What question did this study set out to answer?

To introduce repeated measures correlation (rmcorr) as a statistical technique for evaluating common within-individual linear associations across multiple measurement occasions.

April 7, 2017SHILAP Revista de lepidopterologíaOpen Access

Repeated Measures Correlation

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Authors

JBJonathan Z. BakdashThe University of Texas at Dallas LMLaura R. MarusichUnited States Department of the Army

Discussion

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Implication

Methodological tutorial demonstrates repeated measures correlation for paired longitudinal data, highlighting improved statistical power without violating independence assumptions.

Key Points

To introduce repeated measures correlation (rmcorr) as a statistical technique for evaluating common within-individual linear associations across multiple measurement occasions.
Formulated the mathematical framework, slope estimation procedures, and assumptions of rmcorr relative to standard regression and multilevel modeling.
Developed and evaluated an open-source R software package (rmcorr) using two reproducible example datasets spanning intra-individual and inter-individual analyses.
Demonstrated that rmcorr avoids biased or specious associations caused by violating observational independence, without requiring data aggregation or averaging.
Showed that retaining all measurement points provides substantially greater statistical power to detect shared linear slopes compared to traditional correlation on aggregated data.

Cite This Study

Bakdash et al. (2017) studied this question.

synapsesocial.com/papers/69dcc9ba0de68e8319e53612 https://doi.org/10.3389/fpsyg.2017.00456

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April 7, 2017SHILAP Revista de lepidopterologíaOpen Access

Repeated Measures Correlation

View Full Paper

Ask AI

Bookmark

Authors

JBJonathan Z. BakdashThe University of Texas at Dallas LMLaura R. MarusichUnited States Department of the Army

Discussion

Member takes

Implication

Methodological tutorial demonstrates repeated measures correlation for paired longitudinal data, highlighting improved statistical power without violating independence assumptions.

Key Points

To introduce repeated measures correlation (rmcorr) as a statistical technique for evaluating common within-individual linear associations across multiple measurement occasions.
Formulated the mathematical framework, slope estimation procedures, and assumptions of rmcorr relative to standard regression and multilevel modeling.
Developed and evaluated an open-source R software package (rmcorr) using two reproducible example datasets spanning intra-individual and inter-individual analyses.
Demonstrated that rmcorr avoids biased or specious associations caused by violating observational independence, without requiring data aggregation or averaging.
Showed that retaining all measurement points provides substantially greater statistical power to detect shared linear slopes compared to traditional correlation on aggregated data.

Cite This Study

Bakdash et al. (2017) studied this question.

synapsesocial.com/papers/69dcc9ba0de68e8319e53612 https://doi.org/10.3389/fpsyg.2017.00456

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