Quantitative Results

In past blogs, we have discussed interpretation of binary logistic regressions, multinomial logistic regressions, and the more commonly used linear regressions. In this blog, we will discuss how to interpret the last common type of regression: ordinal logistic regression. It is important to note that, although there are other forms of regression out there, most of these are interpreted in the same way as the aforementioned types.

Although ordinal logistic regression involves some of the same steps of interpretation as the other methods, the interpretation of the individual predictors for ordinal regression can be a little tricky. If you have not already read up on the other common regression interpretations, make sure to give those a visit so you are caught up!

Aligning theoretical framework, gathering articles, synthesizing gaps, articulating a clear methodology and data plan, and writing about the theoretical and practical implications of your research are part of our comprehensive dissertation editing services.

- Bring dissertation editing expertise to chapters 1-5 in timely manner.
- Track all changes, then work with you to bring about scholarly writing.
- Ongoing support to address committee feedback, reducing revisions.

Like the past regressions we have discussed, the first step is to check the model fitting information and make sure the overall regression is significant. You can also investigate the Nagelkerke pseudo *R ^{2}*

Let’s take a look at an example where students are assessed for *College Readiness* (an ordinal dependent variable) and our predictors are MATH score and READING score. The dependent variable ranges from *low,* to *medium*, to *high *readiness. The threshold estimate assigned to *low *is 5, and the threshold assigned to *medium* is 10. This means that once a student hits the threshold of 5, they jump to the *medium* group, and once they hit 10, they are in the *high* group. You might see that MATH score is the only significant predictor, and the estimate assigned to this predictor is 2. This means that each increase of 1 point on the MATH score (the estimate is always based on a 1 unit increase in the predictor) tends to push students 2 points closer to the threshold. So, a student with a math score of 3 is expected to be in the *medium* group because they tend to move 2 units closer to the threshold for each additional point in MATH (2 units closer to threshold for each MATH point * 3 MATH points = 6). Because 6 is greater than the threshold of 5, that student has broken into the *medium* category. If their MATH score were 3 units higher (i.e., 6), we would see the following happen: (2 units closer to threshold for each MATH point * 6 MATH points = 12). This would push them past the threshold of 10 into the *high *group.

You can interpret each significant predictor this way, and even consider how close they get to each threshold based on a combination of predictors, so if READING were also significant, the addition of their score in reading might also help push them past the next threshold even if their math score just barely missed pushing them past the jump.