Thank you very much for your answer! I have tried the same command as you and got approximately the same results (deviance 7951, AIC 8093 for both approaches). The command I have used is quite similar to what is stated in tutorial 7. I have tried to rerun the analyses using my command. The three-dimensional approach without Monte Carlo estimation runs without any problems. With Monte Carlo I got an error-message: ‘a dimension index is out of range for a regression initial value 4. Number of dimensions is 3’. I have use the following command:

title test_befolkning;

datafile befolkningCQ.dat;

format PID 1-6 responses 7-53;

codes 1,2,3,4;

score (1,2,3,4) (0,1,2,3) () () !items (1-16);

score (1,2,3,4) () (0,1,2,3) () !items (17-31);

score (1,2,3,4) () () (0,1,2,3) !items (32-47);

model item + item*step;

set warnings=no,update=yes;

export parameters >> befolkningCQ.prm;

export reg_coefficients >> befolkningCQ.reg;

export covariance >> befolkningCQ.cov;

import init_parameters << befolkningCQ.prm;

import init_reg_coefficients << befolkningCQ.reg;

import init_covariance << befolkningCQ.cov;

estimate!method=montecarlo,nodes=2000,converge=.005;

show cases !pfit=yes,estimates=wle,filetype=xls>> befolkningCQ _PersonFit.xls;

show !filetype=xls, estimates=mle,tables=1:2:3:4 >> befolkningCQ _ItemFit.xls;

itanal >> befolkningCQ _tradAnalyses.xls;

quit;