4.6 The logit vs. probit transformation
In formulating the logit model, an implicit assumption has been made about the variance of the error term. To arrive at equation (3) requires the var(ε) = π2/3. An alternative assumption is that var(ε) = 1. In this case the model is referred to as a probit model. In short, while the assumption about the var(ε) is arbitrary, it does not affect the estimated values of the probabilities.[16] While the choice between the probit and logit models is to a large extent arbitrary, the effects in the logit model, unlike the case of the probit, can be interpreted as changes in the odds. Additionally, logit and probit models are generally asymptotically equivalent – so in a study such as this the coefficients should be effectively the same. We have chosen to use logit models in this study.
Notes
- [16]For a detailed discussion see Long and Freese (2006).
