Literature DB >> 12546291

Measuring what people value: a comparison of "attitude" and "preference" surveys.

Kathryn A Phillips1, F Reed Johnson, Tara Maddala.   

Abstract

OBJECTIVE: To compare and contrast methods and findings from two approaches to valuation used in the same survey: measurement of "attitudes" using simple rankings and ratings versus measurement of "preferences" using conjoint analysis. Conjoint analysis, a stated preference method, involves comparing scenarios composed of attribute descriptions by ranking, rating, or choosing scenarios. We explore possible explanations for our findings using focus groups conducted after the quantitative survey.
METHODS: A self-administered survey, measuring attitudes and preferences for HIV tests, was conducted at HIV testing sites in San Francisco in 1999-2000 (n = 365, response rate = 96 percent). Attitudes were measured and analyzed using standard approaches. Conjoint analysis scenarios were developed using a fractional factorial design and results analyzed using random effects probit models. We examined how the results using the two approaches were both similar and different.
RESULTS: We found that "attitudes" and "preferences" were generally consistent, but there were some important differences. Although rankings based on the attitude and conjoint analysis surveys were similar, closer examination revealed important differences in how respondents valued price and attributes with "halo" effects, variation in how attribute levels were valued, and apparent differences in decision-making processes.
CONCLUSIONS: To our knowledge, this is the first study to compare attitude surveys and conjoint analysis surveys and to explore the meaning of the results using post-hoc focus groups. Although the overall findings for attitudes and preferences were similar, the two approaches resulted in some different conclusions. Health researchers should consider the advantages and limitations of both methods when determining how to measure what people value.

Entities:  

Mesh:

Year:  2002        PMID: 12546291      PMCID: PMC1464045          DOI: 10.1111/1475-6773.01116

Source DB:  PubMed          Journal:  Health Serv Res        ISSN: 0017-9124            Impact factor:   3.402


  18 in total

1.  Using conjoint analysis to elicit preferences for health care.

Authors:  M Ryan; S Farrar
Journal:  BMJ       Date:  2000-06-03

2.  The use of conjoint analysis to elicit willingness-to-pay values. Proceed with caution?

Authors:  J Ratcliffe
Journal:  Int J Technol Assess Health Care       Date:  2000       Impact factor: 2.188

3.  Response-ordering effects: a methodological issue in conjoint analysis.

Authors:  S Farrar; M Ryan
Journal:  Health Econ       Date:  1999-02       Impact factor: 3.046

4.  A role for conjoint analysis in technology assessment in health care?

Authors:  M Ryan
Journal:  Int J Technol Assess Health Care       Date:  1999       Impact factor: 2.188

5.  Magnetic resonance imaging for the investigation of knee injuries: an investigation of preferences.

Authors:  S Bryan; M Buxton; R Sheldon; A Grant
Journal:  Health Econ       Date:  1998-11       Impact factor: 3.046

6.  Using conjoint analysis to assess women's preferences for miscarriage management.

Authors:  M Ryan; J Hughes
Journal:  Health Econ       Date:  1997 May-Jun       Impact factor: 3.046

Review 7.  Can markets give us the health system we want?

Authors:  T Rice
Journal:  J Health Polit Policy Law       Date:  1997-04       Impact factor: 2.265

8.  Patients' preferences regarding the process and outcomes of life-saving technology. An application of conjoint analysis to liver transplantation.

Authors:  J Ratcliffe; M Buxton
Journal:  Int J Technol Assess Health Care       Date:  1999       Impact factor: 2.188

9.  Agency in health care. Examining patients' preferences for attributes of the doctor-patient relationship.

Authors:  S Vick; A Scott
Journal:  J Health Econ       Date:  1998-10       Impact factor: 3.883

10.  Medical decision-making and the patient: understanding preference patterns for growth hormone therapy using conjoint analysis.

Authors:  J Singh; L Cuttler; M Shin; J B Silvers; D Neuhauser
Journal:  Med Care       Date:  1998-08       Impact factor: 2.983

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  49 in total

1.  Measuring preferences for health care interventions using conjoint analysis: an application to HIV testing.

Authors:  Kathryn A Phillips; Tara Maddala; F Reed Johnson
Journal:  Health Serv Res       Date:  2002-12       Impact factor: 3.402

2.  Consumer preferences for hearing aid attributes: a comparison of rating and conjoint analysis methods.

Authors:  John F P Bridges; Angela T Lataille; Christine Buttorff; Sharon White; John K Niparko
Journal:  Trends Amplif       Date:  2012-04-17

Review 3.  A descriptive review on methods to prioritize outcomes in a health care context.

Authors:  Inger M Janssen; Ansgar Gerhardus; Milly A Schröer-Günther; Fülöp Scheibler
Journal:  Health Expect       Date:  2014-08-25       Impact factor: 3.377

4.  Using conjoint analysis to model the preferences of different patient segments for attributes of patient-centered care.

Authors:  Charles E Cunningham; Ken Deal; Heather Rimas; Heather Campbell; Ann Russell; Jennifer Henderson; Anne Matheson; Blake Melnick
Journal:  Patient       Date:  2008-12-01       Impact factor: 3.883

5.  Analysis of patients' preferences: direct assessment and discrete-choice experiment in therapy of adults with attention-deficit hyperactivity disorder.

Authors:  Axel C Mühlbacher; Matthias Nübling
Journal:  Patient       Date:  2010-12-01       Impact factor: 3.883

6.  Modeling the bullying prevention program preferences of educators: a discrete choice conjoint experiment.

Authors:  Charles E Cunningham; Tracy Vaillancourt; Heather Rimas; Ken Deal; Lesley Cunningham; Kathy Short; Yvonne Chen
Journal:  J Abnorm Child Psychol       Date:  2009-10

7.  Patient preferences for personalized (N-of-1) trials: a conjoint analysis.

Authors:  Nathalie Moise; Dallas Wood; Ying Kuen K Cheung; Naihua Duan; Tara St Onge; Joan Duer-Hefele; Tiffany Pu; Karina W Davidson; Ian M Kronish
Journal:  J Clin Epidemiol       Date:  2018-05-30       Impact factor: 6.437

8.  Estimating Preferences for Complex Health Technologies: Lessons Learned and Implications for Personalized Medicine.

Authors:  Deborah A Marshall; Juan Marcos Gonzalez; Karen V MacDonald; F Reed Johnson
Journal:  Value Health       Date:  2017-01       Impact factor: 5.725

9.  Chronic pain patients' treatment preferences: a discrete-choice experiment.

Authors:  Axel C Mühlbacher; Uwe Junker; Christin Juhnke; Edgar Stemmler; Thomas Kohlmann; Friedhelm Leverkus; Matthias Nübling
Journal:  Eur J Health Econ       Date:  2014-06-21

10.  The interim service preferences of parents waiting for children's mental health treatment: a discrete choice conjoint experiment.

Authors:  Charles E Cunningham; Yvonne Chen; Ken Deal; Heather Rimas; Patrick McGrath; Graham Reid; Ellen Lipman; Penny Corkum
Journal:  J Abnorm Child Psychol       Date:  2013-08
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