Beware Graphical Networks from Rating Scales without Concrete Referents

We think of latent variables as hidden causes for the correlations among observed measures and rely on factor analysis to reveal the underlying structure. In a previous post, I borrowed an alternative metaphor from the R package qgraph and produced the following correlation network. Instead of depression as a disease entity represented as a factor, this figure displays depression as a set of mutually reinforcing ratings located toward the bottom of the graph.

I selected the bifi dataset from the psych R package so that readers could reproduce the analysis and so that one could compare the factor structure and the correlation network. However, I was thinking in terms of actual behaviors and not agreement ratings for items from a personality inventory. This distinction was discussed in an earlier post introducing item response theory. The node “Mood Swings” should be measured by a series of concrete behaviors in actual situations. This is the goal of the patient outcome measurement and the call for context-aware measurement. Moreover, one sees the same focus on behaviors or symptoms in the work of Borsboom and his associates, including the author of the R package qgraph that generated the above graphical network.

In an excellent tutorial on network analysis of personality data in R, Sacha Epskamp and others present another example along with all the necessary R code. Correlations networks are produced with qgraph along with partial correlation and LASSO networks, the later with the help of the R package parcor. This paper (“State of the aRt personality research”) outlines all the steps to generate graphical models and interpret the indices that describe the network structure. This is not social network analysis for the nodes are variables and the links are different measures of relationship.

The data comes from a personality inventory with a list of 60 statements and a five-point agreement scale. The scoring key lists the six constructs, abbreviated HEXACO, and their associated items. The first in the list is Sincerity, one of the 24 nodes in the network maps, measured by the following three statements:

I understand that we share a common conceptual space embedded in our language in which the endorsement or rejection of these items might provide some information about self-presentation. Yet, I expect that someone who has never worked could answer the first question because it has nothing to do with actual experience. All that I am being asked is whether I view myself as the type of person depicted in the statement. Similarly, I can respond to the second statement even if I never laugh at anyone’s bad jokes. In fact, I would answer the same regardless of any propensity to laugh or not laugh at other’s jokes.

The HEXACO model of personality structure is but one of a number of different approaches based on the lexical hypothesis that personality gets coded in language. There is a meeting of the minds over the distinctions that are made and what it might mean to position ourselves at different locations within this landscape. In order to communicate with others, we must come to some agreement about the meanings of the statements used in personality inventories. It is the talk and not the behavior that is responsible for the factor structure or the positioning of nodes in the network.

Where are the feedback loops or mutually reinforcing nodes with such measures? It makes sense to talk about a network when the nodes are behaviors, as in the lower portion of our above network map. I get irritated, so I am more likely to get angry. In this agitated state I panic more easily and experience mood swings, all of which is makes me feel blue. You can download the 60-item self-report form and decide for yourself if the statements are linked by anything more than a shared conception and way of talking about personality traits.