Hi all,

I'm exploring an LCA model with a household survey we've ran. This is looking at the activities that citizens do in their day-to-day lives (e.g. sports they play, technology they have access to, etc.). Each question has ordinal answers, as respondents answered on a Likert scale (e.g. 'I run every day', 'I run a few times a week', 'I run every month', ' I don't run', etc.).

I think there's value in exploring this data through an LCA approach to see if there is some framing we can provide beyond analysing the 2,000 responses in the aggregate. I've just started on this, and used the below code:

Code:
. gsem (Internet Phone Skilllevel Ownbusiness Gender <-), ologit lclass(C 3)
And received this error:

Code:
 convergence not achieved
Any thoughts or feedback would be welcome. I've included a data excerpt below. Thank you!

Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input int Respondent byte(COVID19impact Internetaccess Phonepackage Reading Cycling Running Kayaking Skating Surfing Basketball Football Soccer Baseball Hiking Skilllevel Ownbusiness) int Age byte(Gender Education Urban)
  89 2 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 70 3 6 0
 164 3 0 0 1 1 1 0 0 1 1 1 0 0 1 3 1 61 2 9 2
 754 2 0 1 1 0 1 0 0 1 1 0 0 0 0 0 0 59 3 2 2
 763 3 0 1 1 0 1 1 1 1 1 1 0 1 0 1 0 37 3 2 3
 181 3 0 1 0 1 1 1 1 1 1 1 0 0 0 1 1 62 3 6 3
 657 3 0 1 0 1 1 0 0 1 1 1 0 1 0 1 1 64 3 4 2
 221 2 0 1 0 0 0 0 0 1 0 0 0 1 0 1 1 50 3 2 2
1025 3 0 1 0 1 1 0 0 1 1 1 0 0 0 1 2 33 3 4 3
1215 3 0 1 0 0 1 0 1 1 0 1 0 0 1 2 0 21 3 4 0
 769 3 0 1 0 1 1 1 1 1 1 0 0 0 0 2 0 31 3 5 1
 190 1 0 1 0 1 0 0 0 0 0 0 0 0 0 2 0 61 3 8 0
 267 3 0 1 0 1 1 0 0 1 1 1 0 1 0 2 0 40 3 2 2
 276 3 0 1 0 1 0 0 0 1 1 0 0 0 1 2 1 36 0 8 1
 384 2 0 1 0 1 1 1 1 1 1 1 1 0 0 2 1 56 2 4 3
 397 2 0 1 0 0 1 0 0 0 0 0 0 0 1 2 1 50 2 4 0
 544 3 0 1 0 0 1 1 1 0 1 0 0 1 0 2 1 57 3 2 2
 641 1 0 1 0 0 0 0 0 1 0 0 0 0 0 2 1 70 3 3 0
