Our paper on predicting voting decisions using network analysis was published in Scientific Reports (PDF) and our tutorial on analyzing and simulating attitude networks was published in Social Psychological and Personality Science (PDF).
In the first paper, we show that whether attitudes toward presidential candidates predict voting decisions depends on the connectivity of the attitude network. If the attitude network is strongly connected, attitudes almost perfectly predict the voting decision. Less connected attitude networks are less predictive of voting decisions. Additionally, we show that the most central attitude elements have the highest predictive value.
In the second paper, we provide a state-of-the-art tutorial on how to estimate (cross-sectional) attitude networks and how to compute common network descriptives on estimated attitude networks. We also show how one can simulate from an estimated attitude network to derive formalized hypotheses regarding attitude dynamics and attitude change.
Our paper on the Causal Attitude Network (CAN) model was published in Psychological Review (PDF).
In the paper, we introduce the CAN model, which conceptualizes attitudes as networks consisting of interacting evaluative reactions, such as beliefs (e.g., judging a presidential candidate as competent and charismatic), feelings (e.g., feeling proudness and hope about the candidate), and behaviors (e.g., voting for the candidate). Interactions arise through direct causal connections between the evaluative reactions (e.g., feeling hopeful about the candidate because one judges her as competent and charismatic). The CAN model assumes that causal connections between evaluative reactions serve to heighten the consistency of the attitude and we argue that the Ising model’s axiom of energy expenditure reduction represents a formalized account of consistency pressure. Because individuals not only strive for consistency but also for accuracy, network representations of attitudes have to deal with the tradeoff between consistency and accuracy. This tradeoff is likely to lead to a small-world structure and we show that attitude networks indeed have a small-world structure. We also discuss the CAN model’s implication for attitude change and stability. Furthermore, we show that connectivity of attitude networks provides a formalized and parsimonious account of the dynamical differences between strong and weak attitudes.
Dalege, J., Borsboom, D., van Harreveld, F., van den Berg, H., Conner, M., & van der Maas, H. L. J. (2015). Toward a formalized account of attitudes: The Causal Attitude Network (CAN) model. Psychological Review. Advance online publication.