Centering a covariate –a continuous predictor variable–can make regression coefficients much more interpretable. That’s a big advantage, particularly when you have many coefficients to interpret. Or when you’ve included terms that are tricky to interpret, like interactions or quadratic terms.
For example, say you had one categorical predictor with 4 categories and one continuous covariate, plus an interaction between them.
First, you’ll notice that if you center your covariate at the mean, there is (more…)
No, degrees of freedom is not “having one foot out the door”!
Definitions are rarely very good at explaining the meaning of something. At least not in statistics. Degrees of freedom: “the number of independent values or quantities which can be assigned to a statistical distribution”.
This is no exception.
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The last, and sometimes hardest, step for running any statistical model is writing up results.
As with most other steps, this one is a bit more complicated for structural equation models than it is for simpler models like linear regression.
Any good statistical report includes enough information that someone else could replicate your results with your data.
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