I am currently working on a Latent Class Analysis, studying types of employment in Germany between 2006 and 2014. In the end I want to do regression-analysis for the years 2006/2011/2014 using my LC's as independent Variables. Furthermore I would like to give descriptive evidenve of the relative sizes of my LC's from 2006 to 2014. I work with Panel-Data (SOEP), but I do not intend to compute Latent-Growth-Modeling or Markov-Chains, but rather do Trend-Analysis cross-sectionally.
Therefor I do need to assess measurement invariance for my LCA-Model before pooling my data (=restraining the conditional probabilities to be equal across years, the number of classes has already been assured for each year): My results only work if I measure the same classes for each year of my analysis.
Is there a way/method of assessing this measurement invariance for LCA in Mplus? Maybe there is a direct command to check for this?
I refer to Finch (2015): Model Invariance in Latent Class Analysis
Kankaras et al. (2010): Testing for Measurement Invariance With Latent Class Analysis
I am looking forward to your responses!
With kind regards, Nils Teichler from Berlin (Germany)
Alright, so you have panel data and can approach this as a single-level model in wide format. This means that you have an LCA model for each time point and you have a dependent variable at each time point that is influenced by the latent class variable at that time point.
You should set this up as a Latent Transition model for the 3 latent class variables and their indicators, applying measurement invariance across the 3 time points. There are lots of examples of that including our User's Guide and the papers we have posted under LTA. The influence of these 3 latent class variables on the 3 DVs is such that the DV means change over the latent classes at the respective time point (that is, you don't say y on c). You may or may not want to use the new 3-step approach for this. See, e.g.,
Nylund-Gibson, K., Grimm, R., Quirk, M., & Furlong, M. (2014): A latent transition mixture model using the three-step specification. Structural Equation Modeling: A Multidisciplinary Journal, 21, 439-454.
Asparouhov, T. & Muthén, B. (2014). Auxiliary variables in mixture modeling: Using the BCH method in Mplus to estimate a distal outcome model and an arbitrary second model. Web note 21.
This will give you ideas for the Mplus scripts you want to use.
I have one additional problem. As an identifier-Variable for my long-datafile I have a variable that can have up to 12 digits, containing ID and year numerically.
After my analysis when creating a datafile with savedata (I want to do further analysis with Stata), it seems that Mplus changes some of my values of my ID-Variable, resulting in this change of values. It seems as if Mplus rounds up my values beginning at 100000000. Is there any way around this?