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Estimating an Evaluation Utilization Model Using Conjoint Measurement and Analysis

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  • R. Burke Johnson

    (University of South Alabama)

Abstract

The conjoint approach to measurement and analysis is demonstrated in this article through a test of an evaluation utilizationprocess-model that includes two endogenous variables (predicted participation and predicted instrumental utilization). Conjoint measurement involves having respondents rate attribute profiles that are analogous to concepts based on cells in a factorial analysis of variance. Such multidimensional ratings result in ecologically valid measurements because respondents examine and react to wholes, rather than to single unidimensional items as in traditional survey research. Statistically, conjoint analysis is a "decompositional" technique in which respondents' overall reactions to profiles (hypothetical situations) are decomposed to determine how much importance is given to attributes (variables used in the profiles) and to levels of the attributes.

Suggested Citation

  • R. Burke Johnson, 1995. "Estimating an Evaluation Utilization Model Using Conjoint Measurement and Analysis," Evaluation Review, , vol. 19(3), pages 313-338, June.
    • Handle: RePEc:sae:evarev:v:19:y:1995:i:3:p:313-338
    DOI: 10.1177/0193841X9501900305
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    References listed on IDEAS

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    1. Green, Paul E & Srinivasan, V, 1978. "Conjoint Analysis in Consumer Research: Issues and Outlook," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 5(2), pages 103-123, Se.
    2. Greene, Jennifer C., 1987. "Stakeholder participation in evaluation design: Is it worth the effort?," Evaluation and Program Planning, Elsevier, vol. 10(4), pages 379-394, January.
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    Cited by:

    1. Hare, Francis G., 1999. "Applications of Multidimensional Similarity Scaling (MDS) in evaluation research," Children and Youth Services Review, Elsevier, vol. 21(2), pages 147-166, February.
    2. Johnson, R. Burke, 1998. "Toward a theoretical model of evaluation utilization," Evaluation and Program Planning, Elsevier, vol. 21(1), pages 93-110, February.
    3. Keen, C. & Wetzels, M., 2001. "Exploring the Preference Structure for Online and Traditional Retail Formats," Working Papers 01.18, Eindhoven Center for Innovation Studies.
    4. van Hoesel, C.P.M. & Goossens, J.H.M. & Kroon, L.G., 2001. "A branch-and-cut approach for solving line planning problems," Research Memorandum 016, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    5. Keen, Cherie & Wetzels, Martin & de Ruyter, Ko & Feinberg, Richard, 2004. "E-tailers versus retailers: Which factors determine consumer preferences," Journal of Business Research, Elsevier, vol. 57(7), pages 685-695, July.

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