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ULSC #335469, VOL 30, ISS 05
A Meta-Study of the Values of Visitors to Four
Protected Areas in the Western United States
RANDY J. TANNER, WAYNE A. FREIMUND, WILLIAM T.
BORRIE, AND R. NEIL MOISEY
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A Meta-Study of the Values of Visitors to Four Protected Areas in the Western United
States
Randy J. Tanner, Wayne A. Freimund, William T. Borrie, and R. Neil Moisey
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Leisure Sciences, 30: 1–14, 2008
C Taylor & Francis Group, LLC
Copyright
ISSN: 0149-0400 print / 1521-0588 online
DOI: 10.1080/01490400802353026
A Meta-Study of the Values of Visitors to Four
Protected Areas in the Western United States
5
10
10
15
RANDY J. TANNER
WAYNE A. FREIMUND
WILLIAM T. BORRIE
R. NEIL MOISEY
Department of Society and Conservation
University of Montana
Missoula, Montana, USA
The values that visitors assign to protected areas influence the way they relate to those
areas. Because of the diversity of objectives and missions associated with protected
areas, values might be equally diverse. The purpose of this study is to identify through
confirmatory factor analysis the structure of values assigned to one protected area
compared to other protected areas. By employing visitor survey data from four protected
areas in the western United States, the results demonstrate that some assigned values
were common to all of the areas, while others were specific to the type of protected
area. Understanding how and why assigned values vary by protected area is likely to
be useful as managers confront complex and conflict-laden issues.
Keywords
confirmatory factor analysis, protected areas, survey research, values
The study of how societies interact and relate to protected areas has given rise to competing
theories and ideas over the past several decades. One principle that has garnered wide
20 acceptance is that values are important determinants of interactions and relationships.
Yankelovich (1991) discussed how values are higher, more stable and more enduring forms
of public judgment that “reflect the individual’s ideals and goals” (p. 123). More specifically,
values may be thought of as “an enduring conception of the preferable, which influences
choice and action” (Brown, 1984, as cited in Manning, Valliere, & Minteer, 1999, p. 422).
25 Generally speaking, the importance of values lies in the assumption that “values lead us to
regard some goals or ends as more legitimate or correct and other goals as illegitimate or
wrong” (Myers & Close, 1998, p. 291).
The importance of values has been particularly apparent in the management of visitors
to protected areas. As demand for the services that protected areas provide has increased
30 (English, Marcoullier, & Cordell, 2000) and as constituencies of these protected areas
have become increasingly diverse (McKinney & Harmon, 2004), the practice of visitor
management has become correspondingly more complex. Consequently, managers are
increasingly faced with social conflict rooted in values both held by individuals and assigned
to protected areas (Vaske, Needham, & Cline, 2007). Since these values manifest in broader
Received 1 June 2007; accepted 18 September 2007.
The authors would like to thank the graduate students who collected much of the data discussed in this
article and the anonymous reviewers for their timely and helpful comments.
Address correspondence to Randy J. Tanner, Department of Society and Conservation, The University of
Montana, 32 Campus Dr., Missoula, MT 59812. E-mail: [email protected]
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R. J. Tanner et al.
societal discussions or debates on the environment, the operationalizing of these values
occurs in courts, planning processes, and public meetings.
An important and evident trend in the study of protected areas is that scholarship
has broadened to include meaning-bound concepts, such as place attachment. These approaches have rekindled an interest in the role values have in forming the basis of visitor
relationships to protected areas. This trend is largely the result of acknowledging that values fundamentally shape experience motivations (Borrie, Freimund, & Davenport, 2002;
Manfredo, Teel, & Bright, 2003), attitudes related to ecosystem management (Manning
et al., 1999), voting preferences (Vaske & Donnelly, 1999), social conflict (Vaske et al.,
2007), land classification (Raymond & Brown, 2006) and support for conservation (Vining
& Saunders, 2004).
The importance of values for protected area management and governance is relatively
uncontested. Thorough conceptual treatments have been offered (e.g., Borrie et al., 2002;
Fulton, Manfredo, & Lipscomb, 1996; Manning et al., 1999; Vaske et al., 2007). Nevertheless, little empirical research has been conducted aimed at understanding how the values
visitors assign to protected areas either vary or remain constant across protected areas.
