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  • Binary dependent and independent variable: logit result is blank

    Hello. The dependent variable has binary values along with the independent variables except for control variables such as age and age squared. In this form, what type of regression should I use?

    When I used the "regress" command, the coefficients came out neatly, however, when I used "logit" since that is what's supposed to be used with binary values, the result shows collinearity issues and coefficient table is filled with dots.

    What should I do in such a situation?

  • #2
    Sanjana:
    welcome to this forum.
    As per FAQ, please post what you typed and what Stata gave you back. Thanks.
    That said, did Stata thrwo something like -predictor predicts outcome perfectly. # observations were not used- when yoiu ran -logit-?
    Kind regards,
    Carlo
    (Stata 19.0)

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    • #3
      Thank you. I did a few changes in the variables I used. The regression relation I want to find out is the correlation between female decision maker (agricultural decision making of household) and the stunting of children logit stunted
      under 5 yrs. I used mother's education as a control variable however I separated the dummies between primary, secondary and tertiary level. I corrected the regression and only used primary education and omitted few of the variables that I think are causing collinearity problem and found the final result:

      **I am still working in cleaning the dataset for my thesis however I thought of running a rough regression to check if the relationship is significant or not**

      Click image for larger version

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      • #4
        Sanjana:
        1) your pseudo R2 is a bit low;
        2) is age2=age1^2. If this is the case, go -c.age##c.age- exploting the wonderful capabilities of -fvvarlist- notation;
        3) please note that most of your predictors do not seem to explain variations in the regressand (when adjusted for the remaining predictors);
        Kind regards,
        Carlo
        (Stata 19.0)

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        • #5
          Thank you for the feedback. I should probably check the predictors again and then go back to the regression.

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