I'm attempting to use continuous standardized reading data as a covariate in a latent class analysis where the dependent variables are standardized longitudinal math scores from 3rd to 6th grade. However, when I run the analysis I end up w/ all participants in the same latent class. When I run the same analysis with the reading scores transformed into ordinal categories (e.g., failing, proficient, advanced) the analysis seems to run fine. I'm not sure if I need change the syntax when I am using a continuous vs an ordinal covariate?
Variable: names are R6 M6 R5 M5 R4 M4 R3 M3 ID S G C R3L IEP S1871 S1874 S1875 S1876 S1877 IEP6 IEP5 IEP4 IEP3; usevar = R3 M3 M4 M5 M6; missing = all (-9); CLASSES = c(3); Analysis: type = MIXTURE; Model: %OVERALL% i s | M3@0M4@1M5@2M6@3; i-s@0; i s ON R3; c#1 ON R3; Output: sampstat standardized TECH1 TECH8 TECH11 TECH14; PLOT: SERIES = M3-M6 (s); TYPE = PLOT3;
Thank you for your response Dr. Muthen. Just to be clear the math and reading scores I am using are not z or transformed scores. The high-stakes reading and math scores have a M = 1300 and SD = 100. I do have the raw scores as well. What you are saying is to use the raw scores instead of the standard scores? Thanks.
Seltzer, Frank, Bryk (1994). The Metric Matters: The Sensitivity of Conclusions About Growth in Student Achievement to Choice of Metric. Educational Evaluation and Policy Analysis Spring 1994, Vol. 16, No. 1, pp. 41-49
Thanks for the reference Dr. Muthen. I created a new data set with the raw scores as suggested but am still getting the same result. After the syntax this is what appears:
*** WARNING in MODEL command All continuous latent variable covariances involving I have been fixed to 0 because the variance of I is fixed at 0. *** WARNING Data set contains cases with missing on x-variables. These cases were not included in the analysis. Number of cases with missing on x-variables: 214 2 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
No, I am just puzzled as to why the model predicts all members of the sample to belong to a single latent class when I am asking for 2 or 3 class solution. I get the result in my first post when I use an ordinal version of the reading covariate, but when I use the continuous version, using either standard or raw scores, all participants (N =1200) belong to the same class. I know that the variance of the reading scores is not zero, so I am not sure why the model is giving me so much trouble. I don't know if it is a coding error on my part or if I can use a continuous covariate in the latent class growth model?
I am sorry about the confusion. I'm new with MPlus and I am not sure if I am framing my questions with sufficient clarity.