Interpreting Models with Multiple Interaction Terms: Gender, Weight, and Height

In summary, the conversation discusses the interpretation of models with multiple interaction terms. It is stated that the interpretation of the model with two interactions would be the same as a model with only one interaction. However, having multiple interactions may complicate the interpretability. Additionally, the power may be higher in a model with two interactions, but it may also introduce confounding variables. The conversation also includes an example of interpreting the effect of weight across genders, where it is suggested to fix the height to avoid confounding.
  • #1
FallenApple
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If there are two interaction terms in a single model, does that mess up the interpretation of it? For example, Gender*Weight and Gender*Height.

Say the model is Y~Weight+Height +Gender +Gender*Weight+Gender*Height.

Would I simply interpret it as usual? That is, "The difference in mean response for a one unit increase in weight differs between the genders by the value of the interaction coefficient between weight and gender for a subpopulation of people with Height=some fixed value"?I've heard that having multiple interactions isn't good because it might complicate the interpetability. I'm not sure how though which is why I'm asking.

Also, isn't the power higher in a model with two interaction if there are in fact two interactions vs having a separate model for each interaction term?
 
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  • #2
My understanding is that, if the fitted coefficients are B1 to B5 from left to right (ignoring the constant term B0) the interpretation is as follows:

sensitivity of Y to Weight is (B1 + B4 * Gender), so the sensitivity varies by Gender
sensitivity of Y to Height is (B2 + B5 * Gender), so the sensitivity varies by Gender
sensitivity of Y to Gender is (B3 + B4 * Weight + B5 * Height), so the sensitivity varies by Weight and Height

The interaction terms tell us the sensitivities of the sensitivities.
 
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  • #3
andrewkirk said:
My understanding is that, if the fitted coefficients are B1 to B5 from left to right (ignoring the constant term B0) the interpretation is as follows:

sensitivity of Y to Weight is (B1 + B4 * Gender), so the sensitivity varies by Gender
sensitivity of Y to Height is (B2 + B5 * Gender), so the sensitivity varies by Gender
sensitivity of Y to Gender is (B3 + B4 * Weight + B5 * Height), so the sensitivity varies by Weight and Height

The interaction terms tell us the sensitivities of the sensitivities.

Got it. Now to interpret say effect of weight across gender, would I have to fix the height? From a mathematical point of view, I could just fix it and it would drop out. But it could be a confounder so I'm not sure.

if height is x=a

then

B3 + B4 * Weight + B5 * a

Then a unit difference in weight gives rise to B4. So holding weight fixed, the difference in mean response for unit increase in weight is B4. Then height seemingly does matter.
 

1. What are interaction terms in a statistical model?

Interaction terms in a statistical model refer to the variables that are multiplied together to examine the effects of their combined values on the outcome. In this case, the interaction terms are gender, weight, and height.

2. Why is it important to include interaction terms in a model?

Including interaction terms in a model allows us to understand how the relationship between different variables changes based on their combined values. It can help us identify unique patterns and better explain the data.

3. How do we interpret the coefficients of interaction terms in a model?

The coefficients of interaction terms represent the change in the outcome variable for a one-unit increase in the corresponding variables. For example, if the coefficient for the interaction between gender and weight is 0.5, it means that for every one unit increase in weight, the effect on the outcome variable is 0.5 units higher for females compared to males.

4. Can we have more than three interaction terms in a model?

Yes, it is possible to have more than three interaction terms in a model. However, it is important to consider the sample size and the complexity of the model. Including too many interaction terms can lead to overfitting and reduce the generalizability of the results.

5. How do we determine the significance of interaction terms in a model?

The significance of interaction terms can be determined by looking at the p-value associated with the coefficient. A p-value less than the chosen significance level (usually 0.05) indicates that the interaction term is significant and should be included in the model.

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