Which Estimation Method to Choose in Network Psychometrics? Deriving Guidelines for Applied Researchers

The Gaussian graphical model (GGM) has recently grown popular in psychological research, with a large body of estimation methods being proposed and discussed across various fields of study, and several algorithms being identified and recommend as applicable to psychological data sets. Such high-dimensional model estimation, however, is not trivial, and algorithms tend to perform differently in different settings. In addition, psychological research poses unique challenges, including placing a strong focus on weak edges (e.g., bridge edges), handling data measured on ordered scales, and relatively limited sample sizes. As a result, there is currently no consensus regarding which estimation procedure performs best in which setting. In this large-scale simulation study, we aimed to overcome this gap in the literature by comparing the performance of several estimation algorithms suitable for Gaussian and skewed ordered categorical data across a multitude of settings, as to arrive at concrete guidelines from applied researchers. In total, we investigated 60 different metrics across 564,000 simulated data sets. We summarized our findings through a platform that allows for manually exploring simulation results. Overall, we found that an exchange between discovery (e.g., sensitivity, edge weight correlation) and caution (e.g., specificity, precision) should always be expected, and achieving both-which is a requirement for perfect replicability-is difficult. Further, we identified that the estimation method is best chosen in light of each research question and have highlighted, alongside desirable asymptotic properties and low sample size discovery, results according to most common research questions in the field. Translational Abstract Estimating network models to explore interactions between psychological variables has grown popular in psychological research. There are many different estimation routines available for this purpose, each of which performs differently in its ability to estimate robust and replicable network structures. Concrete guidelines to which estimator performs best and what sample sizes are required for network estimation are lacking, especially with regards to skewed and ordered categorical data-two types of data commonly occurring in psychological research. In this work, we present and discuss the results from large-scale simulation studies through an interactive environment designed to aid empirical researchers in making these methodological choices.

Automated summary

This study compares the performance of different estimation algorithms for Gaussian and skewed ordered categorical data in various settings through large-scale simulation, and provides guidelines for empirical researchers in choosing estimation methods.