One common solution is to average the repeated measures data for each participant prior to performing the correlation. Analyzing non-independent data with techniques that assume independence is a widespread practice but one that often produces erroneous results (Kenny and Judd, 1986 Molenaar, 2004 Aarts et al., 2014). For example, if a study collected height and weight for a sample of people at three time points, there would likely be non-independence in the errors of the three observations belonging to the same person. However, the assumption of independence is violated in repeated measures, in which each participant provides more than one data point. For example, when correlating the current height and weight of people drawn from a random sample, there is no reason to expect a violation of independence. This assumption does not pose a problem if each participant or independent observation is a single data point of paired measures (i.e., two data points corresponding to the same individual such as height and weight). However, widely used techniques for correlation, such as simple (ordinary least squares with a single independent variable) regression/Pearson correlation, assume independence of error between observations (Howell, 1997 Johnston and DiNardo, 1997 Cohen et al., 2003). All results are fully reproducible.Ĭorrelation is a popular measure to quantify the association between two variables. Rmcorr is well-suited for research questions regarding the common linear association in paired repeated measures data. The examples are used to illustrate research questions at different levels of analysis, intra-individual, and inter-individual. We introduce the R package (rmcorr) and demonstrate its use for inferential statistics and visualization with two example datasets. To make rmcorr accessible, we provide background information for its assumptions and equations, visualization, power, and tradeoffs with rmcorr compared to multilevel modeling. Rmcorr estimates the common regression slope, the association shared among individuals. Also, rmcorr tends to have much greater statistical power because neither averaging nor aggregation is necessary for an intra-individual research question. Unlike simple regression/correlation, rmcorr does not violate the assumption of independence of observations. Simple regression/correlation is often applied to non-independent observations or aggregated data this may produce biased, specious results due to violation of independence and/or differing patterns between-participants versus within-participants.
Repeated measures correlation (rmcorr) is a statistical technique for determining the common within-individual association for paired measures assessed on two or more occasions for multiple individuals.