Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

An experiment on simplifying conjoint analysis designs for measuring preferences.

Tara Maddala1, Kathryn A Phillips, F Reed Johnson

  • 1Clinimetrics Research Inc, San Jose, California, USA.

Health Economics
|December 16, 2003
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Payers' Views on Insurance Coverage for Confirmatory Diagnostics After a Noncovered Multicancer Early Detection (MCED) Test.

Journal of the National Comprehensive Cancer Network : JNCCN·2026
Same author

Low-Burden Digital Phenotyping of Affective Risk: Positive Emoji Usage, Speech Rate, and Sleep Relate to College Student Mental Health.

Research square·2026
Same author

The potential survival gain and cost-effectiveness of circulating tumor DNA-guided treatment switching in advanced non-small cell lung cancer: A simulation modeling study.

The journal of liquid biopsy·2026
Same author

Diagnostics investments and disease burden.

Science (New York, N.Y.)·2026
Same author

What Is the Consensus Value of Patients' Treatment-Risk Tolerance? Assessing a Stated-Preference Evidence Base for Inflammatory Bowel Disease.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research·2026
Same author

The Food and Drug Administration's role in supporting therapeutic development to address unmet needs: the National Academies report and beyond.

Health affairs scholar·2026

Simplifying conjoint analysis (CA) choice tasks with increased attribute overlap did not significantly improve respondent consistency or efficiency. However, this approach may influence stated preferences in health attribute studies.

Area of Science:

  • Health Services Research
  • Behavioral Economics
  • Survey Methodology

Background:

  • Conjoint analysis (CA) choice tasks involving multiple health attributes can be cognitively demanding for respondents.
  • Standard CA designs often use minimal attribute overlap, potentially increasing respondent burden.
  • Investigating alternative CA designs is crucial for improving data quality and respondent experience.

Purpose of the Study:

  • To evaluate whether increasing attribute overlap in discrete choice CA designs reduces cognitive burden without compromising statistical efficiency.
  • To compare respondent behavior and stated preferences between minimal-overlap and increased-overlap CA designs.

Main Methods:

  • Two discrete choice conjoint analysis (CA) designs were employed: minimal-overlap and increased-overlap.

Related Experiment Videos

  • A total of 353 respondents participated in an HIV testing preference survey, randomly assigned to one of the two CA designs.
  • The increased-overlap design featured shared attribute levels between choice scenarios, unlike the minimal-overlap design where all attribute levels varied.

    • Main Results:

    • No significant improvements were observed in respondent consistency, willingness to trade, perceived difficulty, or fatigue with the increased-overlap design.
    • While not statistically significant, some results trended in the hypothesized direction of reduced cognitive burden.
    • Evidence indicated potential differences in stated preferences between the two experimental conditions.

    Conclusions:

    • Increasing attribute overlap in CA designs may not consistently reduce cognitive burden or enhance statistical efficiency as hypothesized.
    • The observed differences in stated preferences suggest that CA design choices can influence respondent choices.
    • Findings contribute to a better understanding of respondent behavior in CA and inform the design of future health preference surveys.