net.scores {EGAnet}R Documentation

Network Scores


This function computes network scores computed based on each node's strength within each community (i.e., factor) in the network (see net.loads). These values are used as network "factor loadings" for the weights of each item. Notably, network analysis allows nodes to contribution to more than one community. These loadings are considered in the network scores. In addition, if the construct is a hierarchy (e.g., personality questionnaire; items in facet scales in a trait domain), then an overall score can be computed (see argument global). An important difference is that the network scores account for cross-loadings in their estimation of scores


net.scores(data, A, wc, global = FALSE, impute, ...)



Matrix or data frame. Must be a dataset


Matrix, data frame, or EGA object. An adjacency matrix of network data


Numeric. A vector of community assignments. Not necessary if an EGA object is input for argument A


Boolean. Should general network loadings be computed in scores? Defaults to FALSE. If there is more than one dimension and there is theoretically one global dimension, then general loadings of the dimensions onto the global dimension can be included in the weighted scores


Character. In the presence of missing data, imputation can be implemented. Currently, three options are available:

  • none No imputation is performed. This is the default.

  • mean The "mean" value of the columns are used to replace the missing data.

  • median The "median" value of the columns are used to replace the missing data.


Additional arguments for EGA


For more details, type vignette("Network_Scores")


Returns a list containing:


The unstandardized network scores for each participant and community (including the overall score)


The standardized network scores for each participant and community (including the overall score)


Partial correlations between the specified or identified communities


Standardized network loadings for each item in each dimension (computed using net.loads)


Alexander P. Christensen <> and Hudson F. Golino <hfg9s at>


Christensen, A. P., & Golino, H. (2021). On the equivalency of factor and network loadings. Behavior Research Methods, 53, 1563-1580.

Christensen, A. P., Golino, H., & Silvia, P. J. (2020). A psychometric network perspective on the validity and validation of personality trait questionnaires. European Journal of Personality, 34, 1095-1108.

Golino, H., Christensen, A. P., Moulder, R., Kim, S., & Boker, S. M. (2021). Modeling latent topics in social media using Dynamic Exploratory Graph Analysis: The case of the right-wing and left-wing trolls in the 2016 US elections. Psychometrika.


# Load data
wmt <- wmt2[,7:24]

## Not run: 
 # Estimate EGA
 ega.wmt <- EGA(wmt)

## End(Not run)

# Network scores
net.scores(data = wmt, A = ega.wmt)

[Package EGAnet version 1.1.0 Index]