This sort of analysis is definitely not in my field but I think you could use Missing Value Codes for this.
Using Consumer Preferences example dataset. First example without Missing Value Codes:
Categorical analysis as follows:
Categorical(
X(:Age Group),
Responses(:Job Satisfaction),
Crosstab Transposed(1),
Legend(0),
Test Response Homogeneity(1)
)
![jthi_0-1629127980364.png jthi_0-1629127980364.png](https://community.jmp.com/t5/image/serverpage/image-id/35073i39ADA89AD4A35A3B/image-size/medium?v=v2&px=400)
Then set Not at all satisfied as missing value code and rerun analysis:
![jthi_1-1629128032784.png jthi_1-1629128032784.png](https://community.jmp.com/t5/image/serverpage/image-id/35074i615FF1A6AA1ACBF1/image-size/medium?v=v2&px=400)
Missing Value Codes can be set from Column Properties:
![jthi_2-1629128088783.png jthi_2-1629128088783.png](https://community.jmp.com/t5/image/serverpage/image-id/35075i7F11556FD79DA664/image-size/medium?v=v2&px=400)
Or if you don't need those rows at all, you could exclude them totally by first selecting one of the values you want to exclude and choosing Select Matching Cells:
![jthi_3-1629128160913.png jthi_3-1629128160913.png](https://community.jmp.com/t5/image/serverpage/image-id/35076i9820DCAAB176E71C/image-size/medium?v=v2&px=400)
Then from the left side select one of the rows which you have selected and choose Hide and Exclude:
![jthi_4-1629128191293.png jthi_4-1629128191293.png](https://community.jmp.com/t5/image/serverpage/image-id/35077iF9C748B8B2C258D1/image-size/medium?v=v2&px=400)
and analysis to show how it affects the results:
![jthi_5-1629128228283.png jthi_5-1629128228283.png](https://community.jmp.com/t5/image/serverpage/image-id/35078i006AA769E11F4BAD/image-size/medium?v=v2&px=400)
-Jarmo