we conducted a survey with a discrete choice experiment (4 attributes, 1 with 6 levels and 3 with 3 levels, 12 choice sets, 10 blocks ) and used Analysis Manager within Lighthouse for statistical analysis.
We ran a latent class model and decided for a solution with 4 classes (segment sizes: N=50/90/44/44). One of the smallest classes with N=44 has abnormally large coefficients (e.g. -6.2/1.4/4.8). We tried different settings (different starting seed, no. of iterations, convergence limit). The best replication came always up with large coefficients. In a 3- and 5-class solution there is also one class with abnormally large estimates. And it is not always the class with the smallest number of members.
We are not sure how to interpret these large estimates. Shouldn't the coefficients be close to 1 or -1 at most due to effects-coded data? Are the large coefficients a sign for overestimation?
Appreciate any help.