Background The DSM uses one set of abuse and dependence criteria to assess multiple substance use disorders (SUDs). Most SUD research aggregates across these symptoms to study the behavior of SUD as a static construct. We use an alternative approach that conceptualizes symptoms as directly interacting variables in psychopathological networks. We apply network models to symptom-level data to investigate the unique roles of individual symptoms and their interactions in SUD. Methods We analyzed 11 DSM III-R/IV abuse and dependence criteria in a sample of 2405 adult twins who reported use of at least one illicit substance six or more times from the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders (VATSPSUD). We estimated a symptom network for each substance class as well as a global network collapsed across all substance classes. We examined similarities and differences across the 6 networks in terms of symptom-to-symptom connections and symptom centrality. Results The global network model revealed several interesting symptom connections, such as a strong predictive relation between tolerance and more-than-planned substance use. The most central symptom was using a drug more than planned. In addition, several interesting differences across substances emerged, both in the strength of symptom connections as well as the centrality of symptoms to each network. Conclusions When analyzed as networks, abuse and dependence symptoms do not function equivalently across illicit substance classes. These findings suggest the value of analyzing individual symptoms and their associations to gain new insight into the mechanisms of SUD.
This chapter surveys open questions about test score meaning drawing from a variety of literatures engaged with meaning and interpretation. William Jame's
Now in its second edition, this important book examines test validity in the behavioral, social, and educational sciences by exploring three fundamental problems: measurement, causation, and meaning. Psychometric and philosophical perspectives and unresolved issues receive attention, as the authors explore how measurement is conceived from both the classical and modern perspectives. Split into three accessible sections, the first contrasts theories of measurement as applied to the validity of behavioral science measures, and the second considers causal theories of measurement as well as alternative theories of causation. The final section explores the meaning and interpretation of test scores as they apply to test validity, offering a conceptual overview of the field and its current state. Each carefully revised chapter begins with an overview of key theories and literature, concludes with a list of suggested readings, and features boxes with real-life situations that connect theory to practice. Examples of specific issues include: How tests can assess an attribute without measuring it. The role of values in test validity. Interpreting responses to the same question in different languages. Researchers, practitioners, and policy makers interested in test validity or developing tests will appreciate the book's cutting-edge review of test validity. Focusing on both the underlying concepts, as well as practical challenges of test construction and use, it 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.
We address the commentaries on our target article in terms of four major themes. First, we note that virtually all commentators agree that mental disorders are not brain disorders in the common interpretation of these terms, and establish the consensus that explanatory reductionism is not a viable thesis. Second, we address criticisms to the effect that our article was misdirected or aimed at a straw man; we argue that this is unlikely, given the widespread communication of reductionist slogans in psychopathology research and society. Third, we tackle the question of whether intentionality, extended systems, and multiple realizability are as problematic as claimed in the target article, and we present a number of nuances and extensions with respect to our article. Fourth, we discuss the question of how the network approach should incorporate biological factors, given that wholesale reductionism is an unlikely option.
Mottus gives the impression that composites, as well as other models in which traits are a result rather than a cause of their indicators, require “emergent properties” to have causal power. We argue that this is not necessary; composites can be considered causally relevant by themselves when they mediate the relation between their constituents and the outcome variable.
Achilles and the tortoise want to predict peoples’ running performance from their age. In determining age the tortoise has cleverly asked his participants for their passports and has noted their date of birth, before letting them run half the distance between Achilles and himself. Achilles, however, has given his participants a questionnaire consisting of statements such as, feel old, don’t run as fast as I used to, and admit to wearing a toupee. Unsure about what to do with the data so gathered, they decide to solicit advice from methodological experts. After some inquiries, Achilles and the tortoise end up at Dr. Maraun’s office, where they find him playing language games with Dr. Halpin.
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.
No abstract is provided for this article.
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 (total 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.
No abstract is provided for this article.
