Should I be using the logistic regression or the categorical response module?

[#1]

I'm in JMP Pro 15.2.1.

I have 60 thousand observations, where the response is true/false and two categorical predictors: job, and group (assume they're not correlated). Many thousands of observations. I was about to do a logistic regression under the assumption that every observation was independent. But then I realized that each person had three consecutive response opportunities (one each quarter) and that their successive responses are not entirely independent from earlier responses.  I have a person ID for each response, so I know which responses belong to each person ID. 

My first attempt was simply to use the 'fit model' platform with a nominal logistic personality. But I wasn't sure how to handle the repeated measures generated by the 'case' variable. I could ignore case, and treat every observation as independent, but that's probably invalidating my test statistics. 

For repeated measures, I tried the multiple response module in analyze -> consumer research --> categorical -> related -> repeated measures. 'response' is my repeated measure, 'person' is my ID, and 'job' and 'group' are my X values. But when I run the model, I don't see any of the tests that I'd expect to see with logistic regression. Am I in the wrong module after all?

person	case	response	job	group
a	1	T	exec	blue
a	2	F	exec	blue
a	3	T	exec	blue
b	1	F	staff	blue
b	2	F	staff	blue
b	3	T	staff	blue
c	1	F	supt	orange
c	2	T	supt	orange
c	3	T	supt	orange

[#2]

Here is one thought. But I am assuming that the interest is to see (A) how responses are different by job and/or group, AND also (B) how responses change from previous case to next case. If your interest is not that, what I am going to describe may not be proper.

First step, take the subset of all observations whose case = 1. Analyze the subset using logistic regression and try to answer (A) given case = 1.

Second step, take the subset of all observations whose case = 2. Analyze the subset using logistic regression and try to answer (A) given case = 2. Then  add the response from case = 1 as a new explanatory variables. Analyze the new subset using logistic regression and try to answer (B) given case = 2, conditioning on the response for case = 1. If the previous response turns out significant, then it tells something out how previous response affects the later one.

So on so forth. As the step moves along, you may need to try adding either only the immediate previous response as explanatory variable, or all previous responses as explanatory variables, and see which matters.