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Thread: Significance of a Dichotomous Variable Over a Continuous Variable

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    Significance of a Dichotomous Variable Over a Continuous Variable




    Hi all!

    I ran an analysis using the 2005 healthy eating index (HEI) score as the dependent variable and am a bit confused by my results. The HEI score ranges from 0 to 100, and categorized as bad (<51), needs improvement (52-80), and good (81-100). In my dataset, none of the participants scored in the "good" category, so I created a binary variable for my dependent variable. In the logistic regression models, the overall model is significant, and my primary independent variable of interest was also significant. I received a comment questioning my used of a binary variable, because dichotomizing variables typically results in losing valuable information. So, I ran my analysis again with the dependent variable as a continuous variable, and the overall model was not significant and the independent variable of interest was significant.

    Am I doing something wrong? Why would I see significance with a binary versus continuous variable? Also, is there any reason to justify dichotomizing a variable? I thought it would be best to treat the dependent variable using its most practical form, and I have seen other precedence for this as well. Any help would be much appreciated. Thanks!

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    Re: Significance of a Dichotomous Variable Over a Continuous Variable


    First thing I would do in such a situation is to look for missing values and how they are coded. Are there any values like 999 or -999 in your variable? If there are and they are not recognized by the computer as missing values, then there is your solution.

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