nav[aria-label="Primary Navigation"] { padding: 0; & ul { list-style: none; width: 100%; display: flex; flex-direction: row; justify-content: start; align-items: start; gap: 30px; padding: 0; & li { margin: 0; } & ul li { list-style: none; } } }

Continuous Variables Nested within a Categorical Variable?

angie_newbie

Hello, I am new to JMP and am not sure how to model my data so any suggestions on the steps I should take would be much appreciated!! I apologize in advance for my very limited knowledge of statistics.

I have a data set with a categorical variable (donor) and two parameters unique to each donor that are continuous (age and BMI). I would like to see how different response variables (also continuous - e.g. cell growth, etc.) are affected by a donor's age and BMI, and whether there is any interaction between age and BMI in determining the responses. How would you go about doing this? I have been using fit model to do this, at first with donor, age, and BMI all as separate inputs. It seemed like the results explained differences among the donors only, and did not elucidate the effects of age and BMI. When I used fit model with just age and BMI (excluded donor as a factor), the results showed regressions with the effects of both age and BMI. So it seems like there are two ways to model this; one where I can report which donors are significantly different from one another for each response, and one where I can report the effects of age and BMI on each response. I am not sure if I need to use the cross button in the second model to observe the interaction effects of age and BMI. I also don't know how to model all 3 independent variables at the same time. Do I need to nest age and BMI within donor? The support page for the nest option says that nested terms must be categorical.

Thank you so much, any feedback is much appreciated!!

Accepted answers

All comments

statman

Hi, welcome to the community. Is it possible for you to attach your JMP data table? You have an interesting situation...While you suggest that age is nested in donor, I don't think that is the only interpretation. When I think of nesting, I think of hierarchy. A variable nested inside another is contingent upon that variable. For example, you might have a component of variation which is batch-to-batch variation and another which is within batch variation. The within batch component is contingent upon batch (you can't get multiple batches from a within batch component). I don't think age is contingent upon donor...Another item you need to think about is multicollinearity. BMI and age might be collinear. In any case, when developing models to explain the variation in cell growth, you need to realize the analysis dpendst upon what terms are in the model. Change the terms in the model and the analysis likely changes. To combat this dilemma, I suggest you start with subject matter expertise, develop hypotheses (scientific not statistical) as to why the terms in the model would affect cell growth and then build the model from there. If you don't have hypotheses, then saturate the model and remove terms to get an appropriate model.

statman

Thought about your situation a bit more...seems to me age and BMI are confounded with donor, so you can't have all terms in the model.

angie_newbie

Hi @statman. Thank you for the clarification on the nature of nesting and your thoughts on my problem. I cannot share the data since it is proprietary information. However, I can share a completely fabricated dataset that is totally unrelated but conveys the same problem I'm grappling with. You are definitely right about confounding variables - it is impossible to uncouple age and BMI from donor, and I think that is why the output was strange when I added all three to the model. I ran the fit model again with just age and BMI (excluded donor), and age crossed with BMI, and saturated the model like you suggested with all of the other inputs that might garner a response. I removed each one that was not significant and was able to observe the interaction between age and BMI in determining their effects on each response. This seems appropriate in observing the relationship between age and BMI for each response, but now I am unsure if I need to run donor vs. each response in a separate model to determine which donors are significantly different from one another for each response. The first model with all 3 inputs gave me the results for an ANOVA, but does not give me the option to run a Tukey test, whereas running donor as a sole input does. Do you think running two separate analyses to examine the effects of all 3 inputs is necessary? Please let me know if you'd like a silly fake dataset. Thanks so much for all your help!

angie_newbie

On another note...I'm also having trouble determining the appropriateness of a model. If the residuals for a regression do not appear to have a normal distribution, should the model be discounted? Thanks.