Abstract
We describe a web and standalone Shiny app for calculating the common, linear within-individual association for repeated assessments of paired measures with multiple individuals: repeated measures correlation (rmcorr). This tool makes rmcorr more widely accessible, providing a graphical interface for performing and visualizing the output of analysis with rmcorr. In contrast to rmcorr, most widely used correlation techniques assume paired data are independent. Incorrectly analyzing repeated measures data as independent will likely produce misleading results. Using aggregation or separate models to address the issue of independence may obscure meaningful patterns and will also tend to reduce statistical power. rmcorrShiny (repeated measures correlation Shiny) provides a simple and accessible solution for computing the repeated measures correlation. It is available at: https://lmarusich.shinyapps.io/shiny_rmcorr/.
Highlights
IntroductionThe most common techniques for calculating the correlation between two variables (e.g., the Pearson correlation coefficient) assume that each pair of data points arises from an independent observation
The most common techniques for calculating the correlation between two variables assume that each pair of data points arises from an independent observation
We previously developed the rmcorr package[8] in R9 to make the repeated measures correlation technique widely available for researchers; it has since been adapted as a function in the Pingouin statistics package[10] for Python
Summary
The most common techniques for calculating the correlation between two variables (e.g., the Pearson correlation coefficient) assume that each pair of data points arises from an independent observation. Modeling repeated measures data as independent observations is surprisingly prevalent in published research, even though such results will generally be misleading.[2,3,4] A common way to resolve this problem is to use aggregated data: first taking an average of the repeated measures data of each person so that every individual again contributes a single paired data point, and calculating the correlation from these averages (between-participants). Another possibility is to use separate models to analyze each paired data point, removing the dependency. The study by Raz et al.[1] computed separate correlations between brain region volume and age at each of the two time points
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
