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Evaluating More Granular Options for Socio-Demographic Questions in Autism Research.

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Summary

Enhanced demographic surveys are feasible and acceptable to families, improving representation in research. This study refined survey options for race, ethnicity, sex, gender, and socioeconomic status.

Keywords:
demographicsinclusive researchsurveys

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Area of Science:

  • Health Services Research
  • Sociology
  • Demography

Background:

  • Demographic surveys are crucial for understanding participant populations in research.
  • Existing surveys often lack granular options for race, ethnicity, sex, gender, and socioeconomic status, potentially limiting representation.
  • The Autism Care Network (ACNet) sought to improve its demographic survey's inclusivity.

Purpose of the Study:

  • To evaluate the feasibility and acceptability of an updated demographic survey with more detailed response options.
  • To assess the utility of enhanced demographic data for identifying representation gaps and improving generalizability in research.

Main Methods:

  • An iterative approach was used to update the demographic survey, incorporating qualitative and quantitative feedback.
  • Pilot testing was conducted with families (with and without autism) at two academic pediatric tertiary care centers.
  • Purposive sampling enriched recruitment for ethnic, racial, or gender minority participants.

Main Results:

  • The updated survey included expanded options for race, ethnicity, sex, gender, and language.
  • 85 families within ACNet and 242 families outside ACNet participated.
  • 41% of respondents identified with non-White or multiple race groups.
  • 99% rated the updated form as same or better than the original; 91% found it acceptable, and 97% found it feasible.

Conclusions:

  • Despite concerns about respondent burden, granular demographic survey options demonstrate high feasibility and acceptability.
  • This iterative approach can be adapted by researchers to create context-specific, inclusive demographic forms.
  • Enhanced demographic data is vital for identifying and addressing representation gaps, thereby improving study generalizability.