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A method for making inferences in network analysis: Comment on Forbes, Wright, Markon, and Krueger (2017)
Douglas Steinley1, Michaela Hoffman1, Michael J Brusco2
1Department of Psychological Sciences, University of Missouri.
Journal of Abnormal Psychology
|November 7, 2017
Summary
Psychometric network models for psychopathology symptoms require cautious interpretation. New methods reveal many network findings may be indistinguishable from chance, highlighting issues with current approaches.
Area of Science:
- Psychology
- Psychometrics
- Network Analysis
Background:
- Psychopathology symptom networks are increasingly popular but prone to overinterpretation.
- Reproducibility is a growing concern in psychological research.
- Existing network methods may not adequately account for data structure.
Purpose of the Study:
- To investigate the stability and interpretability of psychometric network models.
- To propose a new methodology for analyzing network structures in psychopathology.
- To address limitations in current network analysis techniques.
Main Methods:
- Utilized cross-validation to assess the stability of network estimation methods.
- Developed a nonparametric approach using Monte Carlo simulation for confidence intervals.
- Applied the methodology to the National Comorbidity Survey - Replication data.
Main Results:
- Marginal distributions in multivariate binary data impose significant structure, inflating some centrality statistics.
- A substantial proportion of network results were indistinguishable from chance.
- Issues with multiple testing and applying 1-mode methods to 2-mode networks were identified.
Conclusions:
- Psychometric network models should be used with extreme caution.
- Current interpretation of network results may be overly optimistic.
- Further methodological development is needed for robust psychopathology network analysis.