Autism Identity and the "Lost Generation": Structural Validation of the Autism Spectrum Identity Scale (ASIS) and Comparison of Diagnosed and Self-Diagnosed Adults on the Autism Spectrum.

BACKGROUND

A population segment of autistic adults are under-identified due, in part, to historic changes in criteria for diagnosing autism and diagnostic biases related to gender, socioeconomic status, and other individual characteristics such as intellectual functioning. Some of these individuals, described as the "lost generation", may choose to self-diagnose. Although little is known about this population, it is possible that they share similar self-conceptualizations or internalized stigma as their diagnosed counterparts. This study reports on the structural validity of the Autism Spectrum Identity Scale (ASIS) with individuals diagnosed and self-diagnosed with autism and compares the demographic characteristics, stigma, self-concept, and quality of life of these two groups.

METHODS

Over 1000 adults diagnosed (n = 893) or self-diagnosed (n = 245) with autism were recruited through organizations serving the autism community to participate in a nationally distributed online survey that included demographic questions and measures for stigma, self-concept, quality of life, and wellbeing. The diagnosed dataset was randomly split with exploratory factor analysis performed on a training dataset. Split-half cross-validation was used to predict the factor structure of the holdout dataset. Then, the full diagnosed dataset structure was used to determine the generalizability of the factor structure to the self-diagnosed dataset. The diagnosed and self-diagnosed were also compared for differences in gender, age, employment status, diagnostic term preference, and factors of self-concept (autism identity and self-esteem), stigma, and quality of life.

RESULTS

Factor analysis of diagnosed participants yielded a four-factor structure, consistent with previous research, with strong split-sample cross-validation and good internal consistency. Factor predictions of the self-diagnosed dataset from the diagnosed dataset ranged from .97 - 1.00 with similar internal consistency. Self-diagnosed participants were more likely to be older, women, or employed and less likely to be students or prefer the term "autism" than those with an autism diagnosis. The groups were remarkably similar in reported stigma, self-esteem, quality of life and in ASIS factors; both groups reported lower quality of life than the general population.

CONCLUSIONS

The ASIS demonstrated the same internal structure with both the diagnosed and self-diagnosed. The profile of self-diagnosed participants matches the profile hypothesized for the "lost generation" and others at risk of being under-identified for autism. Both populations appear to be similarly struggling with employment, stigma, and quality of life. Future research should examine whether self-diagnosed individuals meet criteria for autism or could benefit from interventions, programs, or services serving autism communities.