A comprehensive comparison of measures for assessing profile similarity

Profile similarity measures are used to quantify the similarity of two sets of ratings on multiple variables. Computing profile similarity has increased in popularity to study for instance how two persons are similar in terms of their personality or profile of experienced emotions. This popularity has spurred the development of many new measures of profile similarity. Yet, it remains unclear how these measures are distinct or overlap and what type of information they precisely convey, making it unclear how similar or different conclusions would have been, had another measure been applied. With this study, we aim to provide clarity with respect to how existing measures interrelate, and provide recommendations for their use by comparing a wide range of profile similarity measures. To this end, we have taken four steps. First, we reviewed a set of 88 similarity measures by applying them to multiple cross-sectional and intensive longitudinal data sets on emotional experience and retained 43 useful profile similarity measures. Second, we have clustered these 43 measures into similarly behaving groups, and found three general clusters: one cluster with difference measures, one cluster with product measures that could be split into four more nuanced groups and one miscellaneous cluster that could be split into two more nuanced groups. Third, we have interpreted what unifies these groups and their subgroups (and hence what information they convey) based on theory and formulas. Last, based on our findings, we discuss some recommendations with respect to the choice of measure, the Pearson correlation and centering.