Analysis Blinding as a Complement to Preregistration: A Tutorial 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 even when implemented may leave room for researcher degrees of freedom, particularly when data-dependent decisions are difficult to fully anticipate. In this tutorial, we demonstrate how researchers can safeguard against bias by using analysis blinding as a complementary step to preregistration. 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. In this tutorial, we present three common research questions in network analysis and show how they can be addressed using analysis blinding to safeguard their confirmatory nature. We use data from previously published studies employing network analysis and offer a practical, step-by-step guide along with R code that researchers can adapt to their own data and research questions.