Hi
Could please helmp me on a problem
Which of the following correlation method listed in table analysis node is the right one to test correlation between a continuous variable and a binary variable....
Continuity Adj. Chi-Square |
Mantel-Haenszel Chi-Square |
Phi Coefficient |
Contingency Coefficient |
Cramer's V |
The type of correlation you are describing is often referred to as a biserial correlation. There are 3 different types of biserial correlations--biserial, point biserial, and rank biserial. Each of these 3 types of biserial correlations are described in SAS Note 22925. I suspect you need to compute either the biserial or the point biserial correlation. The difference between these two, as described in the aforementioned SAS Note, depends on the binary variable. If the binary variable has an underlying continuous distribution, but is measured as binary, then you should compute a "biserial correlation." If the binary variable is truly dichotomous, then a "point biserial correlation" should be used. (The "rank biserial correlation" measures the relationship between a binary variable and a rankings (ie. ordinal) variable.)
If your binary variables are truly dichotomous (as opposed to discretized continuous variables), then you can compute the point biserial correlations directly in PROC CORR. The point biserial correlation is equivalent to the Pearson product moment correlation between two variables where the dichotomous variable is given any two numeric values. This information is also mentioned in our FASTats link under Correlation> Point Biserial. PROC CORR prints the Pearson product moment correlation by default, so no additional options are required.
If your binary variables are dichotomized continuous variables, then you will need to compute biserial correlations between each of these binary variables and your continuous variable. These correlations are only available through our %BISERIAL macro. SAS Note 24991 describes this macro and includes the source code for the macro in the Downloads tab.
Hey Omer,
Here a great resource that summarizes statistical tests and how to code them in SAS.
Choosing the Correct Statistical Test in SAS, Stata and SPSS
I hope it helps,
Miguel
miguel this is truly a perfect guide for my problem
thank you very much for sharing the page with me...
The type of correlation you are describing is often referred to as a biserial correlation. There are 3 different types of biserial correlations--biserial, point biserial, and rank biserial. Each of these 3 types of biserial correlations are described in SAS Note 22925. I suspect you need to compute either the biserial or the point biserial correlation. The difference between these two, as described in the aforementioned SAS Note, depends on the binary variable. If the binary variable has an underlying continuous distribution, but is measured as binary, then you should compute a "biserial correlation." If the binary variable is truly dichotomous, then a "point biserial correlation" should be used. (The "rank biserial correlation" measures the relationship between a binary variable and a rankings (ie. ordinal) variable.)
If your binary variables are truly dichotomous (as opposed to discretized continuous variables), then you can compute the point biserial correlations directly in PROC CORR. The point biserial correlation is equivalent to the Pearson product moment correlation between two variables where the dichotomous variable is given any two numeric values. This information is also mentioned in our FASTats link under Correlation> Point Biserial. PROC CORR prints the Pearson product moment correlation by default, so no additional options are required.
If your binary variables are dichotomized continuous variables, then you will need to compute biserial correlations between each of these binary variables and your continuous variable. These correlations are only available through our %BISERIAL macro. SAS Note 24991 describes this macro and includes the source code for the macro in the Downloads tab.
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Use this tutorial as a handy guide to weigh the pros and cons of these commonly used machine learning algorithms.
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