I would like to know if Latent Class is suitable when using ACBC to find preference-based segments?

I found contrasting arguments regarding using ACBC data with LC analysis.

In the article of Cunningham (2010), a ACBC study is cited which used Latent Class for segmentation, Jervis & Drake (2012) used it in their CBC vs ACBC comparison study, in the ACBC technical paper (2014) it is briefly stated that you may export the ACBC data for Latent Class analysis. Yet, on this forum Orme warns to not use LC for segmentation with ACBC because of the screener section. Other than that, this seems to be somewhat of a vague/ambiguous topic to me.

I'd like to know if there is some sort of consensus on this matter. Secondly, what is the proper tool for finding preference-based segments on the base of the ACBC-data (HB)?

I hope someone could shed some light on this matter for me. Thank you!

In have been doing some digging into how to do my clustering/segmentation analysis, based on your feedback. I have some options in mind, and i would really appreciate it if you could steer me in the right direction a little bit. To give you some context, my goal is to identify preference based segment. I want to know how big these segments are, and combine them with some profiling variables to characterize the segments.

K-Means:

The most easy option for me is to use the HB utilities (Zero-Centered Diffs) in combination with K-Means clustering using SPSS. But i also think this is the most inferior one, would you agree? Since the clusters will be driven highly by the most important attribute(s). With Latent Class, you can see the attribute importances as well, per cluster. To assign the attribute importances to the K-mean clusters, is this just a matter of calculating the centroids so you just end up with the mean attribute importances per cluster?

CCEA:

Another option as you mentioned is CCEA, which can use mixed methods (like k-means) in the cluster analysis. In terms of using this with ACBC data, would you rate this over using Latent Class as clustering method?

Latent-Class:

Latent Class segmentation based on the HB-utilities(ZC) would seem like the most robust form of segmentation in this matter. But on the other hand, the most complex one. When i am going for this option, i am opting to use XLSTAT-Latent Class software. It is build on the principles of Latent Gold. What would the procedure for this be like, do i only take the HB-utilities(ZC) and not the attribute importances and NONE option into the analysis? I have no clue since i am used to do the Latent Class Analysis with CBC in Lighthouse. I discovered it would have made my life much simpler if i could've used the Sawtooth Latent Class standalone software for my Latent Class clustering analysis, but so be it.

My final questions is regarding the use of a Logit Analysis for my ACBC study (by doing a 1-group Latent Class Analysis). This is common practice for a CBC study analysis, but what would you say is the added value for doing this for ACBC since i already have the utilities/attribute importances within the HB output? Is this to look at the t-stats of the utilities and overall modell fit examination?

Thank you in advance, and again, excuse me for the amount of questions. Since ACBC is a relative new method, and a less used type of conjoint, info concerning some topics are hard to find. Therefore i really appreciate your help!