! Comments follow exclamation points
! see: DeCarlo, L. T. (2002). A latent class extension of signal
! detection theory, with applications. Multivariate Behavioral
! Research, 37, 423-451.
TITLE: Ordinal response, multiple sclerosis data (Landis & Koch 1977);
DATA: FILE IS C:\mplus\files\landis.txt;
! NOTE: if data are in tabular form, they must be written out as individual
! records. This can be done using statistical packages (like SAS)
VARIABLE: NAMES ARE y1 y2;
CLASSES = class(2);
CATEGORICAL=y1-y2;
ANALYSIS: TYPE=MIXTURE; ESTIMATOR=ML;
! See Appendix B for some comments on how the model is parameterized
MODEL: %OVERALL%
f1 by y1@1; f2 by y2@1;
! Factors are included above to allow for non-zero means below
[y1$1*-1] (1); [y1$2*0] (2); [y1$3*1] (3);
[y2$1*-1] (4); [y2$2*0] (5); [y2$3*1] (6);
! The numbers in parentheses restrict the thresholds to be equal
! across the latent classes
%class#1%
[f1@0 f2@0];
%class#2%
[f1*1 f2*1];
! The above allows the means to be non-zero in one latent class.
! This gives the detection parameters
OUTPUT: sampstat tech1;