In the last half of the 20th century, psychology and neuroscience have experienced a renewed interest in intraindividual variation. To date, there are few quantitative methods to evaluate whether a population structure (between-person) is likely to hold for individual people. We present a network information theoretic approach to evaluate the extent to which a system possesses the ergodic property. We introduce a new metric, the ergodicity information index (EII), that can inform whether a set of multivariate time series (or a set of intensive longitudinal measures) should be represented as multiple individual structures or as a single population structure. The EII index quantifies the amount of information lost by representing all individuals with a single population structure. If the individuals don't have a similar structure, representing them with a single population network leads to a loss of information. The EII index value will be higher than in cases where all individuals have a similar structure. A Monte-Carlo simulation is implemented to test the EII index, and the results show that the new index has a 94% accuracy in differentiating data in which all individuals have a similar structure vs. data in which the individuals don't have a similar structure. The paper also presents two new techniques designed to help applied researchers to analyze data when the ergodicity property does not hold. The EII bootstrap test obtains a sampling distribution of EII values as if all participants in the data have the population network structure and compare this null distribution to the empirical EII value. Significant differences indicate that the empirical data cannot be expected to be generated from an ergodic process, and the population structure is not sufficient to describe all individuals. The Domenico clustering method estimates the Von Neumann entropy of two networks and computes their Jensen-Shannon Distance (JSD). Then, a complete-linkage agglomerative hierarchical clustering method is applied to the JSD, and the clustering partitioning is obtained via modularization maximization. The Domenico clustering method allows the discovery of groups of individuals with a similar structure. Finally, two empirical examples are shown, one using data from an intensive longitudinal experience sampling study examining Big Five personality measured by the Big Five Inventory-2, and the other using resting state neuroimaging data taken from a study examining creativity which used the 268-node Shen brain atlas. Starting with personality, the bootstrap EII test was significant, suggesting that the BFI-2 data were nonergodic. Following up on the bootstrap EII test, the information clustering was applied, and the single cluster test was performed. The single cluster test suggested that the empirical networks had significantly greater JSD values than the random networks meaning that the single cluster detected was not meaningful, and each individual in the sample is unique. The brain networks had a significant bootstrap EII test and significantly larger JSD values than random networks. These results, in line with the personality data, suggest that (resting state) brain networks are not ergodic, and no meaningful groups can be formed (i.e., each individual is unique). In sum, the personality and (resting state) brain networks do not possess ergodicity and therefore lose information when data are aggregated into a single population.