EGAnet-package: EGAnet-package

Description Author(s) References

Description

Implements the Exploratory Graph Analysis (EGA; Golino & Epskamp, 2017; Golino, Shi, et al., 2020) framework for dimensionality and psychometric assessment. EGA is part of a new area called network psychometrics that uses undirected network models for the assessment of psychometric properties. EGA estimates the number of dimensions (or factors) using graphical lasso EBICglasso or Triangulated Maximally Filtered Graph (TMFG) and a weighted network community detection algorithm (Christensen & Golino, under review). A bootstrap method for verifying the stability of the dimensions and items in those dimensions is available (bootEGA; Christensen & Golino, 2019). The fit of the structure suggested by EGA can be verified using Entropy Fit Indices (entropyFit, tefi; Golino, Moulder, et al., 2020). A novel approach called Unique Variable Analysis (UVA) can be used to identify and reduce redundant variables in multivariate data (Christensen, Garrido, & Golino, under review). Network loadings (net.loads), which are roughly equivalent to factor loadings when the data generating model is a factor model, are available (Christensen & Golino, 2021). Network scores (net.scores) can also be computed using the network loadings. Finally, dynamic EGA (dynEGA) will estimate dimensions from time series data for individual, group, and sample levels (Golino, Christensen, et al., under review).

Author(s)

Hudson Golino <hfg9s@virginia.edu> and Alexander P. Christensen <alexpaulchristensen@gmail.com>

References

Christensen, A. P., Garrido, L. E., & Golino, H. (under review). Unique Variable Analysis: A novel approach to detect redundant variables in multivariate data. PsyArXiv. doi: 10.31234/osf.io/4kra2
# Related functions: UVA

Christensen, A. P., & Golino, H. (under review). Estimating factors with psychometric networks: A Monte Carlo simulation comparing community detection algorithms. PsyArXiv. doi: 10.31234/osf.io/hz89e
# Related functions: EGA

Christensen, A. P., & Golino, H. (2019). Estimating the stability of the number of factors via Bootstrap Exploratory Graph Analysis: A tutorial. PsyArXiv. doi: 10.31234/osf.io/9deay
# Related functions: bootEGA

Christensen, A. P., & Golino, H. (2021). On the equivalency of factor and network loadings. Behavior Research Methods. doi: 10.3758/s13428-020-01500-6
# Related functions: LCT and net.loads

Christensen, A. P., & Golino, H. (under review). Random, factor, or network model? Predictions from neural networks. PsyArXiv. doi: 10.31234/osf.io/awkcb
# Related functions: LCT

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. doi: 10.1002/per.2265
# Related functions: bootEGA, dimStability, # EGA, itemStability, and UVA

Golino, H., Christensen, A. P., Moulder, R. G., Kim, S., & Boker, S. M. (under review). 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. PsyArXiv. doi: 10.31234/osf.io/tfs7c
# Related functions: dynEGA and simDFM

Golino, H., & Demetriou, A. (2017). Estimating the dimensionality of intelligence like data using Exploratory Graph Analysis. Intelligence, 62, 54-70. doi: 10.1016/j.intell.2017.02.007
# Related functions: EGA

Golino, H., & Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLoS ONE, 12, e0174035. doi: 10.1371/journal.pone.0174035
# Related functions: EGA

Golino, H., Moulder, R. G., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Nesselroade, J., Sadana, R., Thiyagarajan, J. A., & Boker, S. M. (2020). Entropy fit indices: New fit measures for assessing the structure and dimensionality of multiple latent variables. Multivariate Behavioral Research. doi: 10.31234/osf.io/mtka2
# Related functions: entropyFit, tefi, and vn.entropy

Golino, H., Shi, D., Christensen, A. P., Garrido, L. E., Nieto, M. D., Sadana, R., Thiyagarajan, J. A., & Martinez-Molina, A. (2020). Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial. Psychological Methods, 25, 292-320. doi: 10.1037/met0000255
# Related functions: EGA

Golino, H., Thiyagarajan, J. A., Sadana, M., Teles, M., Christensen, A. P., & Boker, S. M. (under review). Investigating the broad domains of intrinsic capacity, functional ability, and environment: An exploratory graph analysis approach for improving analytical methodologies for measuring healthy aging. PsyArXiv. doi: 10.31234/osf.io/hj5mc
# Related functions: EGA.fit and tefi


EGAnet documentation built on Feb. 17, 2021, 1:06 a.m.