The Limited Utility of Network Models

This blog post is based on a commentary that was published in the European Journal of Personality Psychology in 2012. Republishing it as a blog post makes it openly accessible.

The Utility of Network Analysis for Personality Psychology
European Journal of Personality, 26: 446–447 (2012)
DOI: 10.1002/per.1876


We note that network analysis provides some new opportunities but also has some limitations: (i) network analysis relies on observed measures such as single items or scale scores; (ii) it is a descriptive method and, as such,
cannot test causal hypotheses; and (iii) it does not test the influence of outside forces on the network, such as dispositional influences on behaviour. We recommend structural equation modelling as a superior method that overcomes limitations of exploratory factor analysis and network analysis.


Cramer et al. (2012) introduce network analysis (NA) as a new statistical tool for the study of personality that addresses some limitations of exploratory factor analysis (EFA). We concur with the authors that NA provides valuable new opportunities but feel forced by the situational pressure of a 1000 word limit to focus on some potential limitations of

We also compare NA to structural equation modelling (SEM) because we agree with the authors that SEM is currently the most powerful statistical method for the testing of competing (causal) theories of personality.

One limitation of EFA and NA is that these methods rely on observed measures to examine relationships between personality constructs. For example, Cramer et al. (2012) apply NA to correlations among ratings of single items. The authors recognize this limitation but do not present an alternative to this suboptimal approach.

A major advantage of SEM is that it allows researchers to create measurement models that can remove random and systematic measurement error from observed measures of personality constructs. Measurement models of multimethod data are particularly helpful to separate perception and rater biases from actual personality traits
(e.g. Gere & Schimmack, 2011; Schimmack, 2010).

Our second concern is that NA is presented as a statistical tool that can test dynamic process models of personality. Yet, NA is a descriptive method that provides graphical representations of patterns in correlation matrices. Thus, NA is akin to other descriptive methods (e.g. multidimensional scaling, cluster analysis and principal component analysis) that reveal patterns in complex data. These descriptive methods make no assumptions about causality. In contrast, SEM forces researchers to make a priori assumptions about causal processes and provides information about the ability of a causal theory to explain the observed pattern of correlations. Thus, we recommend SEM for theory testing and do not think it is appropriate to use NA for this purpose.

Specifically, we think it is questionable to make inferences about the Big Five model based on network graphs. Cramer et al. (2012) highlight the ability to visualize the centrality of items in a network as a major strength of NA. However, factor loading patterns and communalities in EFA provide similar information. In our opinion, the authors go beyond the statistical method of NA when they propose that activation of central components will increase the chances that neighbouring components will also become
more activated. This assumption is problematic for several reasons.

First, it is not clear what the authors mean by the notion of activation of personality components. Second, the connections in a network graph are not causal paths. An item could be central because it is influenced by many personality components (e.g. life satisfaction is influenced by neuroticism,
extraversion, agreeableness and conscientiousness) or because it is the cause of neighbouring items (life satisfaction influences neuroticism, extraversion, agreeableness and conscientiousness). Researchers interested in testing causal relationships should collect data that are informative about causality (e.g. twin data) and use SEM to test whether the
data favour one causal theory over another.

We are also concerned about the suggestion of Cramer et al. (2012) that NA provides an alternative account of classic personality constructs such as extraversion and neuroticism. It is important to make clear that this alternative view challenges the core assumption of many personality
theories that behaviour is influenced by personality dispositions.

That is, whereas the conception of neuroticism as a personality trait assumes that neuroticism has causal force (Funder, 1991), the conceptualization of neuroticism as a personality component implies that it does not have causal force. The authors compare personality constructs such as neuroticism with the concept of a flock. The term flock in the expression a flock of birds does not refer to an independent entity that exists apart from the individual birds, and it makes no sense to attribute the gathering of birds to the causal effect of flocking (the birds are gathered in the same place because they are a flock of birds). We prefer to compare neuroticism with the causal force of seasonal changes that make individual birds flock together.


Since we published this commentary, network models have become even more popular to make claims about important constructs like depression and other constructs. So far, we have only seen pretty pictures of item clusters, but no evidence that network models provide new insights into the causes of depression or dynamic developments over time. The reason is that the statistical tool is merely descriptive, whereas the articles talk a lot about things that go well beyond the empirical contribution of plotting correlations or partial correlations. In this regard, network articles remind me of the old days in personality psychology, where researchers told stories about their principle components. Instead researchers interested in individual differences should learn how to use structural equation modeling to test causality and to study stability and change of personality traits and states. Unfortunately, learning structural equation modeling is a bit more difficult than network analysis which requires no theory and does not test model fit. Maybe that is the reason for the popularity of network models. Easy to do and pretty pictures. Who can resist.

Ulrich Schimmack, March 1, 2019

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