Binary Logistic Regression Assignment Help — Odds Ratios and SPSS Output

What Is Binary Logistic Regression and When Do You Use It?

Binary logistic regression predicts a two-category outcome — pass/fail, yes/no, disease/no disease — from one or more predictors. Use it whenever your outcome has exactly two categories; linear regression requires a continuous outcome and isn’t valid here.

Assumptions You Must Check Before Running It in SPSS

Dependent Variable Coding and Linearity of the Logit

Your outcome variable must be coded 0/1 — SPSS treats the higher-coded value as the “event” it’s predicting. Continuous predictors should have a roughly linear relationship with the log-odds of the outcome, formally testable with the Box-Tidwell procedure (often waived at coursework level, expected at dissertation level).

How to Run Binary Logistic Regression in SPSS (Step by Step)

1. Go to Analyze > Regression > Binary Logistic.
2. Move your 0/1-coded outcome into Dependent.
3. Move your predictors into Covariates.
4. Click Options, then tick Hosmer-Lemeshow goodness-of-fit and Classification plots.
5. Click Continue, then OK.

How to Interpret Binary Logistic Regression Output

Odds Ratios — Reading Exp(B)

Exp(B) is the odds ratio. Above 1 means increased odds of the outcome per 1-unit increase in the predictor; below 1 means decreased odds. An Exp(B) of 1.8 means each 1-unit increase in the predictor is associated with 80% higher odds of the outcome.

Model Fit — Hosmer-Lemeshow and Nagelkerke R²

The Hosmer-Lemeshow test works backwards from what you’d expect: a non-significant result (p > .05) indicates good fit. Nagelkerke R² is a pseudo-R² that approximates variance explained — it isn’t directly comparable to linear regression’s R².

The Classification Table — Sensitivity and Specificity

This table reports the overall percentage correctly classified, plus sensitivity (correctly identified positives) and specificity (correctly identified negatives).

How to Report Binary Logistic Regression Results in APA Format

Logistic regression was used to predict recovery from treatment adherence. The model was statistically significant, χ²(1) = 15.6, p < .001, and correctly classified 78% of cases. Treatment adherence was a significant predictor, B = 0.59, Wald χ²(1) = 9.87, p = .002, Exp(B) = 1.80.

What If Your Outcome Has More Than Two Categories?

Binary vs Multinomial/Ordinal Logistic Regression vs Linear Regression

This page covers exactly two outcome categories. If your outcome has three or more unordered categories, you need multinomial logistic regression; if the categories are ordered, you need ordinal logistic regression. If your outcome is continuous rather than categorical, use multiple linear regression instead. See the full SPSS statistical test guide to confirm which fits your data.

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