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Learning to Live With Sampling Variability: Expected Replicability in Partial Correlation Networks
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DOI：10.1037/met0000417.png)
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Abstract
En 中文
The topic of replicability has recently captivated the emerging field of network psychometrics. Although methodological practice (e.g., p-hacking) has been identified as a root cause of unreliable research findings in psychological science, the statistical model itself has come under attack in the partial correlation network literature. In a motivating example, I first describe how sampling variability inherent to partial correlations can merely give the appearance of unreliability. For example, when going from zero-order to partial correlations there is necessarily more sampling variability that translates into reduced statistical power. I then introduce novel methodology for deriving expected network replicability (ENR), wherein replication is modeled with the Poisson-binomial distribution. This analytic solution can be used with the Pearson, Spearman, Kendall, and polychoric partial correlation coefficient. I first employed the method to estimate ENR for a variety of data sets from the network literature. Here it was determined that partial correlation networks do not have inherent limitations, given current estimates of replicability were consistent with ENR. I then highlighted sources that can reduce replicability, that is, when going from continuous to ordinal data with few categories and employing a multiple comparisons correction. To address these challenges, I described a strategy for using the proposed method to plan for network replication. I end with recommendations that include the importance of the network literature repositioning itself with gold-standard approaches for assessing replication, including explicit consideration of Type I and Type II error rates. The method for computing ENR is implemented in the R package GGMnonreg.
Keywords:
replicability
partial correlation network
power
error rates
frequentist inference
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