Introduction to Latent Class Analysis in Mplus
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- Опубліковано 20 тра 2024
- This presentation will introduce Latent Class Analysis (LCA) and its implementation in Mplus. LCA, a latent variable modeling approach, is used to classify people into groups that are similar on unobserved constructs, based on their response patterns. In LCA, group membership is unseen and is indicated by observed variables. LCA is a model-based approach and thus the results from LCA can be replicated in other samples. LCA provides researchers valuable insights into the various types of respondents and how to better construct future intervention strategies targeting different types of respondents. During the presentation, we will talk about the overview of LCA and learn how to conduct LCA in Mplus step-by-step through an example (data/syntax/output).
Visit education.uky.edu/edp/apslab/... to download the Handout and Mplus Files for this talk.
Recently during my own research, I found some useful information for people who are doing similar research using LCA:
1. When saving the new dataset including conditional probability and class membership, if you would like to save other variables that are irrelevant to the LCA, such as an ID variable, under the Variable command, we can add one line: auxiliary = id; This will tell Mplus in the new dataset please save the ID variable.
In the new dataset, the order of the variables are: variables used to predict class, ID, conditional probabilities, class membership.
2. Another useful book to refer to for doing LCA: Data Analysis with Mplus by Christian Geiser, Chapter 6
3. Multilevel LCA is introduced by a paper by Kimberly L. Henry and Bengt Muthen with the title "Multilevel latent class analysis: An application of adolescent smoking typologies with individual and contextual predictors.
Thanks for sharing! As you said in the lecture that LCA is similar to factor analysis, but is person-centered. I am wondering does an LCA have to be one-factor structure? To be more specific, doing a factor analysis a model can have multiple factors, but it seems that LCA only generates one factor, in which there can be 2, 3, 4, or 5 values (as the example you showed in the lecture).
Not to my knowledge. In LCA we use all indicators or items to identify the 2 + latent classes/groups. However, you might want to check out cognitive diagnostic modeling techniques (see works by Jonathan Templin and his colleagues). You might also find mixture IRT of interest (see a recent how to on Mixture IRT by Ralph De Ayala).
I just start to learn Mplus, would you mind sharing some good materials or examples of using Mplus for LCA? Thanks
Thanks for sharing. I was wondering how I could include an ID variable.
This is pretty clear and step-by-step learning. Excellent resources for beginners. Thank you!
This must be the best lecture on the topic!!! Very clear -- this will safe me much time!
Very clear lecture! I would really appreciate it if there could be more lectures like this on other topics.
clear enough to let me understand, thanks
Thanks Caihong, your presentation was easy to understand and helpful.
Tremendously helpful. Thank you for sharing knowledge. Such a strong motivation for me that she was also a candidate when she was giving this great presentation!
This was excellent! So helpful and clear.
Thank you for the concise and informative video! :)
Great work indeed. Please also add videos of EFA, CFA and SEM with all kinds of variables .
Great lecture. Very useful.
Thank you very much for sharing!
Great job! A very clear and useful presentation. Thanks!
We are so glad to hear you enjoyed our presentation on LCA. We hope to have more in the future and are open to suggestions.
Thank you much for providing all the information that I need for my research
Thank you so much. This helped me greatly with my thesis. :)
I am glad to hear this helped you with your thesis. This is exactly the sort of reason we created these videos. Good luck on finishing your thesis.
great lecture! thanks a lot:)
Thank you so much. It was very helpful. :))
thake you is very good
This was a really easy to follow presentation. Thank you! Would it be possible to get this or a similar data set to practice? At the moment, I can only use the demo version of Mplus, which can't take more than 6 variables and all the example data sets that I've found online had more than 6.
Thank you for this super clear lecture -- I am including both continuous and categorical variables in my analyses. My outcome show that 3 classes suit the data best (and seem theoretically feasible) but I don't understand how to test if the mean differences across latent classes are significant. I understand that the "Model test" function is suppost to test this using a wald statistic but I am not entirely sure how to set up the syntax. Do you happen to have a tip?
Thanks for Presenation. Is it correct that memebership in a class is given by k-nary logistic regression?
Thank you so much for the video! It was a great help.
I have a sample of 455 families and I wanted to build a latent class regarding the quality of the young-mother relationship informed by both perspectives. Given the sample size, is it advisable to consider another type of analysis? Let me know your thoughts about that.
Thanks
I'm so glad that you found this video of great help.
Regards to your question, it depends on a lot of variables. Your sample size for LCA is not quite that large but it might be amendable to using LCA. I recommend you read The study titled effect size, statistical power and sample size requirements for the bootstrap likelihood ratio test in latent class analysis. The paper was published in structural equation modeling, 2014, volume 21 issue 4. Pages 534 to 552. I would check out table 8 in particular.
Please in the same easy to comprehend way, also do videos on CFA with categorical indicators and SEM
Dentiste 72 our plans are to eventually start posting more on EFA, CFA, sem, and other measurement error correction techniques.
hoping to get a tutorial for LCA using Stata MP 15.
Unfortunately, we don't really focus on stata but maybe in the future we can add material for stata
@@ltolandky Thanks! just wondering, is there any tutorial for RMLCA and LTA? Heard that RMLCA syntax is almost the same as LCA syntax. LTA on the otherhand is time consuming. Can it predict the group too and giving the output save file like how the lca analysis did in MPLUS?
The explanation for LMR comparison is too short...