Jonas Dalege

The Apple Does Not Fall Far From the Tree, or Does It? A Meta-Analysis of Parent-Child Similarity in Intergroup Attitudes

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.

Network Analysis on Attitudes : A Brief Tutorial

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.

Network Structure Explains the Impact of Attitudes on Voting Decisions

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.

The role of stabilizing and communicating symptoms given overlapping communities in psychopathology networks

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.

A Network Perspective on Attitude Strength: Testing the Connectivity Hypothesis

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.