EGAnet-package: EGAnet-package
In EGAnet: Exploratory Graph Analysis – a Framework for Estimating the Number of Dimensions in Multivariate Data using Network Psychometrics
EGAnet-package R Documentation
EGAnet-package
Description
Implements the Exploratory Graph Analysis (EGA) framework for dimensionality
and psychometric assessment. EGA estimates the number of dimensions in
psychological data using network estimation methods and community detection
algorithms. A bootstrap method is provided to assess the stability of dimensions
and items. Fit is evaluated using the Entropy Fit family of indices. Unique
Variable Analysis evaluates the extent to which items are locally dependent (or
redundant). Network loadings provide similar information to factor loadings and
can be used to compute network scores. A bootstrap and permutation approach are
available to assess configural and metric invariance. Hierarchical structures
can be detected using Hierarchical EGA. Time series and intensive longitudinal
data can be analyzed using Dynamic EGA, supporting individual, group, and
population level assessments.
Author(s)
Hudson Golino <hfg9s@virginia.edu> and Alexander P. Christensen <alexpaulchristensen@gmail.com>
References
Christensen, A. P. (2023).
Unidimensional community detection: A Monte Carlo simulation, grid search, and comparison.
PsyArXiv.
# Related functions: community.unidimensional
Christensen, A. P., Garrido, L. E., & Golino, H. (2023).
Unique variable analysis: A network psychometrics method to detect local dependence.
Multivariate Behavioral Research.
# Related functions: UVA
Christensen, A. P., Garrido, L. E., Guerra-Pena, K., & Golino, H. (2023).
Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation.
Behavior Research Methods.
# Related functions: EGA
Christensen, A. P., & Golino, H. (2021a).
Estimating the stability of the number of factors via Bootstrap Exploratory Graph Analysis: A tutorial.
Psych, 3(3), 479-500.
# Related functions: bootEGA, dimensionStability,
# and itemStability
Christensen, A. P., & Golino, H. (2021b).
Factor or network model? Predictions from neural networks.
Journal of Behavioral Data Science, 1(1), 85-126.
# Related functions: LCT
Christensen, A. P., & Golino, H. (2021c).
On the equivalency of factor and network loadings.
Behavior Research Methods, 53, 1563-1580.
# Related functions: LCT and net.loads
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.
# Related functions: bootEGA, dimensionStability,
# EGA, itemStability, and UVA
Christensen, A. P., Gross, G. M., Golino, H., Silvia, P. J., & Kwapil, T. R. (2019).
Exploratory graph analysis of the Multidimensional Schizotypy Scale.
Schizophrenia Research, 206, 43-51.
# Related functions: CFA and EGA
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.
# 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.
# 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.
# Related functions: CFA, EGA, and bootEGA
Golino, H., Moulder, R., 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.
# Related functions: entropyFit, tefi, and vn.entropy
Golino, H., Nesselroade, J. R., & Christensen, A. P. (2022).
Towards a psychology of individuals: The ergodicity information index and a bottom-up approach for finding generalizations.
PsyArXiv.
# Related functions: boot.ergoInfo, ergoInfo,
jsd, and infoCluster
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.
# Related functions: EGA
Golino, H., Thiyagarajan, J. A., Sadana, M., Teles, M., Christensen, A. P., & Boker, S. M. (2020).
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.
# Related functions: EGA.fit and tefi
Jamison, L., Christensen, A. P., & Golino, H. (2021).
Optimizing Walktrap's community detection in networks using the Total Entropy Fit Index.
PsyArXiv.
# Related functions: EGA.fit and tefi
Jamison, L., Golino, H., & Christensen, A. P. (2023).
Metric invariance in exploratory graph analysis via permutation testing.
PsyArXiv.
# Related functions: invariance
Shi, D., Christensen, A. P., Day, E., Golino, H., & Garrido, L. E. (2023).
A Bayesian approach for dimensionality assessment in psychological networks.