Such information would not only be interesting from a theoretical perspective but would
also be useful to protected area managers. Identifying how values do or do not vary across
protected areas could, for instance, assist managers in more accurately predicting support
for management actions or identifying scenarios where user conflict might arise.
Study Purpose
The purpose of this meta-study was to explore values based on four previously conducted
studies of the values visitors assign to protected areas (Borrie et al., 2002; Cauley, 2004;
Carson, 2005; Manning, Freimund, & Marion, 2004). These studies were conducted at
Yellowstone National Park (Borrie et al., 2002), Zion National Park (Manning et al., 1999),
the Missouri National Wild and Scenic River (Montana; Carson, 2005) and Birds of Prey
National Conservation Area (Idaho; Cauley, 2004). In each study, researchers distributed
visitor surveys that included the same 24 item “values scale.” In all cases, exploratory
factor analysis or principal components analysis was employed to identify the structure of
assigned values. Our overarching research question for this study was: how does the value
structure from one protected area fit the values that visitors assign to other protected areas?
To respond to this question, we conducted confirmatory factor analyses of the values scale
employed in each of the four studies identified above.
We begin by providing an overview of the values scale first used by Borrie et al. (2002)
in Yellowstone National Park, as well as a sample of results from the Yellowstone and Birds
of Prey (Cauley, 2004) studies. We then describe the confirmatory factor analysis procedure
employed to test the fit of certain value structures against the respective datasets. Next, we
discuss how two values structures emerged from that analysis, including one that exhibited
the best fit for the National Parks datasets and another that exhibited the best fit for the other
two datasets. Despite some commonalities, important differences were found between the
two values structures. We conclude by discussing possible explanations for this difference,
as well as recommendations for the study of protected area values.
35
40
45
50
55
60
65
70
75
Scale for Exploring Protected Area Visitors’ Values
Each of the studies used in the meta-study employed the same values scale within the
context of different research purposes. The Yellowstone study (Borrie et al., 2002) was
an exploration of visitor preferences for winter conditions and their support for winter use
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Values Structures of Protected Area Visitors
85
90
95
100
105
110
115
120
125
130
3
management actions. The Zion study (Manning et al., 2004) was aimed at understanding the
Visitor Experience and Resource Protection (VERP) planning framework and informing a
backcountry management plan. The Birds of Prey study (Cauley, 2004) was conducted in
order to understand social justice dimensions of protected areas. The Missouri River study
(Carson, 2005) was aimed at understanding the historical and recreational significance of
the Lewis and Clark Bicentennial. See Table 1 for a summary of these studies. Though
situated within different studies, the same values scale (see Table 2) was presented to all
respondents to obtain a better understanding of the values visitors assign to protected areas.
Respondents were asked to respond on Likert-type scales whether they agreed or disagreed
with the item statements.
The scale was first employed in the Yellowstone study led by Borrie et al. (2002). They
discussed how the 24-item scale was not developed as a result of preliminary qualitative
research as many scales are, but was based on a “review of the literature concerning the
National Park idea, in particular the work of Henneberger (1996)” (Borrie et al., p. 43).
In general, and as illustrated in Table 2, the scale items broadly refer to foundational,
historic, recreational, wildlife, and spiritual values that are commonly used to describe
National Park. In the Yellowstone study, a principal components analysis of responses to
the scale items led to the identification of four values dimensions: natural values (e.g.,
“protection of fish and wildlife habitat”), symbolic and historic values (e.g., “a symbol
of America’s identity”), recreation and tourism values (e.g., “a tourist destination”), and
personal growth and development values (e.g., “a place to develop my skills and abilities”)
(p. 44).
Although the values scale was developed within the context of National Parks, the
values underlying the scale items also pertain to broader discussions of protected areas, in
general. As such, the scale was also employed in two non-National Park settings: the Birds
of Prey National Conservation Area (Cauley, 2004) and the Missouri National Wild and
Scenic River (Carson, 2005). In the Birds of Prey study (Cauley), a natural values dimension
emerged as did in the Yellowstone (Borrie et al., 2002) and Zion (Manning et al., 2004)
studies. Rather than constituting a single dimension, tourism and recreation values emerged
as two separate dimensions. Moreover, a historic and symbolic values dimension did not
emerge at all in the Birds of Prey study, as it did in the Yellowstone study. In the Missouri
River study, the values structure consisted of dimensions that described environmental
values, historic values, and social values.
