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On the equivalency of factor and network loadings
Alexander P Christensen1, Hudson Golino2
1University of North Carolina at Greensboro, Greensboro, NC, 27402, USA. alexpaulchristensen@gmail.com.
Behavior Research Methods
|January 7, 2021
Summary
New network loadings effectively separate latent causes and estimate factor loadings, offering a valuable tool for psychological network analysis and measurement. This method enhances understanding of complex psychological constructs.
Area of Science:
- Psychometrics
- Network Science
- Quantitative Psychology
Background:
- Node strength in network analysis approximates confirmatory factor analysis (CFA) loadings.
- Node strength conflates multiple latent causal influences.
- A need exists for network measures that disentangle latent causes.
Purpose of the Study:
- To formulate network loadings as a network equivalent to factor loadings.
- To evaluate the ability of network loadings to separate multiple latent causes.
- To assess the accuracy of network loadings in estimating factor loading matrices.
Main Methods:
- Developed network loadings as a novel network measure.
- Conducted three simulations to test network loadings.
- Derived effect size guidelines for network loadings.
- Developed the Loadings Comparison Test (LCT) algorithm.
Main Results:
- Network loadings successfully separated multiple latent causes in simulations.
- Network loadings accurately estimated simulated factor loading matrices.
- The Loadings Comparison Test (LCT) demonstrated high sensitivity and specificity in identifying data-generating models.
- Network and factor loadings showed distinct patterns when data originated from different models.
Conclusions:
- Network loadings offer a viable alternative to traditional factor loadings in specific contexts.
- Network loadings can be used similarly to factor loadings for applications like item selection and measurement invariance.
- The LCT provides a robust method for distinguishing between factor and network data structures.
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