Hi Marcus,

the “diametric model” of mental illness looks to be a very exciting line of research. Coming from organizational cognition, I am not familiar with mentalising scores. However, your words “… once I have this 'mentalising score' I will be able to examine how it maps on to various cognition”, drive me to think in the other way around namely in a person-centered analytical approach to your data.

Taking in consideration the proposition that “Normal mentalism” occupies a central ground and represents balance (a) between enough mentalizing ability to understand other people’s minds but not so much as to be paranoid (b), or so little that you become autistic (c); a distinct and yet complementary approach to your goal could be the identification of the empirical representation of individuals mentalism distribution, based on individuals reports.

A person-centered strategy (e.g. latent profile instead of regression or SEM) assume that a sample can contain subgroups and that the variables of interest (e.g., hypo-mentalism, hyper-mentalism) might combine and relate differently to other variables within these subgroups.

Assuming that the ability to understand other people´s minds can be measured properly (e.g. AP, QP), the identification and comparison of at least these three subgroups of individuals (a, b, c) will allow the identification of the most likelihood scores of the individuals belonging to each group and the differences between the mentalistic continuum. This information will help to support the development of a empirical score theory driven.

Recent developments in mixture modeling, for instances latent profile analyses (LPA: Muthén, 2002) and factor mixture analyses (FMA - Lubke & Muthén, 2005), represents a model-based approach to clustering that allows for the direct specification of alternative models that can be compared with various fit statistics and the simultaneous inclusion of continuous, ordinal, and categorical measurement scales in the same model (McLachlan & Peel, 2000).

A great source of information is Mplus site http://www.statmodel.com/discussion/messages/13/13.html?1394751196

Some references are:

Lubke, G. H., & Muthén, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10, 21-39.

Magidson, J., & Vermunt, J. K. (2004). Latent class models. In D. Kaplan (Ed.), Handbook of quantitative methodology for the social sciences (pp. 175-198). Newbury Park, CA: SAGE.

McLachlan, G., & Peel, D. (2000). Finite mixture models. New York: John Wiley.

Muthén, B. O. (2002). Beyond SEM: General latent variable modeling. Behaviormetrika, 29, 81-117.

Muthén, B. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (ed.), Handbook of quantitative methodology for the social sciences (pp. 345-368). Newbury Park, CA: Sage Publications.

Just a thought. My best

Dear Marcus,

there are many groups working on it - or put it another way, know that dichotomies lead nowhere.

have a look at the http://www.nimh.nih.gov/research-priorities/rdoc/index.shtml

concept and their approach.

We use many items to "destill" a persons score on the concept of neurodiversity vs neurotypical (original name is Aspie-quiz but it evolved to more than just measuring AQ, see Ekblad 2013, Pfuhl and Ekblad, submitted; in preparation).

Regarding analysing it. You can use EFA and then CFA. Item analysis is another important concept you need to become familiar with, i.e. which items to keep in your questionnaire, which tell you nothing. Iterative processes as we use it in our survey, get you data reasonably quickly (rdos.net -> Aspie quiz)