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Testing attribute significance in Latent Class

I have run a latent class analysis and my conclusion is that I should work with 2 segments. Next, I would like to test whether my 5 attributes are all significant in my 2 groups/segments.
I've read that it is possible to have insignificant attributes in latent class which were significant in the logit analysis, is this correct? In my logit analysis all my attributes were significant, but I am not sure if this is still the case.

Do you have an example of a calculation or can you explain how to calculate whether the attributes are significant in the segments or not?

Many thanks in advance
asked Feb 16, 2017 by Arianne

1 Answer

+1 vote
A simple way to do this is to compute a t-ratio for each of the levels of an attribute within each of the class solutions.  Divide the effect (utility) by the standard error of the effect (our software does this automatically for you and reports the t-ratios for each level within each class).

If none of the levels for a given attribute have a t-ratio larger than 1.96 within a class, then for that class this attribute does not cross the threshold of significance at the 95% confidence level.
answered Feb 16, 2017 by Bryan Orme Platinum Sawtooth Software, Inc. (134,015 points)
I recommend keeping all attributes in the Latent Class model.  If some of the segments have reversals on the utilities for attributes with known order, and you are certain that all respondents should agree with that known order of preference, then you should constrain the utilities for that problematic attribute to have the utility order that you rationally expect.
When I look at the T-Ratio's at my latent class analysis within the different segments not all levels of an attribute are > 1.96.  What does it mean when for example an attribute has 3 levels and when looking at segment one for that specific attribute only one level is >1.96?
Does this mean that the other levels are useless? What should i do in that case?
Note that we use effects-coding in our X matrix (our design matrix) so that the levels within an attribute are zero-centered.  So, for an important attribute with 3 or more levels, at least some of the levels would be expected to fall around zero just due to our zero-centering (the middle levels of preference will likely fall near zero).  But, this does not mean this is an overall useless attribute!  

Now, if you ran a 4-group latent class solution and you see for a given attribute that all of its levels have t<|1.96| for all four segments, then you would question whether this attribute should be in the model or not.  But, if for even one group this attribute had for even one of its levels a significant t, then you would think that it is probable that this attribute adds fit to the model.

The more appropriate test for figuring out if an attribute adds significant fit to the model is by comparing the overall LL fit of the model with and without that attribute added.  Two times the difference in LL between those two latent class runs is distributed as Chi square, with degrees of freedom equal to the number of additional parameters in the model for that attribute included vs. not included.
Attibute significane Multinominal logit model
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