The Ordinal logistic regression is another regression from all the regressions that are utilised in the SPSS data analysis where this regression is sometimes called just by the name of 'ordinal regression' which is basically used for predicting the ordinal dependent variable from the given variables of one or more independent variables. This regression can also be considered as either a form of generalisation of the multiple linear regression or as the generalisation form of binomial logistic regression. Along with all the other types of regressions utilised in the SPSS statistical software, the ordinal logistic regression is considered to be used for formulating the interactions between the independent variables which are to be predicted and all the dependent variables involved.

What is Ordinal Logistic Regression in SPSS?

The Ordinal Logistic Regression SPSS is a form of statistical test that is used for predicting a single ordered dependent categorical variable by using one or more other independent variables. This regression can also be used for determining the statistical numerical relationship between all such sets of variables. One important thing to notice is that the variable which is to be predicted needs to be ordinal in its form and along with that the data set must also need to meet all the other assumptions, all of which are involved in this whole process. The Ordinal Logistic Regression is often known by the name of ordered categorical logistic regression due to the presence of an ordered logit and an ordinal regression. In every statistical method, there are some assumptions where these assumptions are there to mean that the data of the regression must need to satisfy certain properties of the regression in order to be accurate in the form of the statistical method results. In ordinal logistic regression, there are four main assumptions are involved:

Linearity – This Logistic regression also gets fit into both the logistic curve and binary data where the logistic curve is interpreted with the probability which is associated with each of the outcomes which are across the values of all the independent variables. Logistic regression mainly assumes the relationship between the natural log of all such probabilities and the predictor variable which is linear.

No Outliers – In this assumption, the variables must not have to contain outliers because it is sensitive to outlier points that contain large values or small values. It can be identified whether the variables contain outliers or not by simply plotting them in places and then observing if any point is far from all other points or not.

Independence – Here, each of the observations needs to be independent which means that each of the values of the variables is not dependent on any other variable. As an example, this assumption usually gets violated whenever there are multiple data points present over time from the same unit of observation. The reason behind this is that those data points from the same unit of observation are likely to be related to each other or get affected by one another.

No Multicollinearity – The Multicollinearity assumption mainly refers to such a scenario where two or more independent variables get correlated, substantially with each other. When the multicollinearity assumption is present, then the statistical significance and coefficients of the regression become unstable and less trustworthy, although it doesn’t put much effect on how well this regression model gets fitted in the data per se.

This Ordinal Logistic Regression is generally used in two scenarios, where the first scenario is when the user wants to use one of the variables for doing a prediction of the other one, or he/she wants just to to quantify whether the numerical relationship is present between the two variables or not. And the second scenario is when the variable which the user wants to predict which the dependent variable is it, atypically ordered ordinal categorical variable or not.

Conclusion

The Ordinal Logistic Regression is often known by the name of ordered categorical logistic regression due to the presence of an ordered logit and an ordinal regression. One important thing to notice is that the variable which is to be predicted needs to be ordinal in its form and along with that the data set must also meet all the other assumptions all of which are involved in this whole process. In every statistical method there are some assumptions where these assumptions are there to mean that the data of the regression must need to satisfy certain properties of the regression in order to be accurate in the form of the statistical method results. With SPSS help in ordinal logistic regression, there are four main assumptions involved, which are Linearity, No Outliers, Independence and the No Multicollinearity assumption.