Interpreting multinomial logistic results

  • Thread starter SantyClause
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In summary, a multinomial logistic regression was conducted on likert data from a survey sent to undergraduate students to rate their TA on various attributes. The regression included three variables: whether the TA was trained, whether the TA is international, and an interaction term trained*international. The odds ratio point estimates for these variables were 2.8, 6.9, and 0.5 respectively. The 2.8 indicates that being trained makes one 2.8 times more likely to not be in the reference group, and the 6.9 indicates that being international makes one 6.9 times more likely to not be in the reference group. However, the interpretation of the last coefficient is unclear without knowing the dependent variable
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SantyClause
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I ran a multinomial logistic regression on some likart data (1-5 survey response questions). For some background, the survey was sent to undergraduate students to rate their TA on various attributes.

Three of the variables are whether the TA was trained, whether the TA is international, AND an interaction term trained*international. For a particular comparison, I have odds ratio point estimates of 2.8, 6.9, and .5 respectively. I'm fairly certain the 2.8 indicates that being trained means you are 2.8 times more likely to not be in the reference group. Likewise, 6.9 times more likely to not be in the reference group if international. But how do I interpret the last coefficient?
 
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I'm sorry you are not generating any responses at the moment. Is there any additional information you can share with us? Any new findings?
 
  • #3
What is the dependent variable?
 

1. What is multinomial logistic regression?

Multinomial logistic regression is a statistical method used to predict the probability of an outcome with three or more categories. It is an extension of binary logistic regression, which is used for predicting outcomes with only two categories.

2. How do I interpret the results of a multinomial logistic regression?

The results of a multinomial logistic regression typically include coefficients, odds ratios, and p-values for each predictor variable. These values can be used to determine the strength and direction of the relationship between the predictor variables and the outcome categories. The odds ratio represents the change in odds of a particular outcome category for a one-unit increase in the predictor variable.

3. What is the difference between multinomial and ordinal logistic regression?

The main difference between multinomial and ordinal logistic regression is the type of outcome variable being predicted. Multinomial logistic regression is used when the outcome variable has three or more unordered categories, while ordinal logistic regression is used when the outcome variable has three or more ordered categories. Additionally, the assumptions and interpretation of the results may differ between the two methods.

4. How do I assess the overall fit of a multinomial logistic regression model?

There are several metrics that can be used to assess the overall fit of a multinomial logistic regression model, including the deviance, the Akaike Information Criterion (AIC), and the Bayesian Information Criterion (BIC). Lower values of these metrics indicate a better fit for the model.

5. What are some common challenges when interpreting multinomial logistic regression results?

Some common challenges when interpreting multinomial logistic regression results include collinearity between predictor variables, small sample sizes, and non-proportional odds assumptions. It is important to carefully consider these factors when interpreting the results and to use caution when making inferences from the model.

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