Safeguarding Against Bias Without Preregistration: A Tutorial on Analysis Blinding for Network Analysis

Network analysis has become a popular computational modeling approach in psychological research and is now increasingly used to address confirmatory research questions. However, preregistration of such analyses remains rare and given the complexity of the data and methods, is often perceived as limiting or even infeasible. In this tutorial, we demonstrate how researchers can safeguard against bias without relying on preregistration by using analysis blinding. The method involves temporarily altering the data to remove the key effect of interest while preserving all other aspects. Analysis blinding makes it possible to explore important features of the data, for instance, spotting outliers or adjusting the computational model, without introducing bias. This tutorial presents three common research questions in network analysis and shows how they can be addressed using analysis blinding to safeguard their confirmatory nature. It uses data from previously published studies employing network analysis, and offers a practical, step-by-step guide along with R code that researchers can adapt to their own data and research questions.