Heuristically, a comparison of values structures across all four protected areas could be
based on the original analyses performed. For instance, we might conclude that, structurally
speaking, symbolic and historic values are not as apparent among visitors to Birds of Prey
(Cauley, 2004) as they are to Yellowstone visitors (Borrie et al., 2002). Two observations,
though, render this approach weak. First, different analytic methods were performed in
each study. For example, in the Yellowstone study principal components analysis was used,
whereas in the Zion study (Manning et al., 2004) exploratory factor analysis was employed.
Moreover, each analysis used different factor loading suppression levels and factor rotation
methods. Comparing the studies in their original form would, methodologically speaking,
be a comparison of apples and oranges. Second, in each study the analyses gave rise to the
extraction of one “best” model, depending on the method of component or factor extraction.
A number of values structures might exhibit good fit for the dataset, but the analyses only
gave rise to the best or optimal models. For example, the absence of a symbolic and historic
values dimension in the best or optimal extracted model for the Birds of Prey dataset
does not imply that such a dimension necessarily exhibits poor fit for the dataset. Through
the confirmatory factor analysis procedure, we sought to determine whether or not factor
models derived from one dataset would exhibit good fit for the others.
4
Multistage cluster
sampling
1064
71%
Principal components
Natural Values
Historic and Symbolic
Values
Recreation and Tourism
Values
Personal Growth and
Development Values
Sampling method
Values structure
(i.e., factors or
components extracted)
Sample size
Response rate
Analytic method
Survey type
Borrie, Freimund, &
Davenport, 2002
Mail-back survey
Scenic Beauty Values
Manning, Freimund, &
Marion, 2004
On-site and mail-back
surveys
Multistage cluster
sampling
1099
80%
Exploratory factor analysis
Natural Values
Historic and Symbolic
Values
Recreation and Tourism
Values
Freedom Values
Zion
Environmental Values
271
91%
Principal components
Quota sampling
On-site
Carson, 2005
Missouri River
Tourism and Personal Growth Historic Values
Values
Recreation Values
Social Values
Natural Values
213
90%
Principal components
Multistage cluster sampling
On-site surveys
Cauley, 2004
Birds of Prey
Study
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Study citation
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TABLE 1 A Summary of the Four Values Studies
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TABLE 2 I believe Yellowstone National Park Is
Particularly Important As:
Item
A wildlife sanctuary
A place for education about nature
A place to develop my skills and abilities
A protector of threatened and endangered species
A sacred place
An economic resource
A family or individual tradition
A place everyone should see at least once in their life
A place without most types of commercial development
A display of natural curiosities
A historic resource
A symbol of America’s identity
A place for the use and enjoyment of the people
A social place
A site to renew your sense of personal well being
A place of scenic beauty
A place to be free from society and its regulations
A reserve of natural resources for future use
A tourist destination
A place for scientific research and monitoring
A place for recreational activities
A place for wildness
A place for all living things to exist
Protection of fish and wildlife habitat
Methods
Confirmatory factor analysis was employed in this meta-study to determine how well
different values structures conformed to or fit the values visitors assigned to the four
protected areas in question. In exploratory factor analysis, the values structure emerges
135 from the dataset in a quasi-inductive way. With confirmatory factor analysis, a structure is
deductively imposed on the dataset and its fit is tested. Through confirmatory factor analysis
we were able to craft factor models that represented values structures and test them against
each of the four datasets. In doing so, a model might exhibit good fit for a dataset but look
very different than the best or optimal model derived through exploratory factor analysis.
140 If a particular factor model exhibits good fit for two or more datasets, the value structure
it represents is common to those datasets. If, on the other hand, if a model that exhibits
good fit for those datasets does not exist, then the values structures of those datasets differ
in some way.
With such an approach, model selection and design is a critical initial step. Confirmatory
145 factor models may be designed on the basis of different considerations whether theoretical
or empirical. Based on the empirical findings from the Yellowstone (Borrie et al., 2002)
cite and Birds of Prey (Cauley, 2004) studies, we reasonably asserted that a natural values
factor in a model comprised items such as “A wildlife sanctuary,” “A place for wildness” or
“Protection of fish and wildlife habitat.” For theoretical reasons, however, one might posit
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that a model should consist of an economic/utilitarian values factor consisting of items such
as “An economic resource” and “A reserve of natural resources for future use.” However,
no empirical evidence to support this factor was found in the four studies analyzed. Either
approach to model selection (i.e., theoretical or empirical or a mix) is valid in a confirmatory
factor analysis framework.
