Multinomial logistic regression

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    I am using multinomial logistic regression to study driver distraction.  My dependent variable has four outcome categories (not distracted, slightly distracted, distracted, very distracted). The first outcome category (not distracted) is set as the baseline. The SPSS 17.0 analysis consists of three independent binary comparisons. Now, when I calculate the Predicted Probability P(Y) of an outcome category Y occurring, I am using the binary logistic regression equation suggested by Field (2009) page 300 for each of the three pairs of MLR output as follows:

    P(Y1) = 1/(1+e-Y1),

    Y1 = β0 + β1X1 + β2X2 + β3X3 + ——— + βkXk


    In my discussion of the results, I am mentioning the probabilities are in comparison (versus) to the baseline category. For example, the likelihood of outcome B occurring VERSUS A (baseline) occurring will increase by ……


    Please let me know if this is a plausible approach, or do I need to use the following more complex MLR equation for predicting each of the three outcomes:

    P(Y1) = eY1/( eY1+ eY2+ eY3)


    Please respond at your earliest convenience.  If you are interested, I can send you my paper.



    Field, A. (2009). Discovering Statistics Using SPSS. SAGE Publications Ltd., 3rd Edition.

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