Limits...
Repeated Measures Correlation

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

The x-axis is reaction time (seconds) and the y-axis is accuracy in visual search. (A) Rmcorr: each dot represents the average reaction time and accuracy for a block, color identifies participant, and colored lines show rmcorr fits for each participant. (B) Simple regression/correlation (averaged data): each dot represents a block, (improperly) treated as an independent observation. The red line is the fit to the simple regression/correlation. (C) Simple regression/correlation (aggregated data): improperly treating each dot as independent. This figure was created using data from Gilden et al. (2010).
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5383908&req=5

Figure 6: The x-axis is reaction time (seconds) and the y-axis is accuracy in visual search. (A) Rmcorr: each dot represents the average reaction time and accuracy for a block, color identifies participant, and colored lines show rmcorr fits for each participant. (B) Simple regression/correlation (averaged data): each dot represents a block, (improperly) treated as an independent observation. The red line is the fit to the simple regression/correlation. (C) Simple regression/correlation (aggregated data): improperly treating each dot as independent. This figure was created using data from Gilden et al. (2010).

Mentions: As in the first example dataset, we worked through three different models for analyzing the relationship between RT and accuracy in Figure 6: (A) rmcorr, (B) simple regression/correlation (averaged data), and (C) simple regression/correlation (aggregated data): improperly treating each observation as independent. At the intra-individual level, rmcorr yields a negative relationship between speed and accuracy, rrm (32) = −0.41, 95% CI [−0.66, −0.07], p < 0.02 (Figure 6A), consistent with a speed-accuracy tradeoff. This indicates that for a given individual, faster speed comes at the cost of reduced accuracy.


Repeated Measures Correlation
The x-axis is reaction time (seconds) and the y-axis is accuracy in visual search. (A) Rmcorr: each dot represents the average reaction time and accuracy for a block, color identifies participant, and colored lines show rmcorr fits for each participant. (B) Simple regression/correlation (averaged data): each dot represents a block, (improperly) treated as an independent observation. The red line is the fit to the simple regression/correlation. (C) Simple regression/correlation (aggregated data): improperly treating each dot as independent. This figure was created using data from Gilden et al. (2010).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5383908&req=5

Figure 6: The x-axis is reaction time (seconds) and the y-axis is accuracy in visual search. (A) Rmcorr: each dot represents the average reaction time and accuracy for a block, color identifies participant, and colored lines show rmcorr fits for each participant. (B) Simple regression/correlation (averaged data): each dot represents a block, (improperly) treated as an independent observation. The red line is the fit to the simple regression/correlation. (C) Simple regression/correlation (aggregated data): improperly treating each dot as independent. This figure was created using data from Gilden et al. (2010).
Mentions: As in the first example dataset, we worked through three different models for analyzing the relationship between RT and accuracy in Figure 6: (A) rmcorr, (B) simple regression/correlation (averaged data), and (C) simple regression/correlation (aggregated data): improperly treating each observation as independent. At the intra-individual level, rmcorr yields a negative relationship between speed and accuracy, rrm (32) = −0.41, 95% CI [−0.66, −0.07], p < 0.02 (Figure 6A), consistent with a speed-accuracy tradeoff. This indicates that for a given individual, faster speed comes at the cost of reduced accuracy.

View Article: PubMed Central - PubMed

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus