Hence, if the fitting process was only guided by the likelihood criterion, the resulting number of subgroupings would be the same as the number of data points.

The quality of fit for a given number of Gaussian distributions is evaluated with the likelihood criterion. The resulting number of Gaussian distributions is usually interpreted as the number of subgroups or clusters in the data. Typically, such Gaussian mixture models fit Gaussian distributions to a given dataset using an iterative search algorithm that varies the number of Gaussian distributions and their parameters. In recent years, univariate and multivariate finite normal mixture models, which are based on the weighted sum of Gaussian distributions, have been applied to mixed populations data from children and adults, to evaluate whether these models can detect discrete subgroups. To extend this line of research, we address shortcomings associated with the assumptions of analytical methods used to identify latent categorical structures within mixed populations and the epidemiological composition of the sample tested with these methods. Understanding the structure of the autism spectrum is important for improving diagnostic procedures, as well as for informing research design and the development of prognostic instruments. However, epidemiological evidence challenges such a taxonic point of view and suggests that autism phenotypes are not bound by conventional diagnostic thresholds, but rather blend imperceptibly with subclinical expressions within the general population, otherwise known as the broader autism phenotype. This conceptualization is supported by taxometric procedures identifying latent categorical structures within the population. Current diagnostic practice conceptualizes autism categorically (i.e., absent or present). Autism is associated with difficulties in social communication and interaction, alongside restricted, repetitive pattern of behaviors and unusually narrow interests. Owing to its flexibility to represent a wide variety of distributions, the Weibull distribution might be better suited for latent structure studies, within enriched and prevalence-true samples.Īutism spectrum condition (hereafter autism) is a neurodevelopmental condition that affects 1 in 59 children. The results provide insight into the continuum nature of the distribution of autistic traits, support the complementary role of both categorical and dimensional approaches to autism spectrum condition, and underscore the importance of analyzing samples that reflect the epidemiological prevalence of the condition. The model yielded results, which are consistent with predictions of current theories advocating for the co-existence of a mixed categorical and dimensional architecture within the autism spectrum. In a mixed autistic and non-autistic population, our model provided a better description of the underlying structure of autistic traits than traditional finite Gaussian mixture models and performed well when applied to a sample that reflected the prevalence of autism in the general population. We employed a dual Weibull mixture model to examine the distribution of the Autism Spectrum Quotient scores of a mixed sample of autistic and non-autistic adults ( N = 4717 autism = 811), as well as of a derived sample (from the enriched sample N = 3973 autism = 67) that reflects the current prevalence of autism within the general population. Second, these analyses have, to our knowledge, been solely applied to enriched samples, where the prevalence of the clinical condition within the study sample far exceeds epidemiological estimates. However, the use of such methods with mixed populations may not be appropriate for two reasons: First, subgroups within mixed populations are often skewed and thus violate mixture models assumptions, which are based on weighted sum of Gaussian distributions.

In recent years, finite mixture models have been increasingly applied to mixed populations of autistic and non-autistic individuals to answer this question. A considerable amount of research has discussed whether autism and psychiatric/neurodevelopmental conditions in general are best described categorically or dimensionally.

Autism spectrum quotient online test