For this meta-study, we employed an empirical approach to model selection based on
models derived through exploratory factor analysis of each dataset. We tested, for example,
how a values structure associated with Zion visitors (Manning et al., 2004) fit the values
visitors assigned to the Missouri River (Carson, 2005). Four factor models representing four
different values structures were tested against each dataset for a total of 16 confirmatory
factor analyses. Because the researchers who initially analyzed the respective datasets
employed different analytic methods for deriving the factor models, each dataset was reanalyzed in a consistent manner to reduce methodological bias. We adopted a relatively
standard approach to exploratory factor analysis by using maximum likelihood extraction
with varimax rotation and suppressing item loadings less than .6. The results of these four
analyses are presented in Table 3. Because of the different analytic approaches, slightly
different models emerged from this analysis compared to the original analysis (e.g., the
Yellowstone model extracted from this analysis does not include what might be labeled
a personal growth and development dimension that was evident in the original analysis;
Borrie et al., 2002).
The second and final step in the analysis consisted of sixteen confirmatory factor
analyses where each of the four factor models derived in the first step was fitted against
each of the four datasets. As an illustration, a visual representation of the model derived from
the exploratory factor analysis of the Yellowstone dataset (Borrie et al., 2002) is presented
in Figure 1. The confirmatory factor analyses were performed with the software package
EQS version 6.1. Because the data are categorical, fit statistics were obtained through robust
estimation and the use of polychoric correlations (DiStefano, 2002; Mislevy, 1986; Olsson,
Drasgo, & Doransm, 1982). Further, because model comparison for each dataset involved
nonnested models, Akaike’s Information Criteria (AIC) rather than a robust chi-square
measure was used to compare the fit of the four models (Ting Hsiang Lin & Dayton, 1997).
Three measures of fit were obtained for each confirmatory factor analysis:
150
155
160
165
170
175
180
• Akaike’s Information Criteria (AIC). The model exhibiting the lowest AIC for a particular
dataset is indicative of the best fit.
• Comparative Fit Index (CFI). A CFI score greater than .95 is indicative of “excellent”
fit, and a CFI score of .9 to .95 is indicates “good” fit.
• Standardized Root Mean Residual (SRMR). For models of categorical data, the SRMR 185
should be less than or equal to .08 for “good” fit and between .08 and .1 for “adequate
fit.”
For a general discussion of fit indices and these criteria, see Hu and Bentler (1999), Kyle,
Graefe, Manning, and Bacon (2004), and Yu (2002).
Results
190
The fit statistics obtained through confirmatory factor analysis for each model and dataset
are shown in Table 4. Two models emerged through the analysis that exhibited excellent fit.
The factor model derived through the exploratory factor analysis of the Yellowstone dataset
(Borrie et al., 2002), which consisted of three factors that emphasized values related to
learning about and protecting wildlife, tourism and recreation and historical identity (see 195
Figure 1), produced excellent fit for both the Yellowstone (Borrie et al.) and Zion (Manning
.7948
Note: Factor loadings lower than .6 are suppressed.
.8422
0.841
0.712
.7786
.8853
0.603
0.819
0.605
.6583
0.689
0.666
.7144
0.811
0.644
.5848
0.687
.8463
0.678
0.767
.8354
0.714
0.62
0.835
0.654
.9238
0.719
0.709
0.711
0.726
.8791
0.746
0.718
0.618
0.624
0.752
.9244
0.627
0.834
0.734
0.614
0.621
LSC
0.709
0.766
0.692
0.619
0.883
0.784
0.752
0.62
0.693
0.737
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0.89
0.629
0.842
0.708
0.845
0.617
Birds of Prey
A wildlife sanctuary
A place for education about nature
A place to develop my skills and
abilities
A protector of threatened and
endangered species
An economic resource
A family or individual tradition
A display of natural curiosities
An historic resource
A symbol of America’s identity
A place for the use and enjoyment
of the people
A social place
A site to renew your sense of
personal well being
A place of scenic beauty
A tourist destination
A place for scientific research and
monitoring
A place for recreational activities
A place for wildness
A place for all living things to exist
Protection of fish and wildlife
habitat
Cronbach’s alpha
Missouri River
Factor 1 Factor 2 Factor 3 Factor 1 Factor 2 Factor 3 Factor 4 Factor 1 Factor 2 Factor 1 Factor 2 Factor 3
Zion
Scale item
Yellowstone
TABLE 3 Factor Loadings and Reliability Scores for Exploratory Factor Analyses
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Factor 1
Learning about and
Protecting Wildlife
Factor 2
Tourism and
Recreation
Wildlife
Sanctuary
E1
Nature
Education
E2
Protector of
Threatened
and
Endangered
Species
E3
Protection of
Habitat
E4
Use and
Enjoyment
E5
Tourist
Destination
E6
Recreation
Factor 3
Historical Identity
E7
Historic
Resource
E8
A Symbol of
Americaís
Identity
E9
FIGURE 1 Factor model derived from the Yellowstone dataset (a.k.a., “The Parks Model”).
