Characterization of the global network topology and the position of individual nodes in that topology. Psychometric network analysisThe analysis of multivariate psychometric data using network structure estimation and network description.

An important goal for psychological science is developing methods to characterize relationships between variables. Customary approaches use structural equation models to connect latent factors to a number of observed measurements, or test causal hypotheses between observed variables. More recently, regularized partial correlation networks have been proposed as an alternative approach for characterizing relationships among variables through covariances in the precision matrix. While the graphical lasso (glasso) has emerged as the default network estimation method, it was optimized in fields outside of psychology with very different needs, such as high dimensional data where the number of variables (p) exceeds the number of observations (n). In this paper, we describe the glasso method in the context of the fields where it was developed, and then we demonstrate that the advantages of regularization diminish in settings where psychological networks are often fitted (p ≪ n). We first show that improved properties of the precision matrix, such as eigenvalue estimation, and predictive accuracy with cross-validation are not always appreciable. We then introduce non-regularized methods based on multiple regression and a non-parametric bootstrap strategy, after which we characterize performance with extensive simulations. Our results demonstrate that the non-regularized methods can be used to reduce the false positive rate, compared to glasso, and they appear to provide consistent performance across sparsity levels, sample composition (p/n), and partial correlation size. We end by reviewing recent findings in the statistics literature that suggest alternative methods often have superior performance than glasso, as well as suggesting areas for future research in psychology. The non-regularized methods have been implemented in the R package GGMnonreg.

It is common practice in correlational or quasiexperimental studies to use statistical control to remove confounding effects from a regression coefficient. Controlling for relevant confounders can debias the estimated causal effect of a predictor on an outcome; that is, it can bring the estimated regression coefficient closer to the value of the true causal effect. But statistical control works only under ideal circumstances. When the selected control variables are inappropriate, controlling can result in estimates that are more biased than uncontrolled estimates. Despite the ubiquity of statistical control in published regression analyses and the consequences of controlling for inappropriate third variables, the selection of control variables is rarely explicitly justified in print. We argue that to carefully select appropriate control variables, researchers must propose and defend a causal structure that includes the outcome, predictors, and plausible confounders. We underscore the importance of causality when selecting control variables by demonstrating how regression coefficients are affected by controlling for appropriate and inappropriate variables. Finally, we provide practical recommendations for applied researchers who wish to use statistical control.

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An important goal for psychological science is developing methods to characterize relationships between variables. The customary approach uses structural equation models to connect latent factors to a number of observed measurements. More recently, regularized partial correlation networks have been proposed as an alternative approach for characterizing relationships among variables through covariances in the precision matrix. While the graphical lasso (glasso) method has merged as the default network estimation method, it was optimized in fields outside of psychology with very different needs, such as high dimensional data where the number of variables (p) exceeds the number of observations (n). In this paper, we describe the glasso method in the context of the fields where it was developed, and then we demonstrate that the advantages of regularization diminish in settings where psychological networks are often fitted (p ≪ n). We first show that improved properties of the precision matrix, such as eigenvalue estimation, and predictive accuracy with cross-validation are not always appreciable. We then introduce non-regularized methods based on multiple regression, after which we characterize performance with extensive simulations. Our results demonstrate that the non-regularized methods consistently outperform glasso with respect to limiting false positives, and they provide more consistent performance across sparsity levels, sample composition (p=n), and partial correlation size. We end by reviewing recent findings in the statistics literature that suggest alternative methods often have superior than glasso, as well as suggesting areas for future research in psychology.