Bivariate tests with dummy variables

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In summary, the speaker is unsure of where to post their question and explains that they have a dataset with variables that they believe should be turned into dummy variables. They have been asked to do bivariate tests and regression, but are unsure how to do bivariate tests with dummy variables. They express concern that using dummy variables may result in less information compared to using the original variables. They give an example of how they would do pairwise correlations with geographic region and poverty level. They are open to other suggestions on how to perform these tests.
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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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  • #2
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.
 

FAQ: Bivariate tests with dummy variables

What are bivariate tests with dummy variables?

Bivariate tests with dummy variables are statistical tests used to analyze the relationship between two variables, one of which is a categorical variable represented by dummy variables.

When should bivariate tests with dummy variables be used?

Bivariate tests with dummy variables should be used when the relationship between two variables is expected to be non-linear or when one of the variables is categorical in nature and cannot be represented by a continuous variable.

What is the purpose of using dummy variables in bivariate tests?

Dummy variables are used in bivariate tests to represent categorical variables, such as gender, race, or location, as numerical values. This allows for the inclusion of categorical variables in statistical analyses and allows for the interpretation of their effects on the outcome variable.

How are dummy variables created and interpreted in bivariate tests?

Dummy variables are created by assigning numerical values of 0 or 1 to different categories of a categorical variable. In bivariate tests, the coefficient of the dummy variable represents the difference in the outcome variable between the two categories. A positive coefficient indicates a higher value for the category represented by 1, while a negative coefficient indicates a lower value for the category represented by 1 compared to the reference category (represented by 0).

What are some commonly used bivariate tests with dummy variables?

Some commonly used bivariate tests with dummy variables include t-tests, ANOVA, and regression analysis. These tests allow for the comparison of means or regression coefficients between different categories of a categorical variable, while also controlling for other variables.

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