EGAnet (version 1.2.3)

net.scores: Network Scores

Description

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

Usage

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

Value

Returns a list containing:

unstd.scores

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

std.scores

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

commCor

Partial correlations between the specified or identified communities

loads

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

Arguments

data

Matrix or data frame. Must be a dataset

A

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

wc

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

global

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

impute

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

Author

Alexander P. Christensen <[e-mail]> and Hudson F. Golino <hfg9s at virginia.edu>

Details

For more details, type vignette("Network_Scores")

References

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.

Examples

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

if (FALSE) {
# Estimate EGA
ega.wmt <- EGA(
  data = wmt,
  plot.EGA = FALSE # No plot for CRAN checks
)}

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

if (FALSE) {
# Produce Methods section
methods.section(
  ega.wmt,
  stats = "net.scores"
)}