Multidimensional scaling and tourism research

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M U L T I D I M E N S I O N A L SCALING A N D T O U R I S M R E S E A R C H

Mark Fenton Curtin University of Technology, Australia

Phi l ip Pearce James Cook Univ. of North Queensland, Australia

Abstract : Multidimensional scaling (MDS) is a tbrm of analysis which permits the relationships among a set of elements to be represented as interelement distances in spaces. It is suitable for data collected according to a number of difti~rent formats. This article describes a number of the tormal and technical features of MDS analysis and its variants. The main purpose of MDS approaches lie in their capacity to explore the structure underlying a set of judgements. Existing and potential uses of the multidimensional scaling procedure in tourist studies are discussed. It is conclud- ed that the multidimensional scaling approach can provide more than a complex technique tot simplifying data sets. It is also argued that the technique can be used to test hypotheses and conceptual arguments in the tourist literature. Keywords: multidimensional scaling, tourism research.

R~sumd: Fanalyse 5 l'~chelle muhidimensionnelle ct la recherche en tourisme. I2analyse 5 l'dchelle multidimensionnelle (EMD) est une mdthode d'analyse qui permet que les rapports entre les ~l~mcnts d'un ensemble soient reprdsent~s comme des distances entre dldments dans l'espace. Cette m6thode convient h des donndes qu'on a recueillies par plusieurs moyens difli~rents. Le present article ddcrit quelques-uncs des caract6ristiques formelIes et techniques de l'analyse EMD et de ses va- riantes. Le but principal des approches EMD se trouve dans leur capacitd d'explorer la structure qui est 5 la base d'un ensemble de jugements. On discute des usages actuels et ~ventuels du procddd de l'analyse ~l l'6chelle multidimensionnelle dans l'dtude du tourisme. On conclut que l 'approche de l'dchelle muhidimensionnelle peut offrir plus qu'une technique con> plexe pour simplifier des ensembles de donn~es. On soutient aussi qu'il est possible d'utiliser cette technique pour v6rifier des hypoth}ses et des arguments conceptuels dans les oeuvres de recherche touristique. Mots clef: analyse 5 l'dchelle muhidimensionnelle, recherche en tourisme.

I N T R O D U C T I O N

Imagine that a map of Australia lies across a desk. Recorded in kilometers are the distances among all major cities in Australia, resulting in a triangular data set which represents all the intercity distances.

Mark Fenton is a lecturer in psychology at the Curtin University of Technology (Bentley WA 6102, Australia). His research interests include environmental perception and computer ergonomics. Ph i l ip Pearce is a senior lecturer in psychology at James Cook University. He has spent ten years conducting research into social psychological aspects of tourists.

236

FENTON AND PEARCE 237

With this data set of all intercity distances, it would be possible to construct, on the basis of these distances, a map showing the location of each of these cities. Now this could take some time since each city would have to be positioned on the map in such a way that the map of distances represent the data matrix o'f distances among cities. Rather than developing the map by hand, the data could be subjected to a multidimensional scaling analysis (MDS), the result of which would be a map of Australian cities, where the mapped intercity distances would be identical to the matrix.

Multidimensional scaling began primarily as a variant of factor analysis within the field of psychophysics in the late 1930s (Eckart and Young 1936; Richardson 1938; Young and Householder 1938). Later, through the papers of Shepard (1962, 1972), Kruskal (1964), and Car- roll and Chang (1970), it was expanded into a broad family of scaling techniques. While it was initially developed within the area of psychology, it has been usefully applied to a number of areas outside the general scope of psychology, including marketing (Green and Carmone 1972), political science (Easterling 1984), and archaeology (Hodson, Kendall, and Tautu 1971).

The twofold aim of MDS is to reduce data so as to make them more manageable and meaningful, and at the same time to identify whether there is any inherent underlying structure within the data. Both aims of MDS are related, as the recovery of underlying structure usually means the reduction of the entire data set to a manageable and limited number of dimensions, clusters, or groupings.

