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This study evaluated methods for handling missing data in network analysis for psychological constructs. Direct EM algorithm generally outperformed other methods, especially with large sample sizes or small networks.

Keywords:
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Area of Science:

  • Psychology
  • Network Science
  • Statistics

Background:

  • Network analysis is increasingly used for psychological constructs.
  • Applied researchers lack clear guidelines for handling missing data in network analysis.

Purpose of the Study:

  • To compare the performance of different missing data handling techniques in network analysis.
  • To identify optimal methods for recovering population networks under various conditions.

Main Methods:

  • Simulation study comparing a two-step EM algorithm, a direct EM algorithm, and pairwise deletion.
  • Investigated varying network sizes, sample sizes, missing data mechanisms, and percentages of missing values.
  • Evaluated network recovery based on precision matrix loss, edge set identification, and network statistics.

Main Results:

  • Adequate network recovery was observed only with large sample sizes or small networks (p=10).
  • The direct EM algorithm demonstrated superior sensitivity and performance across most conditions.
  • The two-step EM algorithm showed better specificity with very large n/p ratios.
  • Pairwise deletion frequently failed to converge and yielded poor results.

Conclusions:

  • The direct EM algorithm is recommended for most network analysis applications with missing data.
  • The direct EM algorithm effectively mitigates the impact of missing data.
  • Further modifications to the two-step EM algorithm could enhance its performance.