# Attitudes Modeled as Networks

**Engaging Market Research**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

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In case you missed it, Jonas Dalege and his colleagues at the PsychoSystems research group have recently published an article in Psychological Review detailing how attitudes can be represented as network graphs. It is all done using R and a dataset that can be downloaded by registering at the ANES data center. You will find the R code under Scripts and Code in a file called ANES 1984 Analyses. With very minor changes to the size of some labeling, I was able to reproduce the above undirected graph with two R packages: IsingFit and qgraph. As usual when downloading others’ files, most of the R code is data munging and deals with assigning labels and transforming ratings into dichotomies.

The above graph represents the conditional independence relationships among node pairings. Specifically, edges are drawn between pairs of nodes only if they are still related after controlling for all the other nodes not in that pair. The center nodes in red are assessments of Ronald Reagan’s ability, decency and caring. The groupings of the red nodes seem reasonable, for example, the thicker green edges connected knowledgeable, hard-working, decent and moral. Similarly, in touch, understands and cares are also drawn together by stronger relationships. These evaluative judgments are joined by positive green edges to the respondents’ feelings of pride and hope (blue nodes). Moreover, they are pushed away by negative red pathways from darker emotional reactions such as fear, anger and disgust (green nodes).

One should not be surprised to learn that it makes a difference whether the attitudes are scored dichotomously (e.g., yes/no, agree/disagree or present/absent) or using some ordinal rating scale. If it helps, you can think of this as you might the distinction between regression (continuous) and classification (discrete) in statistical learning theory. Thus, when I analyzed a set of mobile phone ratings gathered with 10-point scales, I borrowed a graphical lasso model called EBICglasso from the qgraph R package (see Undirected Graphs When the Causality is Mutual). On the other hand, the Ising model from the IsingFit R package was needed when the data came from yes/no checklists (see The Network Underlying Consumer Perceptions of the European Car Market).

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