Clyde Schechter

Makes sense. For example while collecting gender data, ideally one should target sampling in a way that it collects other self reported gender-identity observations apart from cis-man and cis-woman. Otherwise sex and self- reported gender identity will be perfectly correlated.

A) Is it good practice to do correlation analysis before regression and report correlation coefficients as well? Though we can get most of the information from regression coefficients, but correlation coefficients can help us gauge some direction, can help explain large standard errors for the variables and direction to check for multicollinearity.

B) is Pearson correlation a good choice with both variables as binary ?

Makes sense. For example while collecting gender data, ideally one should target sampling in a way that it collects other self reported gender-identity observations apart from cis-man and cis-woman. Otherwise sex and self- reported gender identity will be perfectly correlated.

A) Is it good practice to do correlation analysis before regression and report correlation coefficients as well? Though we can get most of the information from regression coefficients, but correlation coefficients can help us gauge some direction, can help explain large standard errors for the variables and direction to check for multicollinearity.

B) is Pearson correlation a good choice with both variables as binary ?

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