Proportional Odds Ordinal Logistic Regression. The model may be represented by a series of logistic regressions for dependent binary variables with. Hosmer and Lemeshow Applied Logistic Regression 2nd ed p.
This is a problem when the data structure is sparse. Introduction to regression with ordinal response variable eg. With a larger number of adjustments in the model the proportional odds model tends toward a saturated model where each stratum specific probability is close or identical to the fitted values.
Logistic regression is special case c 2 Uses ordinality of y without assigning category scores Can motivate proportional odds structure with regression model for underlying continuous latent variable Anderson and Philips 1981 related probit model.
297 Before we explain a proportional odds model lets just jump ahead and do it. By ordered we mean categories that have a natural ordering such as Disagree Neutral Agree or Everyday Some days Rarely Never. The proportional odds model for ordinal logistic regression provides a useful extension of the binary logistic model to situations where the response variable takes on values in a set of ordered categories. Logistic regression is special case c 2 Uses ordinality of y without assigning category scores Can motivate proportional odds structure with regression model for underlying continuous latent variable Anderson and Philips 1981 related probit model.