Note: E1 to E9 represent measurement errors, which is the indicator variance not explained
by the factors.
et al., 2004) datasets. To prevent any confusion, we label this model the “Parks Model.”
The factor model derived from the exploratory factor analysis of the Missouri River dataset
(Carson, 2005), consisting of two factors describing values related to Learning about and
Protecting wildlife and identifying with history and Nature (see Figure 2), demonstrated 200
excellent fit for the Missouri River and Birds of Prey (Cauley, 2004) datasets. To prevent confusion, we labeled this model the “Conservation Area Model.” As illustrated in
Tables 5 and 6, each factor for the two models had acceptable Cronbach alphas. For factors
with less than 6 items, .6 is generally used as the cutoff criteria (see, e.g., Kyle et al., 2004).
A particular model exhibiting better fit for a dataset than the model that was derived 205
from that dataset may seem counterintuitive. The Parks Model derived from the Yellowstone dataset (Borrie et al., 2002), for instance, exhibited better fit for the Zion dataset
(Manning et al., 2004) than did the Zion model. This phenomenon can be attributed to
the parsimonious nature of the four models employed in the analysis. By including only
a subset of the total scale items in the models, no assurance exists that a particular factor 210
model will demonstrate the best fit for the dataset from which it was derived. Excluding
a subset of scale items from a model (i.e., in this case those items that had a loading of
less than .6) changes the covariance structure of the model to the extent that the model
64.17
136.1
14.04
15.00
Yellowstone
Zion
Birds of Prey
Missouri River
.978
.962
.910
.960
CFI
.051
.058
.059
.070
SRMR
214.7
447.7
3.925
96.95
AIC
.948
.924
.984
.892
CFI
Zion
Note: Bolded fit statistics identify the models with the best fit.
AIC
Dataset
Yellowstone Model
.082
.070
.073
.091
SRMR
122.8
1569
21.56
271.29
AIC
.655
.703
.940
.638
CFI
.109
.115
.075
.117
SRMR
Birds of Prey
103.9
202.3
−1.232
7.832
AIC
.971
.952
.994
.977
CFI
.067
.079
.038
.067
SRMR
Missouri River
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TABLE 4 Fit Statistics for Confirmatory Factor Analyses of Values Models
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Factor 1
Learning about and
Protecting Wildlife
Factor 2
Identifying with
History and Nature
Wildlife
Sanctuary
E1
Nature
Education
E2
Protector of
Threatened
and
Endangered
Species
E3
Protection of
Habitat
E4
Natural
Curiosities
E5
Historic
Resource
E6
A Symbol of
Americaís
Identity
E7
Scenic
Beauty
E8
FIGURE 2 Factor model derived from the Missouri River dataset (a.k.a., “The Conservation Areas Model”).
might not exhibit good fit for the data from which it was derived. The models derived from
the Zion and Birds of Prey datasets, however, would have demonstrated the best fit for 215
their respective datasets if the models had accounted for the effects of all twenty-four scale
items on all possible factors (i.e., even those factors where all items loaded lower than .6).
But, using such full models, where each item is specified to load on every factor, would
have precluded the construction of parsimonious factor models that could be meaningfully
220
evaluated across all four datasets.
For both the Parks and Conservation Areas models, the confirmatory factor loadings
were all statistically significant (p < 0.05) with relatively small standard errors (SE <
0.10). All of the loadings ranged from 0.47 to 0.94 with the exception of historic resource’s
loading on the Missouri River dataset (Carson, 2005), which loaded at 0.19. Moreover,
225
each factor demonstrated acceptable reliability (see Tables 5 and 6).
Discussion
Understanding the structures of values visitors assign to protected areas is important both
to researchers and protected area managers because values provide the foundation for the
way visitors relate to protected areas. Borrie et al. (2002), for instance, found the values of
visitors to Yellowstone National Park were important predictors of support for or opposition 230
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TABLE 5 Factor Loadings and Reliability Scores for Confirmatory Factor Analyses
Performed on the Yellowstone and Zion datasets (fitted with the “Parks model”)
Factor and items
Learning about and Protecting Wildlife
A wildlife sanctuary
A place for education about nature
A protector of threatened and endangered species
Protection of fish and wildlife habitat
Cronbach’s alpha
Tourism and Recreation
A place for the use and enjoyment of the people
A tourist destination
A place for recreational activities
Cronbach’s alpha
Historical Identity
An historic resource
A symbol of America’s identity
Cronbach’s alpha
Yellowstone
Zion
.871
.663
.749
.836
.842
.864
.748
.895
.776
.885
.826
.714
.731
.795
.772
.472
.564
.616
.837
.781
.779
.793
.727
.714
to management actions related to snowmobile use (e.g., more aggressive enforcement
of snowmobile speed limits, closing roads to over-snow vehicles, etc.). Although this
meta-study did not attempt to explore the relationship between visitors’ values and the
acceptability of management actions across the four study sites, the results provide valuable
235 insight related to the common structures of protected area visitors’ values.
Using the factor models derived from exploratory factor analyses provided four models
to analyze. Through confirmatory factor analyses, the Parks Model and the Conservation
Areas Model demonstrated the best fit. The Parks Model provided the best fit for the
TABLE 6 Factor Loadings and Reliability Scores for Confirmatory Factor Analyses
Performed on the Missouri River and Birds of Prey Datasets (fitted with the
“Conservation Areas Model”)
Factors and items
Learning about and Protecting Wildlife
A wildlife sanctuary
A place for education about nature
A protector of threatened and endangered species
Protection of fish and wildlife habitat
Cronbach’s alpha
Identifying with History and Nature
A display of natural curiosities
An historic resource
A symbol of America’s identity
A place of scenic beauty
Cronbach’s alpha
Missouri River
Birds of Prey
.947
.708
.765
.702
.884
.946
.863
.937
.812
.846
.682
.192
.706
.723
.910
.937
.855
.752
.831
.835
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Yellowstone (Borrie et al., 2002) and Zion (Manning et al., 2004) datasets, and the Conservation Areas Model provided the best fit for the Missouri River (Carson, 2005) and Birds
of Prey (Cauley, 2004) datasets. As different as these protected areas are (e.g., Yellowstone
receives over three million visitors annually from around the world, while Birds of Prey
receives far less visitation and is used primarily by local residents), noting that the values
structures for these areas were similar is interesting. The only difference between the two
datasets fitted with the Parks Model and the two fitted with the Conservation Areas Model
was the existence of the tourism and recreation factor in the Parks model. Such a factor
emerging for Zion and Yellowstone National Parks was not surprising, since providing for
both tourism and recreation are fundamental purposes of National Parks and both feature
strongly in Zion and Yellowstone. Protected areas such as the Missouri River and Birds of
Prey, however, were not necessarily established to provide a wide array of recreational and
tourism opportunities. For example, elaborate visitor areas, museums, curio shops or highly
developed roads would not be found in either of these areas, while such infrastructure is
prevalent in Yellowstone and Zion.
Differences existed between the Parks and Conservation Areas models, but they were
more similar than different. Both include a learning about and protecting wildlife factor that
contained the same items and similar factor loadings. The existence of this factor common
to both models supports the proposition that whatever their charter might be, protected
areas are valued by visitors as areas that provide for the protection of wildlife and their
habitats (e.g., particularly those that are threatened or endangered) and as areas that visitors
can learn about nature. The same could also be said of values associated with the historical
identity that protected areas provide. Both models contain a factor with items characterizing
the value of the areas as historic resources and as symbols of America’s identity. Moreover,
the Conservation Areas Model includes items related to the scenic beauty of the areas
and the natural curiosities found within them. Thus, just as visitors valued the four areas for
providing opportunities to learn about and protect wildlife, the areas also serve as historical
artifacts that are a part of the American identity. Among the four areas studied, the values
associated with learning about and protecting wildlife, as well as historic and symbolic
values constituted the values common to the visitors of each area.