 377 3 0 1 0 1 1 0 0 1 1 1 0 1 0 2 1 26 3 2 2
 309 3 0 1 0 1 1 1 1 1 0 1 0 1 1 2 1 33 3 4 1
 102 2 0 1 0 0 0 0 0 0 0 0 0 0 1 2 1 20 3 9 1
1136 3 0 1 1 1 1 1 1 1 1 1 0 0 1 2 1 23 3 9 0
 903 1 0 1 0 0 0 0 0 0 1 1 0 1 0 2 1 50 3 9 0
 235 0 0 1 0 0 0 0 0 1 0 0 0 0 0 2 1 46 3 8 1
 183 2 0 1 0 1 0 0 0 0 0 0 0 0 0 2 2 59 2 3 1
1278 3 0 1 0 1 1 0 0 1 1 0 0 0 0 2 2 67 2 3 1
 307 3 0 1 0 0 1 1 1 1 1 1 0 1 1 2 2 49 3 8 2
 897 3 0 1 1 1 1 1 1 1 1 1 0 1 1 3 0 20 0 4 0
 418 2 0 1 0 0 1 0 0 0 0 0 0 0 0 3 0 23 0 9 0
1076 3 0 1 0 1 1 0 0 1 0 0 0 0 0 3 0 67 2 5 3
 365 2 0 1 0 0 1 1 0 0 0 1 0 0 0 3 0 23 2 4 2
 391 2 0 1 1 1 1 1 1 0 1 0 0 0 1 3 0 52 2 4 2
1113 2 0 1 1 1 1 1 1 1 1 1 1 1 1 3 0 21 2 2 0
1184 2 0 1 0 1 1 0 0 1 1 1 0 1 0 3 0 26 3 9 2
1269 3 0 1 0 1 1 0 0 1 1 1 0 0 0 3 0 23 3 4 2
  65 3 0 1 0 1 1 1 1 1 1 1 0 0 1 3 0 39 3 5 2
 131 3 0 1 1 1 1 1 1 1 1 1 1 0 1 3 0 52 3 6 3
1045 3 0 1 1 0 1 0 0 1 0 1 0 1 0 3 0 23 3 4 0
 717 3 0 1 1 0 1 1 1 1 0 1 0 1 1 3 0 18 3 2 2
 726 3 0 1 0 1 1 1 1 1 0 1 0 0 0 3 0 44 3 3 1
 996 3 0 1 1 1 1 0 1 1 1 0 0 0 0 3 1 45 2 8 3
1262 3 0 1 0 0 0 0 0 0 0 1 0 0 0 3 1 22 2 4 1
 500 3 0 1 1 1 1 0 0 1 1 1 0 0 1 3 1 43 3 2 0
1255 3 0 1 1 1 1 0 0 1 1 1 0 0 1 3 1 45 3 2 2
 472 3 0 1 1 1 1 1 1 1 1 1 1 0 0 3 1 59 3 2 2
 312 3 0 1 0 0 1 0 1 1 0 1 0 1 0 3 1 48 3 3 3
 925 2 0 1 1 0 1 1 1 0 0 0 0 1 1 3 1 30 3 8 2
 841 3 0 1 1 1 1 0 1 1 0 1 1 0 0 3 1 26 3 3 2
1284 2 0 1 1 0 1 1 1 1 1 1 0 0 1 3 1 23 3 4 0
 330 3 0 1 0 0 0 0 0 0 0 0 0 0 1 3 1 34 3 2 3
 367 3 0 1 0 0 1 1 1 1 1 1 0 1 0 3 1 29 3 2 3
 980 3 0 1 1 1 1 1 1 1 0 1 0 0 1 3 2 52 3 5 2
1250 3 0 1 1 1 1 1 1 1 0 0 0 0 0 3 2 34 3 4 2
 341 1 0 1 0 1 1 0 0 1 0 0 0 0 0 3 2 63 3 6 3
 253 2 0 1 0 1 1 0 1 1 1 1 1 0 0 3 3 48 3 5 1
 479 3 0 2 0 0 1 0 0 0 0 0 0 0 0 0 1 54 3 2 3
 757 3 0 2 0 0 1 1 1 0 1 1 0 0 0 1 0 55 0 9 2
1311 3 0 2 1 1 1 0 0 1 1 1 0 0 0 1 0 54 3 4 2
 439 3 0 2 0 0 0 0 0 1 0 1 0 0 0 1 0 69 3 6 1
