Repeated Measures Correlation

By Jonathan Z. Bakdash, Laura R. Marusich. Frontiers in Psychology, 2017

1,832 citations recorded by the source corpus.

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. 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. Unlike simple regression/correlation, rmcorr does not violate the assumption of independence of observations. Also, rmcorr tends to have much greater statistical power because neither averaging nor aggregation is necessary for an intra-individual research question. Rmcorr estimates the common regression slope, the association shared among individuals. To make rmcorr accessible, we provide background information for its assumptions and equations, visualization, power, and tradeoffs with rmcorr compared to multilevel modeling. We introduce the R package (rmcorr) and demonstrate its use for inferential statistics and visualization with two example datasets. The examples are used to illustrate research questions at different levels of analysis, intra-individual, and inter-individual. Rmcorr is well-suited for research questions regarding the common linear association in paired repeated measures data. All results are fully reproducible.

Subfields: Computer Networks and Communications, Experimental and Cognitive Psychology, Food Science

View Paper: https://www.semanticscholar.org/paper/5363ae6d0b2b708542a5e526c30dcd1df8d8fd2e

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Citations in the corpus

A computational solution for bolstering reliability of epigenetic clocks: Implications for clinical trials and longitudinal tracking (2021, methodology): “To account for varying measurement intervals, we performed repeated-measures correlation 40 .”
Fifty shades of food: The influence of package color saturation on health and taste in consumer judgments (2020, methodology): “We conducted all analyses with SPSS and R (R Core Team, 2016) statistics software, specifically the packages rmcorr (Bakdash & Marusich, 2017), lme4 (Bates, Maechler, Bolker, & Walker, 2015), and nlme (Pinheiro, Bates, DebRoy, Sarkar, & R Core Team, 2016).”
Employee adaptive performance and job satisfaction during organizational crisis: the role of self-leadership (2018, methodology): “…explain why there were no changes in the level of self-leadership Within-subjects correlations were estimated using the R software package “rmcorr” (Bakdash & Marusich, 2017); it estimates the repeated measures correlation by determining the common within-individual association for paired…”
Pingouin: statistics in Python (2018, background): “…effect sizes, as well as more advanced functions such as Bayesian T-tests (Rouder, Speckman, Sun, Morey, & Iverson, 2009), repeated measures correlations (Bakdash & Marusich, 2017), robust correlations (Pernet, Wilcox, & Rousselet, 2012) and circular statistics (Berens, 2009), to cite but a few.”