Political belief systems lay at the heart of multiple disciplines including social psychology, political science, and sociology. They can encourage or obstruct social movements and social changes as they reflect people’s social circumstances and provide people with a lens to view the world (Duckitt & Sibley, 2010; Jost, Nosek, & Gosling, 2008; Kahan, Peters, Dawson, & Slovic, 2017). Although different disciplines, and even scholars within the same discipline, have different definitions for political belief systems and related terms (e.g., ideology, worldview), they tend to converge on the idea that belief systems are the interrelationships of attitudes and beliefs relevant to politics (Gerring, 1997). In this article, we integrate research that models psychological phenomena as networks with the idea that belief systems are defined by the interrelationships of attitudes and beliefs to understand what is central to political belief systems. Identifying the central components of political belief systems is important because it informs us about how people reason about political issues (Bartels, 2002; Hatemi & McDermott, 2016; Kahan et al., 2017) and the components most likely to affect their political decisions (Campbell, Converse, Miller, & Stokes, 1960; Converse, 1964, 2000; Ellis & Stimson, 2012; Kinder & Kalmoe, 2017). A deeper understanding of the central feature (or features) of political belief systems would also inform people’s reactions to political events (Huddy, Mason, & Aarøe, 2015), and their positions on new policy proposals (Cohen, 2003; Malka & Lelkes, 2010).

Belief Systems as Networks

One solution to this challenge is to directly model the interrelationships of attitudes and beliefs relevant to politics (i.e., the political belief system) as a network of interacting nodes (see Boutyline & Vaisey, 2017 for the first use of this general approach). We treat each node as a measure of an operational or symbolic component of the belief system. The belief system then consists of all of the relevant nodes and their connections with one another. This approach integrates work distinguishing symbolic and operational components of belief systems (e.g., Ellis & Stimson, 2012; Sears, 1993) with research modeling psychopathologies (Borsboom & Cramer, 2013), personality (Cramer et al., 2012), and single attitudes (Dalege et al., 2015) as networks. It has several distinct advantages.

First, a network approach successfully operationalizes the definition of political belief systems. By modeling belief systems as a network, we can explicitly model the interrelationships between the attitudes and beliefs relevant to politics.

Second, the network approach easily accommodates many belief system components within the analysis. Specifically, multiple operational and symbolic beliefs, as well as their interrelationships, can be modeled simultaneously. This allows for the possible recognition of multiple central and peripheral constructs, as well as constructs that might be somewhere in the middle of the central–peripheral continuum.

Third, by modeling belief systems as networks, we can adopt measures of network centrality from network science that are used to assess centrality within the structure of a broad array of systems (Barrat, Barthelemy, Pastor-Satorras, & Vespignani, 2004; Borgatti, 2005; Newman, 2010; Opsahl, Agneessens, & Skvoretz, 2010). Because belief systems are interrelationships between attitudes and beliefs, measures of centrality should take into account where a component is within that web of interrelationships. Centrality indicators from network science do just that, by identifying how a particular node is embedded within the network.

We focus on three centrality indicators that have been used in past work on psychological networks (e.g., Boutyline & Vaisey, 2017; Costantini, Epskamp, et al., 2015; cf. Freeman, 1978): strength, closeness, and betweenness centrality. Strength centrality is the sum of the absolute value of the edge weights that directly connect to a node (Barrat et al., 2004; Newman, 2010), and is an indicator of the immediate connections and potential influences a belief system component has on its neighbors. The other two measures of centrality are related to the position of the node within the overall structure of the belief system network. Closeness centrality is the inverse of the sum of the distance between a node and all other nodes (Borgatti, 2005), thereby representing how “quickly” the influence of a particular component can get from one component of the belief system to the rest of the components in the system. Betweenness centrality is the number of shortest paths that pass through a node between two other nodes (Borgatti, 2005). Higher betweenness thus captures how necessary the belief system component is for linking together the other parts of the belief system.

Although strength centrality may be an important property of belief system networks, closeness and betweenness centrality are theoretically most closely related to belief system scholars’ understanding of centrality. Whereas strength centrality taps into the embeddedness of a belief system component in its particular region of the belief system, closeness and betweenness centrality assess how well a component can tie components from disparate regions of the belief system together and influence the network as a whole. This is consistent with Converse’s (1964) description of the centrality of belief system components as “the role that they play in the belief system as a whole” (p. 208, emphasis added). For example, a component with high betweenness can serve as a bridge between different regions of the belief system, thereby helping to form a single belief system rather than multiple nonoverlapping belief systems. This is different from other descriptions of centrality, including those provided by Converse, that identify central variables as being more causally potent. Causal potency does not necessarily say anything about centrality, as the causally potent variable may be on the periphery of the belief system. Only by modeling the whole system as a network are we able to locate components within the broader belief system.