407 publications from this institution
This paper analyzes the theoretical, pragmatic, and substantive factors that have hampered the integration between psychology and psychometrics. Theoretical factors include the operationalist mode of thinking which is common throughout psychology, the dominance of classical test theory, and the use of "construct validity" as a catch-all category for a range of challenging psychometric problems. Pragmatic factors include the lack of interest in mathematically precise thinking in psychology, inadequate representation of psychometric modeling in major statistics programs, and insufficient mathematical training in the psychological curriculum. Substantive factors relate to the absence of psychological theories that are sufficiently strong to motivate the structure of psychometric models. Following the identification of these problems, a number of promising recent developments are discussed, and suggestions are made to further the integration of psychology and psychometrics.
Attitude strength is a key characteristic of attitudes. Strong attitudes are durable and impactful, while weak attitudes are fluctuating and inconsequential. Recently, the causal attitude network (CAN) model was proposed as a comprehensive measurement model of attitudes, which conceptualizes attitudes as networks of causally connected evaluative reactions (i.e., beliefs, feelings, and behavior toward an attitude object). Here, we test the central postulate of the CAN model that highly connected attitude networks correspond to strong attitudes. We use data from the American National Election Studies 1980–2012 on attitudes toward presidential candidates ( N = 18,795). We first show that political interest predicts connectivity of attitude networks toward presidential candidates. Second, we show that connectivity is strongly related to two defining features of strong attitudes—stability of the attitude and the attitude’s impact on behavior. We conclude that network theory provides a promising framework to advance the understanding of attitude strength.
Here we introduce a Special Section of Child Development entitled “Formalizing Theories of Child Development.” This Special Section features five papers that use mathematical models to advance our understanding of central questions in the study of child development. This ...
Network models like the Ising model are increasingly used in psychological research. In a recent article published in this journal, Brusco et al. (2019) provide a critical assessment of the conditions that underlie the Ising model and the eLasso method that is commonly used to estimate it. In this commentary, we show that their main criticisms are unfounded. First, where Brusco et al. (2019) suggest that Ising models have little to do with classical network models such as random graphs, we show that they can be fruitfully connected. Second, if one makes this connection it is immediately evident that Brusco et al.'s (2019) second criticism-that the Ising model requires complete population homogeneity and does not allow for individual differences in network structure-is incorrect. In particular, we establish that if every individual has their own topology, and these individual differences instantiate a random graph model, the Ising model will hold in the population. Hence, population homogeneity is sufficient for the Ising model, but it is not necessary, as Brusco et al. (2019) suggest. Third, we address Brusco et al.'s (2019) criticism regarding the sparsity assumption that is made in common uses of the Ising model. We show that this criticism is misdirected, as it targets a particular estimation algorithm for the Ising model rather than the model itself. We also describe various established and validated approaches for estimating the Ising model for networks that violate the sparsity assumption. Finally, we outline important avenues for future research. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
According to Oude Maatman (2020), our recent suggestion (Borsboom et al., 2019) that symptom networks are irreducible because they rely on folk psychological descriptions, threatens to undermine the main achievements of the network approach. In this article, we take up Oude Maatman’s challenge and develop an argument showing in what sense folk psychological concepts describe features of reality, and what it means to say that folk psychology is a causal language.
Study Objectives Major depressive disorder (MDD) is the leading cause of disability worldwide. Its high recurrence rate calls for prevention of first-onset MDD. Although meta-analysis suggested insomnia as the strongest modifiable risk factor, previous studies insufficiently addressed that insomnia might also occur as a residual symptom of unassessed prior depression, or as a comorbid complaint secondary to other depression risks. Methods In total, 768 participants from the Netherlands Study of Depression and Anxiety who were free from current and lifetime MDD were followed-up for four repeated assessments, spanning 6 years in total. We performed separate Cox proportional hazard analyses to evaluate whether baseline insomnia severity, short-sleep duration, and individual insomnia complaints prospectively predicted first-onset MDD during follow-up. The novel method of network outcome analysis (NOA) allowed us to sort out whether there is any direct predictive value of individual insomnia complaints among several other complaints that are associated with insomnia. Results Over 6-year follow-up, 141 (18.4%) were diagnosed with first-onset MDD. Insomnia severity but not sleep duration predicted first-onset MDD (HR = 1.11, 95% CI: 1.07–1.15), and this was driven solely by the insomnia complaint difficulty initiating sleep (DIS) (HR = 1.10, 95% CI: 1.04–1.16). NOA likewise identified DIS only to directly predict first-onset MDD, independent of four other associated depression complaints. Conclusions We showed prospectively that DIS is a risk factor for first-onset MDD. Among the different other insomnia symptoms, the specific treatment of DIS might be the most sensible target to combat the global burden of depression through prevention.
No abstract is provided for this article.
Conditional independence is a crucial concept in the network models discussed in this book, because the canonical pairwise Markov random field (PMRF) formulation of networks is defined in terms of this concept: nodes that are disconnected in the true PMRF are conditionally independent given the other nodes in the network. This chapter provides an introduction to the concept of conditional independence and illustrates its importance in reasoning about possible generative models. With the intuitive understanding of conditional independence provided in this chapter, readers will be able to grasp the essence of the estimation routines discussed in subsequent chapters.
No abstract is provided for this article.
The “Ising model” refers to both the statistical and the theoretical use of the same equation. In this article, we introduce both uses and contrast their differences. We accompany the conceptual introduction with a survey of Ising-related software packages in R. Since the model’s different uses are best understood through simulations, we make this process easily accessible with fully reproducible examples. Using simulations, we show how the theoretical Ising model captures local-alignment dynamics. Subsequently, we present it statistically as a likelihood function for estimating empirical network models from binary data. In this process, we give recommendations on when to use traditional frequentist estimators as well as novel Bayesian options.
Network theories have been put forward for psychopathology (in which mental disorders originate from causal relations between symptoms) and for personality (in which personality factors originate from coupled equilibria of cognitions, affect states, behaviours, and environments). Here, we connect these theoretical strands in an overarching personality–resilience–psychopathology model. In this model, factors in personality networks control the shape of the dynamical landscape in which symptom networks evolve; for example, the neuroticism item ‘I often feel blue’ measures a general tendency to experience negative affect, which is hypothesized to influence the threshold parameter of the symptom ‘depressed mood’ in the psychopathology network. Conversely, events at the level of the fast–evolving psychopathology network (e.g. a depressive episode) can influence the slow–evolving personality variables (e.g. by increasing feelings of worthlessness). We apply the theory to neuroticism and major depressive disorder. Through simulations, we show that the model can accommodate important phenomena, such as the strong relation between neuroticism and depression and individual differences in the change of neuroticism levels and development of depression over time. The results of the simulation are implemented in an online, interactive simulation tool. Implications for research into the relationship between personality and psychopathology are discussed. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology
