Message/Author 

Anonymous posted on Friday, April 19, 2002  9:41 am



Dear Linda & Bengt, I am wondering if it is legitimate to use a binary instead of a continuous variable as an outcome in LCA? Thanks. Best, Hanno 

bmuthen posted on Friday, April 19, 2002  6:11 pm



LCA is for categorical outcomes. LPA for continuous outcomes. Maybe you are thinking of something else? 

Anonymous posted on Monday, April 22, 2002  6:37 am



Dear Bengt, sorry for the misunderstanding. What I meant to ask was the following: In a LCA, where I use a certain set of categorical latent class indicators (u's), is it legitimate to use a categorical instead of continuous variables as outcomes (y's). Example 25.9B on page 268 uses a continous outcome. I am looking forward to your reply. Best, Hanno 

bmuthen posted on Monday, April 22, 2002  9:35 am



I see what you mean. Yes, you can use a categorical "distal outcome" instead of a continuous one. This distal is then simply yet another indicator of the latent class variable because there is no difference statisticallly between this and regressing the distal on class. When you have more than one categorical distal, however, you need to watch out for the fact that the 2 distals become independent given class and that is maybe not what you want. 

Anonymous posted on Monday, April 22, 2002  10:47 am



Dear Bengt, thank you for your reply. If I understand you correctly, the categorical outcome also needs to specified in the list of categorical variables. Additionally, instead of adding a line for the mean and variance (for continuous y's), I just provide start values for the threshold. Best, Hanno 

bmuthen posted on Tuesday, April 23, 2002  12:35 pm



Yes and yes. 

Anonymous posted on Tuesday, March 09, 2004  9:59 am



I would like to model two periods, where in period 1 a person can be in one of three mutually exclusive states(c1,c2,c3) and in the second period they could be in one of the three mutually exclusive states. My interest is in predicting class membership in period 2 given class membership in period 1 and covariates. Is this possible in Mplus? 

bmuthen posted on Wednesday, March 10, 2004  7:28 am



I think you are considering an unordered categorical (nominal) variable at two time points and want to relate these two variables. This can be done by letting the variables be represented by perfectly measured latent class variables, one at each time point. In Mplus 2.14 this is done by a single latent class variable with 3 x 3 = 9 classes (see paper #86 on the Mplus home page), while in the soon to be released Version 3 it is done by regressing the time 2 variable on the time 1 latent class variable. 

Anonymous posted on Wednesday, July 28, 2004  7:32 am



Dear Linda & Bengt, I would like to perform LCA, where each mixture is an IRT model (logistic regression model with random intercept). My question is whether Mplus is capable of handling such models. Best, Istvan 

BMuthen posted on Wednesday, July 28, 2004  9:01 am



Yes, this can be done in Mplus Version 3. Our experience to date shows that with binary observed variables, it can be hard to estimate such a model unless the mixture is very clear, while with ordered polytomous observed variables it is easier. 

Anonymous posted on Wednesday, July 28, 2004  9:27 am



Dear Bengt, thank you very much for your prompt answer. I actually would like to fit discrete mixture models, where each mixture is a 2PL model, with random person parameter, and compare the models with different restrictions. (My intention is to do it with both dichotomously and polytomously scored items. But in separate analyses, not mixing the two types of scoring.) Best, Istvan 

bmuthen posted on Thursday, July 29, 2004  8:21 am



It will be interesting to see how this works out. I am looking for good examples to illustrate these new methods, so please let me know of any successes. 

JISUN CHOI posted on Wednesday, August 25, 2010  12:40 pm



Dear Linda, Hello. I am interested in using Mplus to do a mixture regress analysis and have a couple of basic questions. 1. I saw the example 7.1(mixture regression analysis for a continuous dependent variable). My dependent variable is a binary variable. Can I test this model using a binary dependent variable instead continuous dependent variable? 2. I am also interested in looking at whether individuals in different latent classes vary in terms of their background (covariates  i.e. race and education level) on the relationship bewteen independent and dependent variable. Can I get profile information (i.e. frequency or portion)of these covariates associated with each latent class membership? or Can I get plot for the findings to see visibly different group membership? I will appreciate if you tell me some references about that. Best, JiSun 


1. See Example 7.3. 2. You can regress the latent class variable on a set of covariates to see which are related to class membership. You can also use the AUXILIARY (e) option. See the user's guide for details. I don't know how you would plot this information. 

JISUN CHOI posted on Thursday, August 26, 2010  10:28 am



Dear Linda, Thank you very much for responding very quick. Your response raised one more question. I saw Example 7.3. In my understanding I can do this latent class analysis if all dependent variables are binary that refer to binary latent class indicators. What I tried to do is a mixture regression analysis. I am interested in looking at the relationship between job satisfaction (a binary dependent variable) and several continuous covariates. I think it might be closer to Example 7.2. And, I thought that I can use logistic regression with a categorical latent variable. Is it possible? Thanks a lot for your time, advice, and suggestions. 


I'm sorry. You should just add the CATEGORICAL option to Example 9.1 if your dependent variable is binary. For this model it is difficult to have slopes vary across classes. You may only be able to allow intercepts to vary. 

JISUN CHOI posted on Thursday, August 26, 2010  1:02 pm



No problem. I guess you mean Example 7.1? Many thanks. 


Yes. 

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