Figure 5. An overview of network methodology, with a focus on the relationship between causal systems, data, and the empirical networks most commonly used in the network approach literature (Pairwise Markov Random Fields).

In many areas of network science, both the elements of the network and the connections among them can be directly observed (e.g., train stations and the tracks that connect them). In psychiatry, symptoms can be assessed, but the relationships among them must be inferred. Network psychometrics aims to infer those relationships using statistical associations. The method by which this is done depends on the data collected (for a discussion of Cattell’s data cube and its relation to specific analyses, see Wardenaar and de Jonge, 2013). For cross-sectional data, a single network is estimated based on the covariation of symptoms between-persons at that point in time. For n=1 time-series data, networks are estimated based on the covariation of symptoms over time within one individual, and can be used to inform contemporaneous and temporal (lagged) associations among symptoms. In time series data in larger samples, networks can be estimated using both within- and between-person information. Importantly, the network structure derived from between-person analyses and within-person analyses are unlikely to be equivalent and, for many plausible causal systems, it remains unclear how the structured derived from either analysis corresponds to the “true structure” of the causal system. The relationships among between-person networks, within-person networks, and the “true structure” of different types of causal systems are critical directions for future research.