Replicating a published rural job-preference study with a causal discrete choice experiment

Edited by Avi Yashchin, CEO and Co-Founder ·

The decision: trust a new method, or field a traditional study first

A research or insights leader weighing a workforce or job-preference study faces a real choice: commission a traditional fielded study, or trust a causal discrete choice experiment to answer the same question faster and at lower cost. Committing budget to an unvalidated method wastes the study. Rejecting a method that already reproduces known findings delays a real workforce or incentive-design decision a team needs to make.

One way to test a causal discrete-choice method before committing to it: check whether it reproduces a discrete choice experiment already published and peer-reviewed.

The published study being replicated

Rao et al. ran a discrete choice experiment to assess rural physician and nurse job acceptance and scarcity in India, published as Rural Clinician Scarcity and Job Preferences of Doctors and Nurses in India: A Discrete Choice Experiment in PLOS ONE. The paper measured how job attributes such as location and incentives shaped the stated job preferences of doctors and nurses choosing between rural and urban postings.

Five steps: published study exists, run same-question experiment, compare rankings, get r_s = .7286 p < .001, decide whether to trust the method.
A single strong correlation against one published study is a reasonable basis for trust, not a general accuracy guarantee.

What was compared

Subconscious ran its own discrete-choice-style causal experiment on the same rural clinician job-preference question and compared the resulting preference ordering against the published Rao et al. findings. The comparison produced a Spearman rank correlation of r_s = .7286, p < .001, between the two sets of results.

That correlation is the only figure this comparison supports: agreement between one replication and one published study on one job-preference question. It is not a general accuracy claim, and it does not extend to other studies, domains, or experiment designs.

What this comparison does not show

The archived record lists no attribute set and no full results table alongside the correlation figure, so this page does not reconstruct one. It does not claim a general accuracy rate for the underlying method, and it is not evidence of a healthcare-workforce product or a domain-specific service. It documents a single replication comparison, not a customer engagement.

Four boundaries around r_s = .7286: one replication of one study on one question, no attribute set archived, no general accuracy rate, no healthcare-product evidence or guarantee for future studies.
The correlation is a reasonable basis for trusting the method on a comparable question, not a general accuracy or product claim.

Why this matters for a workforce study decision

A published, peer-reviewed discrete choice experiment gives a known answer to check a new method against before applying it to a real decision. A correlation this strong between an independent replication and the original published rankings is a reasonable basis for trusting the experiment design on a comparable job-preference question, not a guarantee that any new causal study will match every published result this closely.

Teams evaluating a workforce, incentive-design, or job-preference question can review Subconscious's research and other case studies to see how the same methodology has been applied elsewhere, or book time to discuss whether a causal discrete choice experiment fits a specific workforce decision.