407 publications from this institution
This article introduces the Attitudinal Entropy (AE) framework, which builds on the Causal Attitude Network model that conceptualizes attitudes as Ising networks. The AE framework rests on three propositions. First, attitude inconsistency and instability are two related indications of attitudinal entropy, a measure of randomness derived from thermodynamics. Second, energy of attitude configurations serves as a local processing strategy to reduce the global entropy of attitude networks. Third, directing attention to and thinking about attitude objects reduces attitudinal entropy. We first discuss several determinants of attitudinal entropy reduction and show that several findings in the attitude literature, such as the mere thought effect on attitude polarization and the effects of heuristic versus systematic processing of arguments, follow from the AE framework. Second, we discuss the AE framework's implications for ambivalence and cognitive dissonance.
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.
Background Little is known about the specific factors that contribute to the well-being (WB) of individuals with autism spectrum disorder (ASD). A plausible hypothesis is that ASD symptomatology has a direct negative effect on WB. In the current study, the emerging tools of network analysis allow to explore the functional interdependencies between specific symptoms of ASD and domains of WB in a multivariate framework. We illustrate how studying both higher-order (total score) and lower-order (subscale) representations of ASD symptomatology can clarify the interrelations of factors relevant for domains of WB. Methods We estimated network structures on three different construct levels for ASD symptomatology, as assessed with the Adult Social Behavior Questionnaire (item, subscale, total score), relating them to daily functioning (DF) and subjective WB in 323 adult individuals with clinically identified ASD (aged 17–70 years). For these networks, we assessed the importance of specific factors in the network structure. Results When focusing on the highest representation level of ASD symptomatology (i.e. a total score), we found a negative connection between ASD symptom severity and domains of WB. However, zooming in on lower representation levels of ASD symptomatology revealed that this connection was mainly funnelled by ASD symptoms related to insistence on sameness and experiencing reduced contact and that those symptom scales, in turn, impact different domains of WB. Conclusions Zooming in across construct levels of ASD symptom severity into subscales of ASD symptoms can provide us with important insights into how specific domains of ASD symptoms relate to specific domains of DF and WB.
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.
Katherine L. Musliner, PhD; Trine Munk-Olsen, PhD; Thomas M. Laursen, PhD; William W. Eaton, PhD; Peter P. Zandi, PhD; Preben B. Mortensen, DrMedSc
Attitudes can have a profound impact on socially relevant behaviours, such as voting. However, this effect is not uniform across situations or individuals, and it is at present difficult to predict whether attitudes will predict behaviour in any given circumstance. Using a network model, we demonstrate that (a) more strongly connected attitude networks have a stronger impact on behaviour, and (b) within any given attitude network, the most central attitude elements have the strongest impact. We test these hypotheses using data on voting and attitudes toward presidential candidates in the US presidential elections from 1980 to 2012. These analyses confirm that the predictive value of attitude networks depends almost entirely on their level of connectivity, with more central attitude elements having stronger impact. The impact of attitudes on voting behaviour can thus be reliably determined before elections take place by using network analyses.
Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for their computational intractability, we present a computationally efficient model for estimating network structures. The approach, which is based on Ising models as used in physics, combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network. A validation study shows that this method succeeds in revealing the most relevant features of a network for realistic sample sizes. We apply our proposed method to estimate the network of depression and anxiety symptoms from symptom scores of 1108 subjects. Possible extensions of the model are discussed.
This article reflects on the papers included in the Special Issue on The Network Approach and the Future of Clinical Psychology (Roefs, Jansen, & Fried, 2022). The emergence of major empirical research programs into symptom networks is discussed in relation to the research agenda involving the system dynamics of mental disorders. This research agenda is then related to major themes addressed in the special issue: the integration of empirical results into a larger theoretical framework, the role of mental representations and neurobiological factors, the problem of measurement error, and the presence of heterogeneity. Taken together, the generation of large scale research programs, the availability of new sources of data, and the availability of network approaches creates momentum that may allow us to take crucial steps forward in assessing, modeling, and intervening on mental disorders.
Background Mental disorders are highly comorbid: people having one disorder are likely to have another as well. We explain empirical comorbidity patterns based on a network model of psychiatric symptoms, derived from an analysis of symptom overlap in the Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV). Principal Findings We show that a) half of the symptoms in the DSM-IV network are connected, b) the architecture of these connections conforms to a small world structure, featuring a high degree of clustering but a short average path length, and c) distances between disorders in this structure predict empirical comorbidity rates. Network simulations of Major Depressive Episode and Generalized Anxiety Disorder show that the model faithfully reproduces empirical population statistics for these disorders. Conclusions In the network model, mental disorders are inherently complex. This explains the limited successes of genetic, neuroscientific, and etiological approaches to unravel their causes. We outline a psychosystems approach to investigate the structure and dynamics of mental disorders.
In recent years, network analysis has been applied to identify and analyse patterns of statistical association in multivariate psychological data. In these approaches, network nodes represent variables in a data set, and edges represent pairwise conditional associations between variables in the data, while conditioning on the remaining variables. This Primer provides an anatomy of these techniques, describes the current state of the art and discusses open problems. We identify relevant data structures in which network analysis may be applied: cross-sectional data, repeated measures and intensive longitudinal data. We then discuss the estimation of network structures in each of these cases, as well as assessment techniques to evaluate network robustness and replicability. Successful applications of the technique in different research areas are highlighted. Finally, we discuss limitations and challenges for future research. Network analysis allows the investigation of complex patterns and relationships by examining nodes and the edges connecting them. Borsboom et al. discuss the adoption of network analysis in psychological research.
Background It has been suggested that the structure of psychopathology is best described as a complex network of components that interact in dynamic ways. The goal of the present paper was to examine the concept of psychopathology from a network perspective, combining complementary top-down and bottom-up approaches using momentary assessment techniques. Method A pooled Experience Sampling Method (ESM) dataset of three groups (individuals with a diagnosis of depression, psychotic disorder or no diagnosis) was used (pooled N = 599). The top-down approach explored the network structure of mental states across different diagnostic categories. For this purpose, networks of five momentary mental states (‘cheerful’, ‘content’, ‘down’, ‘insecure’ and ‘suspicious’) were compared between the three groups. The complementary bottom-up approach used principal component analysis to explore whether empirically derived network structures yield meaningful higher order clusters. Results Individuals with a clinical diagnosis had more strongly connected moment-to-moment network structures, especially the depressed group. This group also showed more interconnections specifically between positive and negative mental states than the psychotic group. In the bottom-up approach, all possible connections between mental states were clustered into seven main components that together captured the main characteristics of the network dynamics. Conclusions Our combination of (i) comparing network structure of mental states across three diagnostically different groups and (ii) searching for trans-diagnostic network components across all pooled individuals showed that these two approaches yield different, complementary perspectives in the field of psychopathology. The network paradigm therefore may be useful to map transdiagnostic processes.
