Bivariate tests with dummy variables

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SUMMARY

This discussion focuses on performing bivariate tests using dummy variables in statistical analysis. The user expresses confusion about how to effectively conduct these tests when transforming categorical and ordinal variables into dummy variables. They highlight the potential loss of information when using dummy variables, specifically in the context of geographic regions and poverty levels. The conversation emphasizes the need for clarity on the methodology for pairwise correlations involving dummy variables.

PREREQUISITES
  • Understanding of dummy variable creation in statistical analysis
  • Familiarity with bivariate testing methods
  • Knowledge of correlation techniques
  • Basic concepts of regression analysis
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  • Research methods for conducting bivariate tests with dummy variables
  • Learn about pairwise correlation techniques for categorical data
  • Explore regression analysis involving dummy variables
  • Study the implications of information loss when using dummy variables
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Data analysts, statisticians, and researchers working with categorical data who need to perform bivariate tests and regression analysis using dummy variables.

Cookie G
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I'm not sure if this belongs in the Basic area or here.

I have a dataset with variables that I think it makes sense to make them into dummy variables (some are categories, some are ordinal). I've been asked to do bivariate tests and regression. I don't understand how I can do bivariate tests when I'm using dummy variables.

Thanks
 
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I guess I just use the dummy variables for the bivariate tests. It just seems less than ideal because in a sense one dummy variable has less information than the original variable.

For example, if I have geographic region and poverty level, I could do pairwise correlations. region 1 with pov level 1, region 1 with pov level 2, region 1 with pov level 3, region 2 with pov level 1, etc.

I've not been able to figure out any other way to do this. Please let me know if I'm missing something.
 

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