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Related Concept Videos

Correlations02:20

Correlations

34.6K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Correlation and Regression00:53

Correlation and Regression

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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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Correlation and Causation01:27

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
Correlation versus Causation
If the dependent variable increases or decreases when the independent variable increases, there is a positive or negative...
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Microsoft Excel: Pearson's Correlation01:18

Microsoft Excel: Pearson's Correlation

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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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Cause and Effect01:53

Cause and Effect

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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Correlation01:09

Correlation

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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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Conducting correlation analysis: important limitations and pitfalls.

Roemer J Janse1, Tiny Hoekstra2, Kitty J Jager3

  • 1Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.

Clinical Kidney Journal
|November 10, 2021
PubMed
Summary

The correlation coefficient measures variable association but has limitations. For assessing agreement between measurement methods, alternatives like the intraclass coefficient are more appropriate.

Keywords:
Bland–AltmanPearson correlation coefficientcomparing methodscorrelation analysislimits of agreement

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Area of Science:

  • Statistics
  • Nephrology Research

Background:

  • The correlation coefficient is widely used to assess relationships between variables.
  • Its application is common in scientific studies, including those in nephrology.

Purpose of the Study:

  • To explain the basics, interpretation, and assumptions of the correlation coefficient.
  • To highlight the limitations of the correlation coefficient, particularly for assessing agreement.
  • To introduce superior alternatives for agreement assessment in scientific research.

Main Methods:

  • Review of statistical principles concerning the correlation coefficient.
  • Discussion of its assumptions (e.g., linear association) and interpretation.
  • Comparative analysis with agreement metrics like intraclass coefficient and Bland-Altman limits.

Main Results:

  • The correlation coefficient is suitable for association but not ideal for agreement.
  • Linearity assumption and range sensitivity are key limitations.
  • Intraclass coefficient and Bland-Altman methods offer valid agreement assessment.

Conclusions:

  • The correlation coefficient should not be used to assess agreement between measurement methods.
  • Researchers should utilize appropriate statistical tools like the intraclass coefficient or Bland-Altman analysis for agreement studies.
  • Proper statistical method selection enhances the validity of research findings in nephrology and beyond.