PsyArXiv
# Related functions: EGA
See Also
Useful links:
-
Report bugs at https://github.com/hfgolino/EGAnet/issues
EGAnet documentation built on Nov. 18, 2023, 1:07 a.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
| EGAnet-package | R Documentation |
EGAnet-package
Description
Implements the Exploratory Graph Analysis (EGA) framework for dimensionality and psychometric assessment. EGA estimates the number of dimensions in psychological data using network estimation methods and community detection algorithms. A bootstrap method is provided to assess the stability of dimensions and items. Fit is evaluated using the Entropy Fit family of indices. Unique Variable Analysis evaluates the extent to which items are locally dependent (or redundant). Network loadings provide similar information to factor loadings and can be used to compute network scores. A bootstrap and permutation approach are available to assess configural and metric invariance. Hierarchical structures can be detected using Hierarchical EGA. Time series and intensive longitudinal data can be analyzed using Dynamic EGA, supporting individual, group, and population level assessments.
Author(s)
Hudson Golino <hfg9s@virginia.edu> and Alexander P. Christensen <alexpaulchristensen@gmail.com>
References
Christensen, A. P. (2023).
Unidimensional community detection: A Monte Carlo simulation, grid search, and comparison.
PsyArXiv.
# Related functions: community.unidimensional
Christensen, A. P., Garrido, L. E., & Golino, H. (2023).
Unique variable analysis: A network psychometrics method to detect local dependence.
Multivariate Behavioral Research.
# Related functions: UVA
Christensen, A. P., Garrido, L. E., Guerra-Pena, K., & Golino, H. (2023).
Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation.
Behavior Research Methods.
# Related functions: EGA
Christensen, A. P., & Golino, H. (2021a).
Estimating the stability of the number of factors via Bootstrap Exploratory Graph Analysis: A tutorial.
Psych, 3(3), 479-500.
# Related functions: bootEGA, dimensionStability,
# and itemStability
Christensen, A. P., & Golino, H. (2021b).
Factor or network model? Predictions from neural networks.
Journal of Behavioral Data Science, 1(1), 85-126.
# Related functions: LCT
Christensen, A. P., & Golino, H. (2021c).
On the equivalency of factor and network loadings.
Behavior Research Methods, 53, 1563-1580.
# Related functions: LCT and net.loads
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.
# Related functions: bootEGA, dimensionStability,
# EGA, itemStability, and UVA
Christensen, A. P., Gross, G. M., Golino, H., Silvia, P. J., & Kwapil, T. R. (2019).
Exploratory graph analysis of the Multidimensional Schizotypy Scale.
Schizophrenia Research, 206, 43-51.
# Related functions: CFA and EGA
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.
# 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.
# 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.
# Related functions: CFA, EGA, and bootEGA
Golino, H., Moulder, R., 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.
# Related functions: entropyFit, tefi, and vn.entropy
Golino, H., Nesselroade, J. R., & Christensen, A. P. (2022).
Towards a psychology of individuals: The ergodicity information index and a bottom-up approach for finding generalizations.
PsyArXiv.
# Related functions: boot.ergoInfo, ergoInfo,
jsd, and infoCluster
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.
# Related functions: EGA
Golino, H., Thiyagarajan, J. A., Sadana, M., Teles, M., Christensen, A. P., & Boker, S. M. (2020).
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.
# Related functions: EGA.fit and tefi
Jamison, L., Christensen, A. P., & Golino, H. (2021).
Optimizing Walktrap's community detection in networks using the Total Entropy Fit Index.
PsyArXiv.
# Related functions: EGA.fit and tefi
Jamison, L., Golino, H., & Christensen, A. P. (2023).
Metric invariance in exploratory graph analysis via permutation testing.
PsyArXiv.
# Related functions: invariance
Shi, D., Christensen, A. P., Day, E., Golino, H., & Garrido, L. E. (2023).
A Bayesian approach for dimensionality assessment in psychological networks.
PsyArXiv
# Related functions: EGA
See Also
Useful links:
Report bugs at https://github.com/hfgolino/EGAnet/issues
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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