Creating Confidence intervals via bootstrapping

I want to calculate confidence Intervals using bootstrapping. I am trying to estimate three parameters in a model via maximum likelihood estimation (MLE). I am able to do this but now I need to find my CI’s. My problem is that I am unsure on how to actually perform bootstrapping ? I have written my own code for the parameter estimation.

The data is in the binary format of 1’s and 0’s. And it’s from those data (put into a model with three parameters) that I have tried to estimate the parameter values.

So let’s say my cohort is 500, is the idea then that I take a sample from my cohort, maybe 100, and then expand it to 500 again by just multiplying the sample 5 times, and run the simulation once again, which in turn should result in some new parameter estimates, and then just do this 1000-2000 times in order to get a series of parameter values, which can then be used to define the CI ?

Can anybody tell if I am lacking something in my approach?

So the idea is not to sample 100. You must sample with replacement and taking the same sample size (500). After this you re-estimate your parameter many times. And then there’s several ways of taking all of these estimates and turning them into a confidence interval. For example, you can use them to estimate the standard error (the standard deviation of the sampling distribution), and then use (+/- 2*se).