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Description: (for a Special Issue on novel methods and techniques in Social Cognition.) Representational similarity analysis (RSA) is a simple and widely used technique for comparing relationships between different types of measures using a common set of items. RSA allows researchers to infer structural similarities among these items across diverse modalities (e.g., brain and behavioral measures of the same set of participants, response patterns to the same stimuli in different conditions). This technique effectively quantifies relationships between complex, high-dimensional measures, contributing to its recent popularity. However, existing tutorials focus on neuroscience use cases, limiting their applicability to social cognitive research. This tutorial covers an accessible introduction to RSA, discussing its strengths and limitations compared to other multivariate methods. We provide commented code and functions in R and Python in the context of two examples that showcase different applications of RSA, illustrating its potential to address questions of broad interest to the field of social cognition.