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Thread: Linear, step wise regression vs binary logistic regression - which method to choose

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    Linear, step wise regression vs binary logistic regression - which method to choose




    Dear friends
    I have a dilemma about choosing a right logistical method.

    i want to find predictor factors of life satisfaction and have 5 covariats.
    My dependent variable - life satisfaction is on nominal level. 0 - satisfied, 1 not satisfied
    Excoriates vary from ordinal to nominal levels.

    So which method should i choose? I had a binary logistic regression but i don't think now it is an appropriate method.

    Thank you for the advice

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    Re: Linear, step wise regression vs binary logistic regression - which method to choo

    Why don't you think it is appropriate?

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    Re: Linear, step wise regression vs binary logistic regression - which method to choo

    because when i change the direction of the dependent viable 0 not satisfied, 1 satisfied (i have now 0 - satisfied, 1 not satisfied ) i get very different OR and 95 % CI.

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    Re: Linear, step wise regression vs binary logistic regression - which method to choo

    Of course it's going to be different, as you are now working out the OR of being 'not satisfied' rather than 'satisfied'.

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    Re: Linear, step wise regression vs binary logistic regression - which method to choo

    Quote Originally Posted by evelyn13 View Post
    Of course it's going to be different, as you are now working out the OR of being 'not satisfied' rather than 'satisfied'.
    it is very different. for example, if for not satisfied OR for gender is 0.06, on satisfied version i get OR 4,58 on THE SAME variable.

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    Re: Linear, step wise regression vs binary logistic regression - which method to choo

    They should be very different. Think of it this way: if the odds of being happy for women is twice the odds of being happy for men, then the odds of being unhappy for women should be half the odds of being unhappy for men. So the relationship should that the OR in the happy version is 1/OR in the unhappy version, and vice versa. The fact that the ORs on the same variable are very different depending on how you code your dependent variable is normal.

    However, there is a big problem with your data: \frac{1}{.06} \approx 16.7 \neq 4.58. I suspect that when reverse coding your sattisfaction variable missing values were mistakenly turned into observed values, or some similar mistake.

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    Re: Linear, step wise regression vs binary logistic regression - which method to choo

    Quote Originally Posted by maartenbuis View Post
    They should be very different. Think of it this way: if the odds of being happy for women is twice the odds of being happy for men, then the odds of being unhappy for women should be half the odds of being unhappy for men. So the relationship should that the OR in the happy version is 1/OR in the unhappy version, and vice versa. The fact that the ORs on the same variable are very different depending on how you code your dependent variable is normal.

    However, there is a big problem with your data: \frac{1}{.06} \approx 16.7 \neq 4.58. I suspect that when reverse coding your sattisfaction variable missing values were mistakenly turned into observed values, or some similar mistake.

    Thank you sooo much!! I will see it now
    Last edited by badibad; 09-29-2015 at 05:40 AM.

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    Re: Linear, step wise regression vs binary logistic regression - which method to choo


    Quote Originally Posted by maartenbuis View Post
    They should be very different. Think of it this way: if the odds of being happy for women is twice the odds of being happy for men, then the odds of being unhappy for women should be half the odds of being unhappy for men. So the relationship should that the OR in the happy version is 1/OR in the unhappy version, and vice versa. The fact that the ORs on the same variable are very different depending on how you code your dependent variable is normal.

    However, there is a big problem with your data: \frac{1}{.06} \approx 16.7 \neq 4.58. I suspect that when reverse coding your sattisfaction variable missing values were mistakenly turned into observed values, or some similar mistake.
    just a question
    Would it be the same way if i have an ordinal variable (scale 1-5)

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