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Dealing with small samples in disability research: Do not fret, Bayesian analysis is here.
Karyssa A Courey1, Felix Y Wu1, Frederick L Oswald1
1Department of Psychological Sciences, Rice University.
Rehabilitation Psychology
|August 22, 2024
Summary
Bayesian methods enhance disability research with small sample sizes. These analyses reveal that individuals with certain disabilities, particularly ambulatory and cognitive impairments, face lower employment rates.
Area of Science:
- Social Sciences
- Statistics
- Disability Studies
Background:
- Small sample sizes pose challenges in disability research.
- Traditional statistical methods may be limited with insufficient data.
Purpose of the Study:
- Demonstrate the application and benefits of Bayesian methods in small sample disability research.
- Illustrate how Bayesian approaches can yield more insights from limited data.
Main Methods:
- Bayesian analysis of employment status (employed vs. unemployed) in small disability subsamples (average n=26).
- Utilized empirically informed priors from large-scale Current Population Survey (CPS) data (N=95,593).
- Conducted sensitivity analyses with various prior types (theory-driven, neutral, noninformative, skeptical).
Main Results:
- Bayesian analysis indicated reduced employment likelihood for individuals with at least one disability.
- Ambulatory, independent living, and cognitive difficulties showed a notable association with lower employment rates.
Conclusions:
- Bayesian analyses effectively incorporate prior knowledge (research, theory) into small sample studies.
- Researchers can gain more insights from small datasets using Bayesian methods compared to frequentist approaches.
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