The best way to show that a predictor is categorical?
Posted: Mon Aug 19, 2024 4:07 am
Hi there,
The power size is something I'm currently calculating with MLPowSim. Predictor x1 can be either a binary (1), a continuous (2), or an all MVN (3) type, and users need to indicate it in the model setup. Two things have come to mind:
Which of the three options given above is the best fit for a categorical predictor such as work satisfaction, which is measured on a 10-point Likert scalegeometry dash?
I was also wondering if you would think about taking ordinal variables and making them continuous (like a z-score) before choosing the binary choice (=1)? I would greatly appreciate your opinion on this matter.
Much obliged,
Whoopigoldberg
The power size is something I'm currently calculating with MLPowSim. Predictor x1 can be either a binary (1), a continuous (2), or an all MVN (3) type, and users need to indicate it in the model setup. Two things have come to mind:
Which of the three options given above is the best fit for a categorical predictor such as work satisfaction, which is measured on a 10-point Likert scalegeometry dash?
I was also wondering if you would think about taking ordinal variables and making them continuous (like a z-score) before choosing the binary choice (=1)? I would greatly appreciate your opinion on this matter.
Much obliged,
Whoopigoldberg