Question: I was using respondent-driven sampling analysis tool (RDSAT) to get bootstrapping confidence intervals. But each time I re-did the analysis, I noticed the bootstrapping standard errors and confidence intervals changed a little bit.
Is this normal for bootstrapping confidence intervals? Thank you!

Answer: 
Bootstrapping involves resampling your data randomly. Thus, each time you bootstrap, a different (re)sample will be drawn. Therefore, the results of different bootstrap runs will be different.

If these differences are large, then you should be suspicious that your bootstrap may not be working well. If the differences are trivial, they are no problem.

You may want to set the seed value of your random number generator in order to make your bootstrap *exactly* replicable.
This is totally normal and why we set a random seed (to get the same randomization each time) via `set seed` in R or `np.random.seed` in Python.

The way bootstrap works is to take many random samples, with replacement, of your data, so there should be small fluctuations in your calculated values as those random samples vary.