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I'm a bit confused on this matter because people suggest different approaches.

For instance, my experiment has 3 independent variables. All of them are continuous, but 2 are Likert scales outcomes (which are treated as continuous because I'm using the mean outcomes) ranging both 1 to 5. The third independent variable is also continuous but has a totally different range and it's not a Likert scale (range is 0 - 35400).

Moreover, my dependent variable is a count variable and I'm using a Negative Binomial model to test my hypothesis.

My question is: Should I Standardize my variables before I run the analysis?

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You don't have to. Two possible benefits spring to mind but they won't always be important so you have to decide for yourself.

  1. It can help with convergence. If you're having trouble with model convergence then standardising your variables can be helpful.

  2. It will make it easier to compare effect sizes of your independent variables.

The coefficient of a independent variable is the change in your response when the independent variable increases by 1. Even if a variable has a strong impact when it has a large range the coefficient may be misleadingly small. So if you want to say how important your 3rd variable is compared to the others it's easier if you standardise it.

This paper goes into lots more detail if you're interested:

Schielzeth, H. (2010). Simple means to improve the interpretability of regression coefficients. Methods in Ecology and Evolution, 1(2), 103–113. https://doi.org/10.1111/j.2041-210X.2010.00012.x

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