

Questions about LCGA with continuous ... 

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Hello, I am running a LCGA on a continuous variable assessed at 7 consecutive time points. I have a few questions left after consulting the MPlus manual.  I have included only those participants who have filled out the assessments at time point 1 and at least one other time point. Is that sufficient, or should participants have filled out more than 2 measurement points?  The continuous variable that I use is left skewed with an abundance of scores equal to 0. Therefore I have tried to run a LCGA with a censored distribution. However, entropy values are quite less optimal with a censored model than with a "normal" model. For example: entropy in 3class "normal" model = 0,834; entropy in 3class censored model = 0,783. Should I consider using a noncensored model despite the fact that the continuous variable I use is left skewed?  In the Mplus syntax, do I have to state somewhere that my variable is a continuous variable? Right now, in the noncensored model I state nothing about the measurement level of my variable and in the censored model I only state "censored=eds17(b)". I can find commands in the manual for categorical variables, count variables etc but not for continuous variables. 


q1: You can use all people who have at least one time point. q2: I would use a censored model  or a twopart model  because it is more suitable for the data. The entropy consideration is secondary (you have to take what you get). q3: No, continuous is the default. 


Thank you very much for your quick reply! I have one additional question: through the syntax TECH7 I can request the statistics for each class. I am interested in the Mean scores for each time point in each of the classes, which indeed are given. But is there also a possibility to request accompanying standard deviations and confidence interval's for each of these time points, for each class? Thank you in advance! 


No. 


Okay, thank you. I am now deciding the optimal number of classes. I consulted BIC, LMRLRT, and BLRT. The BLRT is significant at the p<.001 level for all models I run (17 class models). The LMRLRT pvalue is .47 for the 4 versus 3 class model, but after that again becomes significant for the 5 versus 4 class model: p=.004. Does this mean that I could also explore the possibility of using the 5class model, or should I already have stopped after I found that the 4class model (compared to the 3class model) was not significant based on the LMRLRT value? (and should I as such stick with the 3class model?) Thank you in advance. 


You should stop when you first find the nonsignificance. So use 3 classes. 


Hello, I have another question about using a left skewed continuous dependent variable in GMM. The variable I use is obviously skewed to the left, but opposed to the variable that I was talking about above (post: posted on Wednesday, February 22, 2017  4:38 am), there is not an abundance of scores equal to 0. For the currenct variable, the values range from 026 with a median, mode, and interquartile range of 5 (which also indicates the skewness). For this variable, would it also be appropriate to use CENSORED = x1x3(b) to indicate that the scores are censored with a floor effect, even though for this variable there maybe isn't a "true" floor effect as there is not an abundance of scores equal to zero? Thank you in advance. 


No, don't use censored for this. Instead, use a skewt distribution for the outcome as shown in the paper on our website: Muthén, B. & Asparouhov T. (2015). Growth mixture modeling with nonnormal distributions. Statistics in Medicine, 34:6, 1041–1058. DOI: 10.1002/sim6388 


Thank you for your reply, this was very helpful. Would this syntax setup be correct? (mainly with regard to the DISTRIBUTION = SKEWT) or are there additional things that I should specify? VARIABLE: NAMES ARE HAPPYnr numero TPDSNA1 TPDSNA2 TPDSNA3; USEVAR = TPDSNA1TPDSNA3; MISSING = all (999); CLASSES = c(1); ANALYSIS: TYPE = MIXTURE; DISTRIBUTION = SKEWT; STARTS = 20 4; STITERATIONS = 10; MODEL: %OVERALL% i s  TPDSNA1@0 TPDSNA2@1 TPDSNA3@2; TPDSNA1TPDSNA3 (1); PLOT: SERIES = TPDSNA1TPDSNA3 (s); TYPE = PLOT3; 

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