Which methods are used to analyze nominal data with more than two categories?

Which methods are used to analyze nominal data with more than two categories?

• A. Cox regression
• B. Log-linear models or multinomial logistic regression
• C. Linear regression
• D. T-test

Answer: (B)

When the outcome is nominal with more than two categories, you need methods that handle multiple categories in the outcome rather than a single continuous or binary outcome. Two standard approaches are multinomial logistic regression and log-linear models. Multinomial logistic regression extends binary logistic regression to multiple categories by modeling the log-odds of each non-reference category relative to a chosen baseline. This allows you to estimate how predictors influence the probability of each category and obtain category-specific odds ratios. Log-linear models, on the other hand, focus on the relationships among categorical variables by modeling the log of expected cell counts in a contingency table. They’re particularly useful for testing independence and interactions between factors in multi-way tables, without directly predicting a specific category. Why the other options aren’t suitable: Cox regression is for time-to-event (survival) data, not nominal categories. Linear regression requires a continuous outcome and assumptions that don’t hold for categorical data. A t-test compares means between two groups and is meant for a continuous outcome, not a multi-category nominal outcome. So the best-fitting methods for analyzing nominal data with more than two categories are log-linear models or multinomial logistic regression.

When the outcome is nominal with more than two categories, you need methods that handle multiple categories in the outcome rather than a single continuous or binary outcome. Two standard approaches are multinomial logistic regression and log-linear models.

Multinomial logistic regression extends binary logistic regression to multiple categories by modeling the log-odds of each non-reference category relative to a chosen baseline. This allows you to estimate how predictors influence the probability of each category and obtain category-specific odds ratios.

Log-linear models, on the other hand, focus on the relationships among categorical variables by modeling the log of expected cell counts in a contingency table. They’re particularly useful for testing independence and interactions between factors in multi-way tables, without directly predicting a specific category.

Why the other options aren’t suitable: Cox regression is for time-to-event (survival) data, not nominal categories. Linear regression requires a continuous outcome and assumptions that don’t hold for categorical data. A t-test compares means between two groups and is meant for a continuous outcome, not a multi-category nominal outcome.

So the best-fitting methods for analyzing nominal data with more than two categories are log-linear models or multinomial logistic regression.