LTA with continuous indicators PreviousNext
Mplus Discussion > Growth Modeling of Longitudinal Data >
Message/Author
 Xiao-min Li posted on Sunday, November 16, 2014 - 7:47 am
Dear Doctors:
I'm a Chinese student majoring in psychology in Beijing Normal University and now I am writing message to ask for help.
My graduate teacher wants me to analyze 2-wave longitudinal data (all variables are continuous) to explore behavior patterns and their transition across time. I have download data and syntax examples provided by official website but just found that all examples are about categorical variables. Im wondering that whether I can run LTA with continuous variable. And if it is ok, following are some questions puzzling:
1. Whether estimation method or other specification should be changed in contrast to categorical data.
2. According to examples, number of category should be set in advance, which parameter in output can I use to test its appropriateness except for BIC and AIC for my graduate teacher ask me to provide more evidence;
3. In Latent Class Analysis, savedata syntax can save cprob to help researchers make sure the class each individual belongs to, so is it also possible in latent transition analysis? If possible, how to write the syntax?
That's all. Thanks for your patience and kindness in advance.
Best regards!
 Bengt O. Muthen posted on Sunday, November 16, 2014 - 11:50 am
Yes, LTA can also use continuous variables as latent class indicators. In fact, all variable types are allowed, including latent variables, and combinations.

1. No.

2. Apart from BIC you can also use the RESIDUAL option of the OUTPUT command.

3. Try the same approach as for LCA.
 Xiao-min Li posted on Sunday, November 16, 2014 - 5:17 pm
thanks for your answering, I adapt the syntax of Hidden Markov model to the following:
ANALYSIS: ALGORITHM=INTEGRATION;
TYPE=MIXTURE;
MODEL:
%OVERALL%
c2 on c1;
MODEL c1: %c1#1%
[h11$1] (1);
[h12$1] (2);
[h13$1] (3);
%c1#2%
[h11$1] (6);
[h12$1] (7);
[h13$1] (8);
%c1#3%
[h11$1] (11);
[h12$1] (12);
[h13$1] (13);

MODEL c2: %c2#1%
[h21$1] (1);
[h22$1] (2);
[h23$1] (3);
%c2#2%
[h21$1] (6);
[h22$1] (7);
[h23$1] (8);
%c2#3%
[h21$1] (11);
[h22$1] (12);
[h23$1] (13);


OUTPUT: TECH1 TECH8 TECH11 TECH14 TECH15

But warning is output:
*** WARNING in MODEL command
All variables are uncorrelated with all other variables within class.
Check that this is what is intended.
*** ERROR
The following MODEL statements are ignored:
* Statements in Class %C1#1% of MODEL C1:
[ H11$1 ]

Thanks!
 Bengt O. Muthen posted on Monday, November 17, 2014 - 7:56 am
You don't use the $1 notation with continuous variables.
 Xiao-min Li posted on Monday, November 17, 2014 - 6:15 pm
Got it, and model executed successfully, thanks very much!
 Xiao-min Li posted on Monday, November 17, 2014 - 6:43 pm
One more question, I-state objects analysis (ISOA) can also be endorsed to explore transition of pattern across time? Is there any difference between ISOA and LTA? In assumption or output? Thanks!
 Linda K. Muthen posted on Tuesday, November 18, 2014 - 7:38 am
I don't know what ISOA is. Do you have a reference that describes it?
 Xiao-min Li posted on Tuesday, November 18, 2014 - 6:26 pm
L. R. Bergman, B. M. El-Khouri (1999) Studying Individual Patterns of Development Using I-States as Objects Analysis (ISOA)Biometrical Journal 41 (1999) 6, 753-770.
Thanks!
 Bengt O. Muthen posted on Wednesday, November 19, 2014 - 12:22 pm
I glanced at the article and although there are similarities in goals, there seems to be many differences in methodology between ISOA and LTA. For one, ISOA uses cluster analysis whereas LTA uses mixture modeling. But I can't say more about ISOA since I have not studied it.
 Xiao-min Li posted on Thursday, November 20, 2014 - 4:05 am
Thanks a lot!
 weiyi cheng posted on Wednesday, January 25, 2017 - 8:07 pm
Hi, I'm running a LTA model with 3 classes at two time points. Somehow, I got same transition probabilities for each time 1 class category. That can't be right I suppose? What might cause this then? I appreciate your insight!

C1 Classes (Rows) by C2 Classes (Columns)

1 2 3

1 0.369 0.403 0.228
2 0.369 0.403 0.228
3 0.369 0.403 0.228
 Bengt O. Muthen posted on Thursday, January 26, 2017 - 3:54 pm
We need to see your full output to say. Please send to Support along with your license number.
 Anshuman Sharma posted on Saturday, October 07, 2017 - 8:15 am
Hi Dr. Muthen,

My objective is to carry out LTA with continuous variables as latent class indicators. I have performed all the three steps as per webnote 15 (Subheading - "Estimating latent transition analysis using the 3-step method"). I can't see any other model fit information except AIC and BIC in the output of the last step.


VARIABLE:NAMES ARE y1-y8 cl1 cl2;
USEVARIABLES ARE cl1 cl2;
CLASSES = c1(3) c2(3);
Nominal are cl1 cl2;

(Only showing a part of model commands just to let you know about the classes)

My questions are:
How can I see any other model fit information?
Also, if you could please suggest, is it important to consider model fit information in step 3 when I have already assessed the model fit of latent classes (c1 and c2 separately) in previous steps.

Thanks and regards,
Anshuman
 Bengt O. Muthen posted on Saturday, October 07, 2017 - 12:50 pm
Q1: There are not really any very useful fit statistics for continuous mixture modeling like LTA. Instead you have to work with a series of model and compare them using BIC. That is, models that differ by e.g. the number of classes or differ by imposing and not imposing measurement invariance, etc.

Q2: It could be useful (by using neighboring models) but perhaps not necessary.
 Anshuman Sharma posted on Saturday, October 07, 2017 - 3:22 pm
Thank you so much for your reply
Prof. Muthen.

Regards,
Anshuman
Back to top
Add Your Message Here
Post:
Username: Posting Information:
This is a private posting area. Only registered users and moderators may post messages here.
Password:
Options: Enable HTML code in message
Automatically activate URLs in message
Action: