In their simplest form, multiattribute utility (MAU) surveys consist of one or more alternatives that can be evaluated by decision makers, who may be a small group of specialists or a large group comprising members of the public (Keeney and Raiffa 1976; Kleindorfer et al. 1993). Alternatives are described by sets of attributes that are deemed essential to the decision process and are understood by decision makers (Louviere 1988). For example, assume that prospective automobile buyers are asked to evaluate
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two prototype automobiles based on five attributes: fuel economy, seating capacity, per- formance, safety and price. Figure 7.2 illustrates this type of comparison using different levels of the attributes for each alternative.
Attribute Model A Model B
Price (new) $25,000 $15,000
Safety Has air bags No air bags
Horsepower 250 200
Fuel Economy 20 miles per gallon 25 miles per gallon
Seating Capacity 6 persons 4 persons
Figure 7.2. A multiattribute description of car model choice.
A pairwise choice process, in which the most preferred model is selected from the two alternatives, requires each consumer to prioritize (weight) the attributes in importance and choose between tradeoffs in the attribute combinations that make up each alterna- tive. Decisions by a number of individuals can be evaluated statistically to identify the relative importance of each attribute.
For a problem with two alternatives, A and B, it is assumed an individual would choose the alternative with a higher level of utility or, in symbolic terms,
U(XA) > U(XB), where
U(.) represents the individual utility function, and
XA, XBrepresent sets of attributes for alternatives A and B.
Utility can be decomposed into a systematic component, v(.), determined by the attrib- utes, and a random component, ε, such that
U(X) = v(X) + ε
The probability that A is preferred to B depends on the probability that the difference between the systematic component of A and B is greater than the difference between the random components, or
Pr(A) = Pr(D > δ), where D = v(XA) – v(XB) δ= εA– εB
With data from a representative sample of decision makers, statistical techniques such as conditional logit (Ben-Akiva and Lerman 1985; Green 1990) can be used to estimate the relative weights assigned to each attribute. These weights provide information
about the preferences of decision makers and can be used to compute utility “scores” and marginal values to rank alternatives with new combinations of attributes.2 APPLICATION OF THEORY TO PRACTICE
An important issue in any multiattribute utility survey is the choice of attributes and attribute levels to describe the decision problem. To evaluate how a set of attri- butes could be used to represent public perceptions and preferences for restoration of the Everglades/South Florida ecosystem, focus groups were conducted in 1997 with members of the general public. Discussions were held also with various state and fed- eral agency staff involved in restoration planning to identify their perceptions of the planning problem and the hydrological/ecological models being used in the planning process.
Survey Design
On the basis of these focus groups and discussions, a set of attributes was devel- oped that described the hydrological characteristics (water levels and timing) in three major subregions of South Florida: Lake Okeechobee, the Water Conservation Areas, and Everglades National Park (Figure 7.1). In addition, because ecosystem restoration objectives in the Everglades/South Florida setting must be considered with other social objectives, three additional attributes were developed as elements of possible restora- tion plans:
• The annual cost of the restoration to households in Florida;
• Possible restrictions on outdoor and indoor water use in South Florida;
• Changes in farmland acreage in South Florida through conversions to wetlands. Table 7.1 summarizes descriptions and the levels of each attribute used in the survey design. The levels for each of the attributes were selected in consultation with scientists and agency staff knowledgeable about the restoration effort. Because of considerable uncertainty about the effects of any restoration plan (U.S. Army Corps of Engineers 1998), three attribute levels were selected to represent (1) baseline (status quo), (2) inter- mediate, and (3) maximum possible restoration relative to historical conditions. This comparison of existing and potential with historical conditions was a convenient
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2 One form of the utility function that is commonly used to weight attributes and score/rank alternatives
is the additive function: U(xj) = ∑WiUi(xi j), for j = 1, ..., n, where U(xj) is the utility of the jth alterna- tive, Wiis the weight for the ith attribute, Uiis the utility function for the ith attribute, and xij is the score given to the jth alternative on the ith attribute. Utility is a linear combination of the weighted values of each attribute. The alternative with the highest score would be the most preferred. The additive function is convenient because it reduces the number of choice repetitions necessary to derive reliable statistical results (Louviere 1988).
method to allow individuals to consider the implications of alternative attribute levels and was consistent with current ideas about restoration philosophy (Bratton 1992).
The combination of three levels for each of the six attributes described in Table 7.1 provided 36or 729 unique possible attribute combinations. After an extensive period of pretesting, attribute sets for the survey were reduced to 27 combinations using an opti- mized factorial design to evaluate all main effects and first-order interactive effects (SAS Institute 1996). A household interview process3was designed that consisted of the following: (1) a general introduction and explanation of the nature and purpose of the survey; (2) a set of questions to elicit respondent attitudes about environmental and public policy issues; (3) an informational video providing general background about the Everglades ecosystem and changes in the system; (4) a pairwise choice process in which respondents selected a preferred alternative from each of seven paired alterna- tives4and (5) questions to identify the respondent’s socioeconomic background. An informational video, approximately 11 minutes in length, was a key part of the survey design and interview process because it provided a common source of background infor- mation for respondents. Also, interviewers used a notebook to provide complete descrip- tions of the attributes along with graphical representations of the attribute levels.5
Table 7.2 is an example of the pairwise choice process used in the survey. Plans A and B represent two possible combinations of attribute levels that resulted from the factorial design. Respondents chose the preferred plan from choices A and B and then proceeded to evaluate six additional pairs of alternative plans during the interview.
A total of 480 interviews were conducted in 1998 in Miami, West Palm Beach, Ft. Myers, Orlando and Tampa using randomly selected households from a stratified design based on census tract median income and ethnic composition. The first three cities were selected to represent the opinions of citizens most directly impacted by restoration plans since they reside in South Florida. The latter two cities represented the opinions of urban Floridians in other parts of the state. The overall margin of error for the survey was +/- 4.5 percent. A professional market research firm conducted the interviews and included bilingual interviewers when necessary.
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3 Due to the complexity of the survey and the need to assure that respondents drawn from the general
population understood the attribute descriptions, household interviews were necessary for this survey. Other MAU based surveys have used other types of data collection methods with good success (e.g., Adamowicz et al. 1994; Opaluch et al. 1993).
4 While there were 27 possible attribute combinations in the optimized factorial design, pretesting indicat-
ed that more than ten pairwise choice tasks was too burdensome for respondents. Therefore the 27 attribute combinations were split into two groups of 7 pairwise choices (2 groups x 7 pairwise choices equals 28 alternatives with 1 alternative repeated in each group) so that each respondent only made 7 repeated choices. Respondents were randomly assigned to choice groups.
5 Also, an incentive of $10 per respondent was offered as compensation for time and cooperation.
Interviews averaged 57 minutes. Interviewers rated each respondent in terms of their seriousness about the survey, level of attentiveness to the choice task, understanding of the choice tasks and general com- ments about the interview.