Multidimensional scaling has many elements in common with factor analysis, but it differs from it in three important respects. First, while factor analysis, like MDS, attempts to identify the inherent structure within data, factor analysis requires that the variables to be analyzed must be measured on at least an interval scale. MDS does not necessarily require this assumption as it determines inherent structure on the ordered relations existing among elements. Second, an MDS solution is usually easier to interpret than a factor analysis solution, as the MDS model is based on the distance between points in a multidimensional space. In factor analysis, the model which is generated is based on the angles between vectors. A third important difference between MDS and factor analysis, which is related to the interpretation of the final configuration, is that the researcher using MDS usually finds relatively fewer dimensions than would occur if the data were analyzed through factor analysis. As Shepard (1972:3) has noted "the high dimensionality characteristic of factor-analytic results is in part a consequence of the rigid assumptions of linearity upon which the standard factor- analytic methods have been based", and, as such, one would expect a solution of lower dimensionality when the assumptions of linearity are removed.

The data on which the MDS analysis is based consist of a series of "proximities" which indicate the degree of similarity or dissimilarity among elements within a defined set. The proximity measures may be either "direct" or "derived" measures of proximity. If the measures are direct, then the subject has usually provided a direct estimate of the degree of (dis)similarity between any pair of elements, either through a rating or

238 MUI3" ID1MENSIONAI~ SCAI. IN(;

classification task. Der ived proximities, on the other hand, usually consist of some measure of association amo n g elements within a set which are not made direct ly by a subject.

M E A S U R E S O F P R O X I M I T Y

Oirec! Measures of Proxbni O,

One of the most c o m m o n measures of direct p roximi ty is paired compar isons . T h r o u g h pairwise judgmen t s , the subject indicates the degree to which two elements are similar or dil'l~rent. In this task, a bipolar rat ing scale of which the endpoints are anchored with the verbal labels o f " v e r y similar" and "very different" is used to record the judg- ment . While the use of the rat ing scale is the most c o m m o n tbrm of eliciting direct measures of proximi ty from subjects, Schiffrnan, Rey- nolds and Young (1981) have suggested that it is sometimes a difticuh and somewhat ambiguous task to per form in terms of assigning a specific mean ing to the numer ic points on the scale. For this reason, Schiffman et al. (1981) have suggested that ra ther than making judgments of similarity with reference to a numer ic scale, a less ambiguous and more effective p rocedure is to have subjects simply place a mark on a line of which the endpoints of the line arc anchored with the verbal labels "exact same" and "most different." The length of the line is then measured in mill imeters from the verbal label up to and including the mark, and as such rcpresents a direct measure of proxi ini ty between elements.

A second method o fub t a in ing direct measures of proximi ty is to have sutzjccts sort a large pool of e lements into a n u m b e r of smaller groups which are perceived as being alike on some at t r ibute of interest. Th e n u m b e r of groupings can be p rede te rmined by the researcher or deter- mined dur ing the sort ing by the subject. After complet ion, a inatrix is const ructed of b inary values represent ing the degrce of similarity between elements, where a 1 might indicate that the elements were sorted into the same group with a 0 indicat ing that the elements were sorted into different groups.

A third method of obta in ing some index of similarity am o n g elements is to have subjects rank order the elements on a par t icular attr ibute. Alternatively, e lements may be rank-ordered in terms of how similar they arc to a predef ined s tandard. Th e completed matr ix represents a rec tangular matr ix of e lements by at tr ibutes, or elements by s tandards, where the values in the body of the matr ix represent the ranking of the e lement in relation to the at t r ibute or s tandard.

While there are a n u m b e r of o ther variants of the three procedures for obta in ing direct measures of proximi ty (see Coxon 1982, Chap. 10), the rating, grouping, and ranking procedures are the most com- inon within the behavioral sciences l i terature. Examples of obta in ing direct proximities through the use of ra t ing scales include research on the percept ion of natural settings (Fenton 1985) and the percept ion of nations (Wish 1971), while examples of the use of g rouping procedures include the percept ion of tourist highways (Pearee and Promni tz 1984),


urban settings (Nassar 1980), and music (Halpern 1984). The ranking procedure is not as common as either the rating or grouping methods, but has been used by Harshman and De Sarbo (1984) in the context of marketing research.

One important consideration which is often overlooked in planning a study, which uses MDS and is based on direct measures of proximity, is the number of elements that are going to be used and the time required to obtain direct measures of proximity among all elements. For example, if the researcher who decides to use the paired comparisons procedure wishes to know what the underlying dimensions are that subjects use in discriminating among four elements, then in order to form a complete proximity matrix six paired comparisons will have to be completed; with eight elements 28 paired comparisons will be required; with 12 elements 66 comparisons; and with 20 elements 190 comparisons. Clearly, as the number of elements increases, there is a corre- sponding and rapid increase in the number of paired comparisons required. In addition, Schiffman et al. (1981) indicate that while the time required to complete a paired comparisons task depends upon the type of elements used, paired comparisons among all possible pairs of 20 elements, with no readaption or rest intervals, could take upwards of l :/2 hours.

In order to partly reduce the time required to complete a set of paired comparisons, MacCallum (1979:69) has suggested the use of an incomplete data design, where only a certain percentage of the proximity matrix is completed by any one individual. Monte Carlo studies, which have compared analyses based on different percentages of incomplete data, have shown "that very accurate recovery of true distances, stimulus coordinates, and weight vectors could be achieved with as much as 60% missing data as long as sample size was sufficiently large and the level of random error was low."

Indirect Measures of Proximity

Indirect measures of proximity usually consist of some index of association, such as a correlational index, as in Pearson's r; a contingency measure, such as Cramer's V or Phi; or a distance measure as usually found in Euclidian, City Block, or Minkowski distances. Given that such indexes of proximity are normally based on aggregated data either across individuals, stimuli, or replications on the variable of interest, then the resulting spatial configuration may simply be an artifact of the process of aggregation.

One of the most valuable reference sources for identifying the different types of indirect measures of proximity available is the SPSSx Users Guide (1986). Not only are the measures identified and the defining formulae supplied, but the SPSSx proximities program module will generate selected proximities on the basis of either the rows or columns of a square or rectangular matrix.

How MDS Works

MDS consists of a broad family of scaling techniques, where the

240 MUI;IIDIMENSI()NAI~ SCALIN(;

selection of any one procedure is dependen t on the type of data to bc analyzed, decisions as to how the data are to be treated dur ing the analysis, and which end result is required. As such, the tbllowing discussion applies only to the most c o m m o n e lementa ry torm of MD S , which will be r e fe r r ed to as classical m u l t i d im en s io n a l scaling (CMDS) . M a n y of the other M D S procedures are simply an extension of this basic model.

Given a t r iangular matr ix of ei ther derived or direct proximities p roduced through any of the procedures previously discussed, the ob- .jective of M D S is to take the proximit ies among elements and represent them as distances in a space of minimal d imensional i ty so that the distances approximate as best as possible the proximities am o n g elements. This is accomplished th rough an iterative cycle and begins with an est imated or r andom start ing configurat ion of" distances among elements. T h e distances in the start ing configurat ion are then compared to the original p roximi ty values among elements and a "goodness of fit funct ion computed," which in most M D S procedures is usually identified as "stress." Once this function has been computed the start ing configurat ion is adjusted so that it more closely approximates the original proximities. Again this adjusted cont igurat ion is compared to the original p roximi ty values with a goodness of" fit function being computed. This iterative cycle cont inues until the m a x i m u m n u m b e r of itera- tions specified by the user has been reached or the improvement in fit or stress is less than a critical interval, which leads to the complet ion of the iterative cycle.

The process of locating elements in a space, where the distances among elements correspond as much as possible to the proximities, may be under taken in a space of any dimensional i ty as specified by the user pr ior to the analysis. Solutions will normal ly be obta ined in a n u m b e r of dimensions, beginning with a one-dimensional solution. As for the m a x i m u m dimensional i ty for the data set, Kruskal and Wish (1978:34), as a rule of' thumb, have indicated that in order tbr the solution to be statistically stable, the n u m b e r of e lements minus one should be greater than four times the proposed dimensionality. Once a series of d imensional solutions is obta ined for a data set, the researcher is then taced with the problem of selecting the most appropr ia te dimensionality and in terpre t ing the dimensional solution.

Interpreting the M D S Solution

It must be emphas ized that there is no one index that will identify the correct d imensional representa t ion for the data. Although the goodness of fit funct ion for each conf igurat ion may partially answer this ques- tion, considerat ion must also be given to the interpretabi l i ty of the dimensional space. When using stress to identify the appropr ia te dimensionality, it is usual to construct a plot showing the relat ionship between stress levels and dimensional solutions (i .e. , Kruskal and Wish 1978, Fig. 16; Schifflnan et al. 1981, Fig. 1.6). When inspecting the plot, the d imensional i ty after which there is little or no substantial improvement in stress is usually selected as the most appropr ia te dimensional solution.


In addition to an evaluation of the stress value, careful consideration must be given to the interpretation of the dimensional solution. It may well be that the solution with the optimum stress value is not necessarily the most interpretable and meaningful solution, and solutions in a higher or lower dimensionality may also need to be considered.

Data Theory

A number of schemes have been developed for classifying the broad range of MDS procedures, the earliest of which was probably that developed by Shepard (1972). However, the most recent non-mathematical overview of MDS, which at the same time attempts a partial taxonomy of MDS models in terms of a general data theory, is that proposed by Schiffman et al. (1981).

The concepts embedded within the data theory proposed by Schiff- man et al. (1981) attempt to link attributes of the data with the model being used to understand the data. As such, three organizing concepts are used to define the data theory proposed: the shape of the data, the number of"ways" of the data, and the nature of the MDS model.

Shape or Mode of the Data

The data for an MDS analysis can be in the form of a rectangular or square matrix. A rectangular data matrix is sometimes also referred to as two-mode data since the proximities within a rectangular matrix emphasize the degree of relationship between two distant sets of elements. For instance, the columns of a rectangular data matrix may represent specific objects, places, or people, while the rows may consist of the rating scales on which the column elements are judged. This is a common type of data matrix collected in behavioral science research, and is in fact the type of matrix that is most often analyzed through factor analysis.

By contrast, the second data shape common to MDS data is the square data matrix which is also sometimes identified as consisting of one mode data. In this matrix, the proximities denote the degree of relatedness among one set of elements, rather than two, as in the case of rectangular data. If the square matrix is symmetrical (i.e., where there is no substantive difference between asking the degree of similarity between element A and B and element B and A), then a special case of the square matrix can be developed known as the triangular or off- diagonal data matrix. Such a triangular data matrix is the most common form of data matrix to be analyzed by MDS.

In some circumstances, it may not be possible to assume symmetry in proximity measures. For example, asymmetric proximity measures may be found in interpersonal perception research, where person A may regard himself as similar to person B, but person B may regard himself as very dissimilar to person A. While it is possible to use a specific MDS program to analyze such data (ALSCAL--AMDS) , it is possible under some circumstances to consider the asymmetry as noise, and to average the two proximity measures and form a triangular data matrix. A second alternative in dealing with this situation is to analyze

242 MUIJI'I1)IMENSIONAI~ S(2:\ld N(;

the upper and lower triangles of the original square matrix separately and compare the resulting solutions.

The 'q4hy" ~ the Data

The way of the data simply represents the dimensional i ty of the original data matrix to be analyzed. Any data matrix must in the first instance be two-way data representing the columns and rows of either a square or rectangular matrix. Three-way data usually takcs the form of several matrices, where the third way is usually represented by either individual subjects or replications over time. It is possible to go beyond three-way or even three mode data (of. Law, Snyder, Hatt ie and Mc- Donald 1984 tot a review of such approaches), and there are M D S programs which will analyze such data (i.e., C A N D E C O M P and PARAFAC). However, the procedure is extremely rare. As Coxon (1982:187) indicates, "users are advised to proceed beyond three-way data with considerable caution. They are in largely unchar ted territo-

It is appropriate when discussing data suitable tot M D S analysis to use both the number of modes and number of ways to define the original data matrix. For instance, a t r iangular data matrix of averaged proxiinity measures across subjects which represents the degree of dissimilarity among a number of countries recently visited could be defined as one mode (countries), two-way data. If, on the other hand, one did not average across subjects but wished to include individual subjects' data matrices in the M D S analysis, one would then have two mode (countries, subjects),hrec-way data. Ahcrnatively, the researcher might ask sut~jects to complete a rectangular matr ix consisting of ratings given to countries on a number of predefined scales. If the researcher does not aggregate across subjects but includes each individual subject's rectangular matrix in the analysis, then there would be three mode (countries, rating scales, subjects), three-way data.

MDS Models

There are two broad classes of MDS models that can be defined as either unweighted MDS or weighted MDS. Only the first of these two will be reviewed here. The most significant difference between the two types of M D S procedures is that weighted M D S specifically examine the variation among matrices that might occur in three-way data. In this case, and if" individuals are represented by different matrices, then it is possible to examine the weighting given by individuals to the averaged spatial configuration.

~ku,,eighted MDS

The most common unweighted MDS model is Classical Multidi- mensional Scaling (CMDS). This model at tempts to identify the underlying structure of" one mode two-way data (i.e., proximity data represented in a lower t r iangular matrix). In this model, the proximi-


ties, which usually represent the degree of dissimilarity between elements, are represented as distances in a multidimensional space.

There are a number of MDS programs which will analyze data of this type including MINISSA from within the MDS(X) set of programs, KYST, POLYCON, M U L T I S C A L E , and ALSCAL. ALSCAL is perhaps tile most appropriate MDS program, as apart from it also being an integral part of the SPSSx statistical package, it has theoretically no limit to the size of the data which can be analyzed, although system memory will ultimately determine this. In addition, ALSCAL has a large number of options available through which the data can be analyzed. For instance, data can be analyzed assuming any level of measurement, as continuous or discrete, and with missing data.

Of course, it must be emphasized that while a CMDS program will analyze two-way one mode data, and hopefully produce an interpretable dimensional solution, this may well be that due to some a priori theoretical reason, the researcher is not interested in identifying the underlying dimensional structure, but wishes to know if there is any clustering or grouping of the elements. In this case, rather than a dimensional analysis being most appropriate, a cluster analysis should be used, such as the program CLUSTER or Q U I C K C L U S T E R from within the SPSSx package, or the clustering algorithm HICLUS (John- son 1967) which is available from within the MDS(X) program set.

The second type of unweighted MDS is perhaps more appropriately referred to as multidimensional unfolding (MDU), and is used to analyze two-way, two mode data. Data suitable for this type of analysis typically consist of a rectangular data matrix, comprising ratings which have been given to a number of elements on the basis of a series of predefined rating scales. MINIRSA from within the MDS(X) program series will perform a MDU analysis, but perhaps the most appropriate program is again ALSCAL from the SPSSx package.

The MDU analysis produces a multidimensional solution which is very different from that achieved in classical MDS, since one is now identifying a spatial solution which represents the interrelationships between two sets of elements or data modes and not one. Given two sets of elements are represented in the derived spatial configuration, it is often referred to as a joint space solution. For instance, if individuals record their preference for visiting a number of different countries, then a MDU analysis of this two mode data (individuals, countries) would provide a spatial solution where both the individuals and countries were identified in a joint space, with some points in the space representing individuals and some points representing countries. Whenever MDU is used, care should be taken when interpreting the dimensional solutions that emerge as they are notoriously uninterpret- able or "degenerate" (Purcell 1984; Schiffman et ah 1981). This insta- bility is a result of the mathematical procedures in transforming joint proximities to joint distances.

There are, however, a number of alternatives to the analysis of rectangular, two way two mode data, and since such data matrices are common in behavioral science research, such alternatives are worth

244 MULTIDIMENSIONAL SCALIN(;

identifying. First, if a large rec tangular data matr ix needed to be analyzed, principal componen ts or factor analysis could be used in preference to MDS , where the factor loadings could be used to interpret one mode of the data, while the factor scores could be used to identify the relat ionship of the second data mode to the first. O f course, as men- t ioned earlier, there are a n u m b e r of drawbacks to the use of factor analysis.

T h e second alternative is to pe r fo rm two C M D S analyses on the data matr ix. In this case, derived proximit ies would be obta ined for both sets of e lements and each der ived proximi ty matr ix analyzed by C M D S . While this solution appears to be s t raightforward, the most obvious shor tcoming is that it is very ditlicult to relate one M D S solution to another, a l though a p rog ram such as P I N D I S , which may be found in the M D S ( X ) series may be useful in this context.

A third p rocedure which has been used, and which has provided a meaningful in terpre ta t ion of two mode data, is the external M D S analysis. This p rocedure initially obtains derived proximi ty measures fbr e lements within one mode of the data, and subsequent ly analyzes them through C M D S . In the next phase of the analysis, the second mode of data is regressed or located into the first. For example, Fenton and Hills (1987), in explor ing the percept ion of animals between animal liberationists and hunters , had subjects rate animal categories on a n u m b e r of elicited constructs , result ing in a two-way mode matr ix consisting of animal categories by constructs. One objective of this s tudy was to identify the most salient d imensions individuals used to discr iminate among the animal categories. In order to accomplish this, a C M D S was pe r fo rmed on a matr ix of derived proximi ty measures which consisted of Eucl idian distances among the animal categories across all constructs. Following the identif ication of space of suitable dimensions, each of the constructs represent ing the second mode of the data was then regressed into the space th rough the use of a p roper ty fitting p rogram, P R O F I T , f rom the M D S ( X ) p rog ram series. ] 'hose constructs which were highly correlated with specific or ienta t ions in the space were then used to identify the way in which individuals appeared to discr iminate among the animal categories.

M D S S T U D I E S IN T O U R I S M R E S E A R C H

T h e r e are a n u m b e r of" examples of mul t id imensional scaling procedures employed in the existing tour ism l i terature. In an early application of the technique, Anderssen and Colberg (1973) studied the similari ty of would-be travelers' percept ions of Med i t e r r an ean destinations. This kind of work is closely allied to market research approaches to the image of one's product compared to its competi tors ' and affords the possibility of diachronic studies of product image. For example, if one dest inat ion is marke ted intensively as offering an exclusive and expen- sive style of holiday experience, then a series of M D S analyses should be able to follow the success of the image making over time.

Similarly, the choice of route to a dest inat ion is sometimes of interest. Pearce and Promni tz (1984) demons t ra ted that highways in Aus- tralia were judged to be very different in their tourist appeal. This


study also indicated that the perceptions people hold about tourist products and services can be clearly linked to their choices and behavior patterns. In this study, the researchers found one cluster of highways which was simply seen as undesirable, unattractive, and to be avoided at holiday times. The traffic flow figures for these highways supported the perceptual data. It would be valuable in many other image studies (including MDS studies) if the perceptions and cogni- tions of tourists could be linked more frequently to their behaviors in regard to those settings (Figure 1).

In an attempt to refine and extend an earlier article on tourist roles by Cohen (1974), Pearce used multidimensional scaling techniques to provide a picture of how a student sample saw 15 travel related roles (Pearce 1982, 1985) (See Figure 2). In a recent extension of this approach, Smithson (1987) noted that the MDS picture provided con- trasting degrees of fuzziness, as defined by fuzzy set theory. Those roles in the center of the space were rated as clearer and less confused than roles towards the periphery. Despite the fact that the behavioral ratings were organized to assess tourism related roles, the core role of tourist is the fuzziest in the whole set.

Smithson's interpretation of the original MDS data with fuzzy set theory methods raises the possibility of other interpretive and statistical approaches adding on to MDS procedures. Recently, Canter has proposed the extensive use of facet theory to interpret, comment on, and organize basic MDS solutions (Canter 1985).

Moscardo and Pearce (1986) used archival material as the input for their multidimensional scaling analysis of the similarity of 17 visitor centers in Britain. The data consisted of ratings of the centers on a number of dimensions and these ratings were converted into similarity scales as discussed earlier in this article. The resulting map of the visitor centers revealed several types of centers which appeared to function in different ways. One cluster of centers provided little more than pamphlets and a booking service, while another group offered the visitor detailed ecological and environmental interpretations of the sur- rounding area. In general, the more detailed visitor centers were those with which the visitors were most satisfied (Figure 3).

A further study employing the MDS technique was conducted at Green Island, one of the most popular destinations on Australia's Great Barrier Reef. In this study the perceptions of tourists and national park staff were compared, and MDS results reflecting the views of each group were obtained. It can be seen that the national park staff not only group the activities differently to the tourists, but indeed use different dimensions or scales to organize their clusters of activities. National park staff emphasize management related issues in their mental maps of the activities (e.g., safe-dangerous, well promoted-not well promoted), while the tourists appear to focus on the experiential dimensions of the activities (how close they get to the reef, the enjoyment level of the activity, and its physical location) (Figures 4 and 5).

Other research efforts using the technique have profitably explored tourists' perceptions of Finland as a destination (Haaht 1986) using a two mode, two-way PREF MAP analysis of the rectangular matrix of countries by attributes'data. This study is allied to the correctional

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marketing use of MDS research and is functionally similar to the earlier Anderssen and Colbey (1973) analysis and use. A more creative use of the procedure is offered by Kemper, Roberts and Goodwin (1983). They employed a card sorting technique to explore the cultural perceptions of a New Mexico community from an anthropological perspective. They used 50 items relating to cultural interests, activities, or services in the card sorting task (e.g., fishing, weaving, pottery) and provided a dimensional solution of the 89 subjects' perceptions. Their analysis revealed a culture-nurture dimension in the data, an active- passive classification of the activities, and a local or widespread identity to the activities. Interestingly, the latter two dimensions are somewhat similar to those reported on the other side of the world in the Green Island study of tourists' perceptions.

Within the wider domain of leisure research applications of the MDS, techniques have included a classification of leisure activity types (Becket 1976; Hirschman 1985; Ritchie 1975), the relationship between public and private recreational systems (Lovingood and Mitchell 1978), an exploration of the kinds of psychological benefits which a recreational park can produce (Uluch and Addoms 1981), and a test of Maslow's theory of motivation (Mills 1985).

Additional Uses

In addition to the studies cited, the multidimensional scaling procedures outlined above provide an exciting methodology for a host of tourism studies. One of the core areas of interest in contemporary tourism studies is how tourists perceive and classify the visited setting (Iso-Ahola 1983; Mayo and Jarvis 1983; Stringer and Pearce 1984). MDS procedures provide a technique for investigating the ways in which tourists rate and organize such stimuli as countries, cities, national parks, theme parks, museums, tourist sites, and information centers. Tourist services too can be examined with this approach and target stimuli might include airline companies, restaurants, package tours, destination resorts, and travel agents. If a large number of these studies were to be conducted by tourism researchers, one's knowledge of tourists' preferences and judgments would be substantially enhanced and could permit the investigation of cross-national and sampling differences in tourists' perceptions.

Some researchers have argued that descriptive research is perhaps less sophisticated and less desirable than hypothesis testing approaches. Initially it might appear that the multidimensional scaling approach will only provide a plethora of descriptive information. It is possible, however, to use the approach to facilitate the testing of theoretical perspectives in tourism studies. For example, one might hypothesize that certain elements should be seen as closer together than others in the final MDS solution. For example, in the study of tourist roles, one might have argued that tourists should be seen more as businessmen than conservationists. The MDS picture obtained from 100 students would have supported this perspective. A sample of conservationists might also have been studied and their mental arrangement of tourist

252 M U L T I D I M E N S I O N A L SCALIN(;

roles could also have been explored. One could then test the proposal that conservationists would see the tourist roles as even more like that of bus inessmen and other exploitative groups than the cluster of roles which include conservationists, explorers, and scientists. The basis fbr such a prediction could lie in the assumed value differences of the conservationist group and this difference could in thct be measured with a scale of values test (e .g. , the Rokeach scale) or a measure such as the Environmenta l Response Inventory (McKechnie 1973). If one were to find the anticipated pattern of similarity with the M D S results, then the predictive power of these personality and individual difference measures tbr tourism related material would be enhanced. It is in thct surprising how little of the convent ional psychological testing material has been used in evaluating tourists' behavior and responses.

Therefore, one theoretical use of the M D S approaches is to specit~' the pattern of anticipated results according to a theoretical perspective and inspect the final structure for its adherence to this pattern. Addi- tionally, one can compare the perceptions of two or more groups of people which could be two different types of tourists, tourists and locals, or tourists and the providers of tourist services. It is argued here that this hypothesis testing or exploring function of mult id imensional scaling has much to offer tourism research. It is hoped that the future use of the procedure will tollow some of these exciting possibilities tor research integration and conceptual development . 2][~

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Submit ted 19June 1987 Accepted 22 j u ly 1987 - Final version submit ted 7 August 1987 Rel;e'reed anonymously

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Multidimensional scaling and tourism research