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Area of Science:

  • Network psychometrics
  • Psychological science
  • Statistical modeling

Background:

  • Replicability is a growing concern in network psychometrics.
  • Methodological issues like p-hacking and statistical model limitations in partial correlation networks are suspected causes of unreliable findings.
  • Sampling variability in partial correlations can create an illusion of unreliability and reduce statistical power.

Purpose of the Study:

  • To introduce a novel methodology for deriving expected network replicability (ENR).
  • To assess the inherent reliability of partial correlation networks.
  • To identify factors that reduce network replicability and propose strategies for planning future replications.

Main Methods:

  • Developed a new analytical method to calculate ENR using the Poisson-binomial distribution.
  • Applied the ENR method to various datasets from the network literature using different partial correlation coefficients (Pearson, Spearman, Kendall, polychoric).
  • Modeled replication processes to estimate expected replicability.

Main Results:

  • Partial correlation networks do not appear to have inherent limitations regarding replicability; current estimates align with ENR.
  • Replicability can be reduced by transitioning from continuous to ordinal data with few categories and by applying multiple comparison corrections.
  • The proposed ENR method can be used to plan network replication studies.

Conclusions:

  • The study provides a robust framework for assessing and planning network replicability in psychometrics.
  • Recommendations include adopting gold-standard replication assessment methods and explicitly considering Type I and Type II error rates.
  • The ENR computation method is available in the R package GGMnonreg.