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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
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Microsoft Excel is a powerful tool for statistical analysis, including calculating Pearson's correlation coefficient, which measures the strength and direction of a linear relationship between two continuous variables. Pearson's correlation coefficient, often denoted as "r," ranges from -1 to 1. A value close to 1 indicates a strong positive correlation, meaning as one variable increases, the other does too. A value close to -1 indicates a strong negative correlation, implying...
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Repeated Measures Correlation.

Jonathan Z Bakdash1, Laura R Marusich2

  • 1US Army Research Laboratory, Human Research and Engineering Directorate, Aberdeen Proving Ground, USA.

Frontiers in Psychology
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PubMed
Summary
This summary is machine-generated.

Repeated measures correlation (rmcorr) analyzes within-individual associations in repeated measures data, offering greater statistical power than simple correlation. This method avoids data aggregation, providing unbiased insights into paired measures across multiple individuals.

Keywords:
correlationindividual differencesintra-individualmultilevel modelingrepeated measuresstatistical power

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Area of Science:

  • Psychology
  • Statistics
  • Behavioral Science

Background:

  • Simple regression/correlation can yield biased results with non-independent or aggregated data.
  • Violations of independence and differing between- vs. within-participant patterns are common issues.
  • Repeated measures correlation (rmcorr) addresses these limitations for paired measures.

Purpose of the Study:

  • To introduce and explain the repeated measures correlation (rmcorr) technique.
  • To provide guidance on its assumptions, equations, visualization, and statistical power.
  • To demonstrate rmcorr's application using an R package and example datasets.

Main Methods:

  • Rmcorr analyzes paired measures from multiple individuals across two or more occasions.
  • It estimates the common within-individual regression slope, representing the shared association.
  • The study introduces an R package (rmcorr) for inferential statistics and visualization.

Main Results:

  • Rmcorr does not violate the independence of observations assumption.
  • It offers greater statistical power by avoiding data averaging or aggregation.
  • The technique effectively illustrates intra-individual and inter-individual research questions.

Conclusions:

  • Rmcorr is a robust statistical technique for analyzing common linear associations in paired repeated measures data.
  • It provides a powerful alternative to simple correlation and aggregation methods.
  • The R package facilitates reproducible research and accessible application of rmcorr.