Introduction

Ordinal logistic regression is one of the most important statistical methods when the dependent variable is measured on an ordinal scale, that is, when it consists of categories that can be ordered hierarchically, without equal distances between them. The main advantage of the method is that it can handle dependent variables with more than two ordered categories, unlike binary logistic regression which is limited to dichotomous outcomes. Its theoretical foundation is associated with the work of Peter McCullagh, while in practice it can be seen as a generalization of logistic regression. A typical example is the study of attitudes towards taxation, where participants are asked to respond on a four-point scale ranging from “strongly disagree” to “strongly agree.” In this case, the dependent variable is the response about taxation, while the independent variables might include age, income, or political affiliation.

Assumptions of the Model

In order to apply ordinal logistic regression, certain statistical assumptions must be met. First, the dependent variable must be clearly defined as ordinal. This means that the categories have a natural order, such as levels of satisfaction, obesity categories, or Likert-type scales. Second, the independent variables can be continuous, such as age or income, categorical, such as gender or occupation, or ordinal variables, which in SPSS must be defined either as continuous or categorical. Third, the model requires the absence of multicollinearity, meaning that the independent variables should not be highly correlated with each other, as this would compromise the accuracy and interpretability of the results. Fourth, the proportional odds assumption must be met. This assumption states that the effect of each independent variable is consistent across all thresholds of the dependent variable. In SPSS, this assumption is tested with the “Test of Parallel Lines,” which helps determine whether the model can be validly applied.

Procedure in SPSS

The analysis in SPSS is carried out mainly through the PLUM (Polytomous Universal Model) command. From the Analyze menu, under Regression, the user selects the Ordinal option. In the dialog box that opens, the dependent variable is placed in the “Dependent” field, while the independent variables are specified either as Factors if they are categorical, or as Covariates if they are continuous. Correct classification of variables is crucial to ensure accurate results. Next, the proportional odds assumption is tested using the “Test of Parallel Lines.” In addition, SPSS allows the researcher to save the predicted probabilities and predicted categories, which can later be used to assess the predictive power of the model. For more advanced needs, such as obtaining odds ratios and confidence intervals, the Output Management System (OMS) is employed. This extracts parameter estimates, which can then be transformed into odds ratios using simple syntax commands in the syntax editor.

Interpretation of Results

The interpretation of ordinal regression results in SPSS unfolds across several levels. First, the overall fit of the model is examined using measures such as Cox & Snell, Nagelkerke, and McFadden R², along with goodness-of-fit tests like Pearson and Deviance. Next, the statistical significance of independent variables is tested through the Wald test, which identifies which variables significantly influence the dependent variable. Of particular importance are the odds ratios. For categorical variables, odds ratios indicate whether one group is more or less likely to fall into a higher category of the dependent variable compared to the reference group. For continuous variables, the odds ratio shows how the probability of moving into a higher category changes with a one-unit increase in the variable. For example, it might be found that business owners are about twice as likely as non-business owners to believe that taxes are too high. Similarly, each one-year increase in age may be associated with higher odds of agreeing that taxes are excessive.

Applications

Ordinal logistic regression has applications across many fields. In the social sciences, it is used to study attitudes and perceptions in relation to demographic or social characteristics. In psychology, it is often applied to psychometric data collected using Likert scales. In public health, it is used to investigate issues such as obesity classification or satisfaction with healthcare services. In business and management, the method helps analyze customer satisfaction, consumer preferences, and overall experiences. The flexibility of ordinal regression allows not only the identification of which independent variables affect the dependent variable but also the estimation of the magnitude of their effect on the likelihood of moving into higher or lower categories of the outcome.

Conclusion

In summary, ordinal logistic regression with SPSS is an essential tool for analyzing ordinal data. Proper application requires careful adherence to assumptions, accurate setup of the SPSS procedure, and thoughtful interpretation of results. Through this analysis, researchers can identify statistically significant predictors, interpret odds ratios for both categorical and continuous variables, and evaluate the predictive ability of the model as a whole. This method provides a bridge between descriptive statistics and predictive modeling, offering valuable insights into complex social, psychological, and health-related phenomena.