1109 1 0 2 0 0 0 0 0 0 1 0 0 0 0 1 1 65 0 9 0
 378 2 0 2 0 0 1 0 0 0 0 0 0 0 0 1 1 76 2 2 0
 557 1 0 2 0 0 1 1 0 0 0 1 0 0 0 1 1 48 2 2 2
 578 2 0 2 1 1 0 1 1 1 1 1 0 0 1 2 0 48 2 3 3
 394 2 0 2 0 0 0 0 0 1 1 1 0 0 0 2 0 70 2 5 1
 579 3 0 2 0 0 1 1 0 1 1 1 0 1 0 2 0 45 3 4 2
1168 3 0 2 0 1 1 0 0 1 1 1 0 0 1 2 0 61 3 5 3
 547 3 0 2 0 0 0 0 0 0 0 0 0 0 1 2 0 64 3 8 2
1103 3 0 2 0 0 1 0 0 1 1 0 0 0 1 2 0 43 3 6 3
 698 2 0 2 1 1 1 0 0 1 1 1 0 1 0 2 0 55 3 4 1
 430 3 0 2 0 1 1 1 0 1 1 1 1 0 0 2 0 57 3 2 0
 619 3 0 2 1 1 1 1 1 1 0 1 0 0 0 2 0 69 3 4 2
1087 3 0 2 0 0 0 0 0 0 0 0 0 0 1 2 0 39 3 6 3
1241 3 0 2 1 1 1 1 0 1 1 1 0 0 0 2 0 59 3 4 1
 502 3 0 2 0 1 1 0 0 1 1 0 0 0 0 2 0 36 3 2 2
 401 3 0 2 0 0 1 1 0 1 1 1 1 0 1 2 0 48 3 3 1
1181 3 0 2 1 1 1 0 0 1 0 1 0 0 0 2 0 52 3 2 0
 420 3 0 2 0 0 0 1 0 1 1 1 0 0 0 2 1 57 2 4 0
 353 3 0 2 0 0 1 0 0 0 0 0 0 0 0 2 1 59 2 9 0
 871 3 0 2 1 1 1 1 1 1 1 0 0 0 0 2 1 44 2 6 1
 118 3 0 2 1 1 1 1 1 1 1 1 1 1 0 2 1 62 2 2 2
 513 2 0 2 1 1 1 1 1 1 1 1 0 0 0 2 1 50 3 4 3
 703 2 0 2 1 1 1 1 1 1 1 1 1 1 1 2 1 43 3 6 0
1052 3 0 2 0 0 0 0 0 0 0 0 0 0 1 2 1 22 3 2 1
 902 2 0 2 1 1 1 1 1 1 1 1 1 1 1 2 1 60 3 6 2
 753 3 0 2 0 0 1 0 0 1 1 0 0 0 0 2 1 56 3 2 2
 847 2 0 2 0 0 1 0 0 1 1 1 0 0 0 2 1 49 3 1 2
 151 3 0 2 0 1 0 0 0 0 1 1 0 1 0 2 1 50 3 8 1
 179 3 0 2 0 0 0 0 1 1 0 1 0 1 0 2 1 60 3 3 2
1308 3 0 2 1 0 0 1 1 1 1 1 0 0 1 2 1 40 3 2 3
1194 3 0 2 0 0 1 1 0 0 1 1 1 1 0 2 2 41 3 4 2
1135 3 0 2 1 1 1 0 1 1 0 0 0 0 0 3 0 39 2 3 1
 419 3 0 2 0 1 1 1 1 1 1 1 1 0 0 3 0 63 2 4 3
1231 2 0 2 1 1 1 1 1 1 1 1 1 1 1 3 0 22 2 8 2
 994 3 0 2 0 0 1 1 1 1 0 1 0 1 0 3 0 28 2 2 2
 822 3 0 2 0 0 1 1 1 1 0 1 0 1 0 3 0 23 2 5 2
 740 3 0 2 0 1 1 0 1 1 1 1 1 0 1 3 0 42 2 4 3
1046 2 0 2 0 0 1 0 0 0 1 1 0 0 0 3 0 71 2 1 0
1301 3 0 2 0 0 1 1 1 1 1 1 0 1 0 3 0 20 2 8 2
 316 3 0 2 1 1 0 1 0 1 0 1 0 1 0 3 0 69 3 8 1
 632 3 0 2 1 1 0 0 0 0 0 0 0 0 0 3 0 50 3 5 3
 264 3 0 2 0 1 0 0 0 1 1 1 0 0 0 3 0 72 3 2 2
end