This paper 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 ÒshortcutsÓ 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. Key words: Network models, attitudes, tripartite model, connectionism, small-world consistency , recent connectionist modeling of attitudes (Monroe & Read, 2008) and recent advancements in applying network theory in psychology (e.g., Cramer, Waldorp, van der Maas, & Borsboom, 2010; for excellent discussions of the relevance of network analysis to the social sciences in general and psychology in particular see Borgatti, Mehra, Brass, & Labianca, 2009; Westaby, Pfaff, & Redding, 2014) to derive a set of requirements for a CAUSAL ATTITUDE NETWORK MODEL 6 realistic formalized measurement model of attitudes. Third, based on these requirements we develop the CAN model and discuss the proposed small-world structure of attitudes that underlies it. Fourth, we discuss the CAN modelÕs perspective on attitude formation and structure, attitude stability and change, and attitude strength. Fifth, we discuss possible extensions of the CAN model, the modelÕs implications for the assessment of attitudes, and some possible avenues for further study of the CAN model.

Characterization of the global network topology and the position of individual nodes in that topology. Psychometric network analysisThe analysis of multivariate psychometric data using network structure estimation and network description.

Understanding the formation of prejudice, stereotypes, and discrimination has long been a core topic of social psychology. Since the seminal theorizing by Allport in 1954, different views on childhood origins of prejudice have been discussed, in which the role of parental socialization varies on a scale from fundamental to negligible. This meta-analysis integrates the available empirical evidence of the past 60 years and critically discusses the current state of knowledge on parental socialization of intergroup attitudes. A random-effects model analysis of data from 131 studies on over 45,000 parent-child dyads indicated a significant medium-sized average effect size for the correlation between parental and child intergroup attitudes. The average effect size was related to study-specific variables, such as the source of parental attitude report (self vs. child reported), the conceptual overlap between measures, and the privacy of assessment. We also found significant moderations by ingroup status and size as well as child age. The latter was, however, mediated by measurement overlap. No significant effect size differences were found in relation to different components of intergroup attitudes (i.e., affective, cognitive, behavioral), nor to child or parent gender. The results unequivocally demonstrate that parent-child attitudes are related throughout childhood and adolescence. We discuss in detail whether and to what extent this interrelation can be interpreted as an indicator of parent-child socialization to allow a critical evaluation of the available contradicting theories. We furthermore address limitations of the available research and the current meta-analysis and derive implications and suggestions for future research.

This article aims to improve theory formation in psychology by developing a practical methodology for constructing explanatory theories: theory construction methodology (TCM). TCM is a sequence of five steps. First, the theorist identifies a domain of empirical phenomena that becomes the target of explanation. Second, the theorist constructs a prototheory, a set of theoretical principles that putatively explain these phenomena. Third, the prototheory is used to construct a formal model, a set of model equations that encode explanatory principles. Fourth, the theorist investigates the explanatory adequacy of the model by formalizing its empirical phenomena and assessing whether it indeed reproduces these phenomena. Fifth, the theorist studies the overall adequacy of the theory by evaluating whether the identified phenomena are indeed reproduced faithfully and whether the explanatory principles are sufficiently parsimonious and substantively plausible. We explain TCM with an example taken from research on intelligence (the mutualism model of intelligence), in which key elements of the method have been successfully implemented. We discuss the place of TCM in the larger scheme of scientific research and propose an outline for a university curriculum that can systematically educate psychologists in the process of theory formation.

In this article, we provide a brief tutorial on the estimation, analysis, and simulation on attitude networks using the programming language R. We first discuss what a network is and subsequently show how one can estimate a regularized network on typical attitude data. For this, we use open-access data on the attitudes toward Barack Obama during the 2012 American presidential election. Second, we show how one can calculate standard network measures such as community structure, centrality, and connectivity on this estimated attitude network. Third, we show how one can simulate from an estimated attitude network to derive predictions from attitude networks. By this, we highlight that network theory provides a framework for both testing and developing formalized hypotheses on attitudes and related core social psychological constructs.

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.

This paper aims to improve theory formation in psychology by developing a practical methodology for constructing explanatory theories: Theory Construction Methodology (TCM). TCM is a sequence of five steps. First, the theorist identifies empirical phenomena to become the target of explanation. Second, the theorist constructs a proto-theory: a set of theoretical principles that potentially explain these phenomena. Third, the proto-theory is used to construct a formal model: a set of model equations or simulation models that encode the explanatory principles. Fourth, the theorist investigates this model’s explanatory adequacy. This is done by formalizing the empirical phenomena in terms of the model, and assessing whether the model indeed reproduces them. Fifth, the theorist studies the overall adequacy of the theory by evaluating whether phenomena are indeed reproduced faithfully, whether explanatory principles are parsimonious and substantively plausible, and whether the theory implies new predictions to promote further research. We illustrate TCM with an example taken from the intelligence literature (the mutualism model of intelligence), discuss the place of TCM in the larger scheme of scientific research, and propose an outline for a university curriculum that can systematically educate psychologists in the process of theory formation.

Network theory, as a theoretical and methodological framework, is energizing many research fields, among which clinical psychology and psychiatry. Fundamental to the network theory of psychopathology is the role of specific symptoms and their interactions. Current statistical tools, however, fail to fully capture this constitutional property. We propose community detection tools as a means to evaluate the complex network structure of psychopathology, free from its original boundaries of distinct disorders. Unique to this approach is that symptoms can belong to multiple communities. Using a large community sample and spanning a broad range of symptoms (Symptom Checklist-90-Revised), we identified 18 communities of interconnected symptoms. The differential role of symptoms within and between communities offers a framework to study the clinical concepts of comorbidity, heterogeneity and hallmark symptoms. Symptoms with many and strong connections within a community, defined as stabilizing symptoms, could be thought of as the core of a community, whereas symptoms that belong to multiple communities, defined as communicating symptoms, facilitate the communication between problem areas. We propose that defining symptoms on their stabilizing and/or communicating role within and across communities accelerates our understanding of these clinical phenomena, central to research and treatment of psychopathology.