Stated Preference & Choice Models – a Versatile Alternative to Traditional
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Monitoring and Management of Visitor Flows in Recreational and Protected Areas Conference Proceedings ed by A. Arnberger, C. Brandenburg, A. Muhar 2002, pages 115-121 Stated Preference & Choice Models – A Versatile Alternative to Traditional Recreation Research Wolfgang Haider Resource and Environmental Management, Simon Fraser University, Burnaby, Canada Email: [email protected] Abstract: In outdoor recreation research and visitor management applications, stated preference and choice methods have not enjoyed the same amount of popularity when compared to other directions of applied research. This is somewhat surprising considering the fact that decisions that managers of protected areas and outdoor recreation in general face are typically multi- attribute in nature and require an understanding of the trade-offs that decision-makers of clients are willing to make. This paper provides an overview to stated choice research by explaining the essential considerations during the design and analysis of this approach. The various stages will be explained on hand of a simple example. Then the versatility of the approach will be demonstrated by discussing research design options in more detail. INTRODUCTION human behavior actually manifests some choice. Such analysis is referred to as revealed preference Stated preference and choice methods have or choice analysis. On the other hand, researchers received less attention in recreation research and may also inquire about future choices or behavioral visitor management of protected areas, compared to intentions, which the literature refers to as stated other research approaches. Yet, I will argue that preferences or choice research. under certain conditions, and for certain research This paper will focus on the latter, stated questions, stated preference / choice approaches are preference and choice research. Specifically, I will more appropriate than visitor monitoring, or present variations of the discrete choice experiment, traditional social psychology methods. a multivariate method that permits one to evaluate Over the past few years, the analysis of scenarios of recreation experiences, management observed behavior (visitor monitoring) has alternatives or outcomes by describing these in witnessed significant progress with the introduction scenarios composed of several attributes. Such of innovative monitoring equipment and GIS, both evaluations may include currently non-existent of which are accompanied by more sophisticated alternatives, and provide insights into the trade-off analytical techniques. Many contributions to this behavior of respondents. [ultimately supporting conference document these developments. decision making] In this paper I will provide a brief However, by definition, such observational data are theoretical background to the method, explain the confined to past behavior, and if more details are basic statistical concepts, present a simple study desired about underlying explanations of the from recreational fishing, and document the behavior, or evaluations about the effects of versatility of the method by discussing variations of pending management decisions are desired, then its application. observational data are of limited value. Therefore, a wide range of behavioral research MODELLING PREFERENCE AND CHOICE techniques, many of which are survey based, have BEHAVIOUR been introduced and adapted to recreation research over the past 30 years. Behavioral research provides Many management problems in visitor and insights into the various behavioral antecedents, protected areas management are of a multi-attribute explaining why visitors behave in certain ways, and nature and involve tradeoffs between several these insights might also be used for predicting desirable policy or management goals. Among the future behavior. Studies focus on attitudes, various methods that have emerged in multi- motivation, satisfaction, perception, or simply attribute preference research, it is useful to preferences. Much of the traditional visitor distinguish between (a) revealed preference /choice management literature is built on these foundations approaches, in which the importance of salient of social psychology. variables influencing a decision is inferred by Research on the phenomenon of choice does not statistical analysis from actual behaviour, and (b) slot into the one or the other category conveniently. stated preference approaches, in which survey Choice research may be undertaken with respondents evaluate hypothetical questions observation type data, because any form actual (Timmermans 1984). Discrete choice models, 115 HAIDER: STATED PREFERENCE & CHOICE MODELS – A VERSATILE ALTERNATIVE TO TRADITIONAL RECREATION RESEARCH which rely on revealed preference data, have been Lately, they have gained increasing popularity in applied successfully to transportation research resource economics (Swallow et al. 1994); more (Ben-Akiva and Lerman 1985; Train, 1986), spatial specifically, several recent studies have compared analysis (Wrigley 1985; Kanaroglou and Ferguson the performance of revealed and stated preference 1996 and 1998) and also to recreation (Stynes and methods for resource valuation (Boxall et al. 1996; Peterson 1984). Adamowicz et al. 1997 and 1998). This interesting Among the stated preference/choice approaches, topic with significant relevance to outdoor it is important to distinguish between compositional recreation remains outside the scope of this paper. and decompositional methods (Timmermans 1984). In compositional approaches, such as the theory of THEORY - THE DCE reasoned action (Ajzen and Fishbein 1980), respondents evaluate each aspect of a complex There are several stages to desinging a proper management issue separately, and thereafter the DCE. First, the attributes and attribute levels that researcher calculates ('composes') an overall utility are crucial to a recreation experience and/or a value for an alternative by combining the decision-making context need to be identified. components of an alternative according to some Second, an experimental design needs to be predefined decision rule. Despite some interesting selected. Third, statistical analysis needs to be attempts towards wider application in various fields undertaken. Finally, the results may be presented in of environmental management (see, for example, a computerized decision support system. An Peterson et al. 1988), the operationalization of these example from a simple study in recreational fishing compositional models has proven difficult. (ice fishers around Sudbury, Canada) will be used In contrast, decompositional multi-attribute to demonstrate the various research stages of data. preference models have been applied to complex management issues with considerable success (for Defining attributes and attribute levels summaries see Timmermans 1984; Timmermans A realistic choice task requires the identification and Golledge 1990). These models have proven to of crucial attributes and attribute levels that be versatile, since they account for the multi- typically influence a respondent’s decision when attribute nature of the management issues, permit purchasing a good or service, or when selecting a the exploration of non-existing alternatives, and recreational trip. Usually one considers attributes avoid the problem of multicolinearity. In these that contribute to the quality of the experience as models, alternatives are defined as combinations of well as the regulatory framework. Attributes and a set of attributes, and each set is evaluated as a their specifications can be identified from the whole. The alternative profiles are constructed by literature; management issues will be conveyed by following statistical design principles, such as managers; any variables pertaining to the fractional factorial designs (for example, Raktoe et experience may be elicited from potential al. 1981). If respondents rate or rank each full respondents through informal interviews or in focus profile separately, the technique is usually referred groups sessions. Attributes and their specifications to as conjoint analysis (Green and Srinavasan, for the ice fishing study are summarized in Table 1. 1978). In a discrete choice experiment (DCE), two or more such hypothetical profiles are combined to Selecting a fractional factorial design choice sets, and respondents choose the most preferred alternative (profile) from each set they are Second, profiles need to be created, and asked to evaluate (Louviere and Woodworth 1983; thereafter two or more profiles need to be combined Louviere et al. 2000). The advantage associated to choice sets. If one were to use all possible with a choice based response task is that the profiles (combinations of attribute levels) in a statistical analysis can be conducted with the same study, one would refer to it as a full factorial design, multinomial logit regression model (see below) that and ANOVA could be used as statistical analytical is typically applied in discrete choice models. In procedure. Given the large number of attributes and other words, DCEs combine the analytical elegance levels that make up a DCE, a full factorial approach of the random utility model (McFadden 1974) with is out of question. An alternative is to show the experimental rigour of conjoint analysis (Green respondents only a small set of all possible and Srinavasan, 1978). The advantages of stated combinations. For that purpose, one can select choice over traditional conjoint analysis are that appropriate