Hi All, I have been trying to run a PROC LCA with a sample of 4300 cases, however, I keep on running into issues where the model will not fit. It appears to be an issue of the sparseness of data, but I only have a couple of variables with missing data. Could it be because most of my variables are dummy variables? Any suggestions on how to fix this? Here's the code I used: PROC LCA DATA=raquel.homicide outest=raquel.homicidelca2a outparam=raquel.homicidelca2b outpost=raquel.homicidelca2c; NCLASS 2; ITEMS Age Age_Entry Schooling CR_B CR_H CR_W CR_M CR_F item1 item2 item3 item4 item5 item6 item7 item8 item9 item10 item11 item12 item13 item14 item15 item16 item17 item18 item19 item20 item21 item22 item23 item24 item25 item26 item27 item28 item29 item30 item31 item32 item33 item34 item35 item36 item37 item38 item39 item40 item41 item42 item43 item44 item45 item46 item47 item48 item49 item50 ; CATEGORIES 84 83 13 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2; ID idnumber; RHO PRIOR=1; Gamma Prior=1; SEED 648521; seed_draws 548961; RUN; And this is the error that keeps popping up: WARNING: The estimation engine was not able to fit the saturated model in order to adjust G-squared to account for missing data. This may be due to having a large number of response items. The G-squared, AIC and BIC fit statistics will NOT be provided in the output. WARNING: The estimation engine was not able to compute standard errors. Standard errors may not be supported for the kind of model and/or the kinds of parameter constraints which you are using, but may be available in future software releases. Please see the users' guide for details. Thanks!!!
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