely biased and highly variable structural parameter estimates when the population model is not a common factor model; (2) model fit is a poor indicator of the degree of bias; and (3) composite models are sometimes more reliable than common factor models under alternative measurement structures, though they also lead to unacceptably bad solutions in some cases.

Overview of all data tables available in this project.

This book examines test validity in the behavioral, social, and educational sciences by exploring three fundamental problems: measurement, causation and meaning. Psychometric and philosophical perspectives receive attention along with unresolved issues. The authors explore how measurement is conceived from both the classical and modern perspectives. The importance of understanding the underlying concepts as well as the practical challenges of test construction and use receive emphasis throughout. The book summarizes the current state of the test validity theory field. Necessary background on test theory and statistics is presented as a conceptual overview where needed. Each chapter begins with an overview of key material reviewed in previous chapters, concludes with a list of suggested readings, and features boxes with examples that connect theory to practice. These examples reflect actual situations that occurred in psychology, education, and other disciplines in the US and around the globe, bringing theory to life. Critical thinking questions related to the boxed material engage and challenge readers. A few examples include: What is the difference between intelligence and IQ? Can people disagree on issues of value but agree on issues of test validity? Is it possible to ask the same question in two different languages? The first part of the book contrasts theories of measurement as applied to the validity of behavioral science measures.The next part considers causal theories of measurement in relation to alternatives such as behavior domain sampling, and then unpacks the causal approach in terms of alternative theories of causation.The final section explores the meaning and interpretation of test scores as it applies to test validity. Each set of chapters opens with a review of the key theories and literature and concludes with a review of related open questions in test validity theory.　 Researchers, practitioners and policy makers interested in test validity or developing tests appreciate the book's cutting edge review of test validity. The book also serves as a supplement in graduate or advanced undergraduate courses on test validity, psychometrics, testing or measurement taught in psychology, education, sociology, social work, political science, business, criminal justice and other fields. The book does not assume a background in measurement.

Abstract In this rejoinder to McNeish (2024) and Mislevy (2024), who both responded to our focus article on the merits of the simple sum score (Sijtsma et al., 2024), we address several issues. Psychometrics education and in particular psychometricians’ outreach may help researchers to use IRT models as a precursor for the responsible use of the latent variable score and the sum score. Different methods used for test and questionnaire construction often do not produce highly different results, and when they do, this may be due to an unarticulated attribute theory generating noisy data. The sum score and transformations thereof, such as normalized test scores and percentiles, may help test practitioners and their clients to better communicate results. Latent variables prove important in more advanced applications such as equating and adaptive testing where they serve as technical tools rather than communication devices. Decisions based on test results are often binary or use a rather coarse ordering of scale levels, hence, do not require a high level of granularity (but nevertheless need to be precise). A gap exists between psychology and psychometrics which is growing deeper and wider, and that needs to be bridged. Psychology and psychometrics must work together to attain this goal.

Network approaches that investigate the interaction between symptoms and behaviours have opened new ways of understanding psychological phenomena in health and disorder in recent years. In parallel, network approaches that characterise the interaction between brain regions have become the dominant approach to understanding brain function in neuroimaging research. Combining these parallel approaches would enable new insights into the interaction between behaviours and their brain-level correlates. In this paper, we introduce a methodology for combining network psychometrics and network neuro-science. This approach utilises the information from the psychometric network to obtain neural correlates that are associated with each node in the psychometric network (network-based regression). Moreover, we combine the behavioural variables and their neural correlates in a joint network to characterise their interactions. We illustrate the approach by highlighting the interaction between the triad of autistic traits and their resting-state functional connectivity associations. To this end, we utilise data from 172 male autistic participants (10-21 years) from the autism brain data exchange (ABIDE, ABIDE-II) that completed resting-state fMRI and were assessed using the autism diagnostic interview (ADI-R). Our results indicate that the network-based regression approach can uncover both unique and shared neural correlates of behavioural measures. In addition, because the shared variance between behavioural measures is controlled for in the approach, the methodology enables us to isolate mechanisms at the brain-level that are unique to particular behavioural variables. For instance, our example analysis indicates that the overlap between communication and social difficulties is not reflected in the overlap between their functional brain correlates.

Maul’s target article is insightful and enlightening, and it presents a number of very important recommendations for psychometric practice. While we overwhelmingly agree with the arguments made in ...

The philosophical and methodological commitments that characterize psychological science are largely based on ideas that crystallized in the 20th century and that are associated with the dominant philosophical doctrines of that time, by and large deriving from logical positivism and falsificationism. Although these doctrines do not enjoy widespread support in current philosophy of science, they continue to influence psychological science through their effect on research practices and its, often implicit, methodological principles. In this paper, we argue that at least five of these principles hamper progress in psychology. Epistemic freezing refers to unhelpful conservatism in the use of psychometric measurement techniques; testing myopia concerns the focus on hypothesis testing at the expense of other research practices; data fixation is the belief that data are what require explanation, and that gathering novel and surprising data is key to scientific progress; smallism is the preference for reductive explanation; and mirrorism denotes a focus on developing true theories as opposed to developing pragmatically or epistemically useful ones. We argue that adherence to these principles, and to the research practices that go with it, puts psychological science at risk of stagnation, and we call for a broad discussion to reassess and adapt them.

This article introduces the Causal Attitude Network (CAN) model, which conceptualizes attitudes as networks consisting of evaluative reactions and interactions between these reactions. Relevant evaluative reactions include beliefs, feelings, and behaviors toward the attitude object. Interactions between these reactions arise through direct causal influences (e.g., the belief that snakes are dangerous causes fear of snakes) and mechanisms that support evaluative consistency between related contents of evaluative reactions (e.g., people tend to align their belief that snakes are useful with their belief that snakes help maintain ecological balance). In the CAN model, the structure of attitude networks conforms to a small-world structure: evaluative reactions that are similar to each other form tight clusters, which are connected by a sparser set of "shortcuts" between them. We argue that the CAN model provides a realistic formalized measurement model of attitudes and therefore fills a crucial gap in the attitude literature. Furthermore, the CAN model provides testable predictions for the structure of attitudes and how they develop, remain stable, and change over time. Attitude strength is conceptualized in terms of the connectivity of attitude networks and we show that this provides a parsimonious account of the differences between strong and weak attitudes. We discuss the CAN model in relation to possible extensions, implication for the assessment of attitudes, and possibilities for further study.