This study investigated the small sample biasness of the ordered logit model parameters under multicollinearity using Monte Carlo simulation. The results showed that the level of biasness associated with the ordered logit model parameters consistently decreases for an increasing sample size while the distribution of the parameters becomes less variable with low extreme values. In the presence of multicollinearity, the level of biasness increases and this issue is particularly severe for small sample sizes. By comparing three different approaches for dealing with the multicollinearity problem in the model, the study demonstrated that the use of penalized maximum likelihood estimation technique provides better results which is interpretable compared to the other approaches considered.