Multinomial and Ordinal Logistic Regression Assignment Help in SPSS

What Are Multinomial and Ordinal Logistic Regression, and When Do You Use Them?

Both extend binary logistic regression to outcomes with three or more categories. Use multinomial logistic regression when those categories have no natural order (e.g. choice of major: Psychology, Business, Engineering). Use ordinal logistic regression when the categories have a meaningful order (e.g. satisfaction: Low, Medium, High). Both predict category membership from one or more continuous or categorical predictors.

If your outcome has exactly two categories, use binary logistic regression instead. It’s simpler and more directly interpretable.

Assumptions You Must Check Before Running It in SPSS

• Independence of observations, as with any regression model.
• No severe multicollinearity among predictors.
• Multinomial regression: assumes independence of irrelevant alternatives (adding or removing a category shouldn’t change the relative odds between the remaining ones), rarely tested directly in student assignments but worth naming.
• Ordinal regression: the proportional odds (parallel lines) assumption. This is the assumption that trips up the most students: it requires that the relationship between each predictor and the outcome is consistent across all threshold levels of the ordinal categories. SPSS tests this with the Test of Parallel Lines, produced automatically in ordinal regression output.

How to Run It in SPSS (Step by Step)

Multinomial:

1. Go to Analyze > Regression > Multinomial Logistic.
2. Move your categorical outcome into Dependent, and choose a Reference Category (Reference Category button), typically the group you want every other category compared against.
3. Move predictors into Factor(s) (categorical) or Covariate(s) (continuous), then OK.

Ordinal:

1. Go to Analyze > Regression > Ordinal.
2. Move your ordered categorical outcome into Dependent.
3. Move predictors into Factor(s) or Covariate(s).
4. Click Output, check Test of parallel lines, then Continue > OK.

How to Interpret the Output

Multinomial: For each non-reference category, read the B, Wald statistic, Sig., and Exp(B) (odds ratio): each set of coefficients describes that category’s odds relative to the reference category specifically.

Ordinal: Check the Test of Parallel Lines first: a non-significant result (p > .05) supports the proportional odds assumption. If it’s significant, the model may need to be reconsidered (e.g. using multinomial regression instead, treating the outcome as unordered). If the assumption holds, read each predictor’s coefficient and its associated odds ratio (a single set applies across all threshold levels).

How to Report the Results in APA Format

Multinomial:

Compared to choosing Psychology, higher motivation scores significantly predicted choosing Business over Psychology, B = 0.42, Wald χ²(1) = 6.14, p = .013, Exp(B) = 1.52.

Ordinal:

The test of parallel lines was non-significant, χ²(4) = 5.02, p = .285, supporting the proportional odds assumption. Motivation significantly predicted higher satisfaction category, B = 0.38, Wald χ²(1) = 9.71, p = .002, Exp(B) = 1.46.

Multinomial vs Ordinal Logistic Regression: Understanding the Difference

The deciding factor is whether your outcome’s categories have a meaningful order. An unordered outcome (department chosen, brand preferred) needs multinomial regression, which estimates separate coefficients per category against a reference. An ordered outcome (low/medium/high, strongly disagree to strongly agree) needs ordinal regression, which assumes, and tests, that predictor effects are consistent across the ordering. Using multinomial regression on an ordinal outcome works but throws away the ordering information and produces a less efficient model; using ordinal regression on a genuinely unordered outcome produces a nonsensical model.

Not sure which one your outcome variable needs? See the full SPSS statistical test guide, or get help with this specific assignment.