Arguing that the Parks Model and the Conservation Areas Model constitute common
value structures for the respective datasets they are fitted to is reasonable, but the explanatory
power of the models should not be overstated. The Parks Model explained only 23% of the
variance of the Yellowstone dataset (Borrie et al., 2002) and 21% of the variance of the Zion
dataset (Manning et al., 2004). Likewise, the Conservation Areas Model explained only
16% of the variance of the Missouri River dataset (Carson, 2005) and 25% of the Birds of
Prey dataset (Cauley, 2004). That the models explain a relatively small amount of variance,
though, does not imply they are poor models. The purpose of the analysis, rather than
maximizing model fit, was to find commonality. The relatively small variance explained
by each model raises an important issue, though – both the Parks and Conservation Areas
models are inherently incomplete. For other protected areas, additional value dimensions
are likely to manifest, which might or might not be common across any number of protected
areas.
Thus, at best, we may only be certain that each model constitutes part (be it large
or small) of the common value structure for the four areas studied. By adding additional
items to the values scale beyond the 24 considered here, other dimensions (e.g., social
justice, as identified by Cauley in her 2004 study at Birds of Prey) might add to the
amount of variance explained. Additional scale items would also add additional variance
to be explained. Nevertheless, although both models exhibiting a relatively high degree of
parsimony demonstrate excellent fit across multiple datasets, they tell only part of the story.
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A number of other value dimensions not identified through these models also might exhibit
good fit.
Perhaps the most interesting unanswered question related to this study is whether
visitors are drawn to areas that reflect their values or whether they simply assign different
values to different places in different contexts. If the former is true, introducing different
governance and management regimes that are inconsistent with the value structure current
295 visitors assign to the area might be appropriate. Those regimes might attract a different set
of visitors. The important aspect, though, is whether or not those regimes comport with
any broader meanings or societal values that define the area’s mission and objectives. If
a goal of management is to manage for visitor satisfaction and if visitors simply assign
different values to different areas, then it behooves protected area managers to manage the
300 areas in a consistently with the values that visitors assign to the areas as long as those
values are consistent with the agreed upon mission and objectives for the area. However,
the decision of how to govern and manage an area is dependent on a number of factors
beyond the values of visitors. Decisions are also made based on the values of other societal
groups (e.g., nonvisitors who have an interest in protected area management, legislators,
305 other officials), budgetary constraints and legal mandates. Visitor values are only a single
variable in the decision-making equation. Inevitably these values are important.
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Conclusion
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Protected areas are established for a variety of reasons and reflect or foster diverse values
structures. In the four areas included in this study, we found the opportunity to learn
about and protect wildlife as well as historical identity were important common features
to the values structures. Despite these shared values, Yellowstone (Borrie et al., 2002) and
Zion National Parks (Manning et al., 2004) were assigned values related to recreation and
tourism, whereas Missouri River (Carson, 2005) and Birds of Prey (Cauley, 2004) were not.
The findings of this study may have implications for protected area management, but we also
feel that further research related to visitors’ values is needed. We feel that knowing whether
or not the values assigned to a protected area reflect the self-selection of visitors (i.e., where
visitors visit those protected areas whose mission and objectives are consistent with their
held values) and/or whether they reflect a recognition by visitors of the multiple missions
and objectives that protected areas may serve is an important consideration. In other words,
would a visitor who values Yellowstone for its tourism and recreation opportunities value
Birds of Prey for the same reason – because that is why they value protected areas, in
general? Or, would they value Birds of Prey for other reasons because they believe Birds of
Prey serves an entirely different purpose than Yellowstone? These explorations will likely
gain importance as protected area researchers and practitioners continue to uncover the
many ways in which visitors’ values influence protected area governance and management.
References
330
Borrie, W. T., Freimund, W. A., & Davenport, M. A. (2002). Winter visitors to Yellowstone National
Park: Their value orientations and support for management action. Research in Human Ecology,
9(2), 41–48.
Brown, T. (1984). The concept of value in resource allocation. Land Economics, 60, 231–
246.
Carson, H. H. (2005). Access tradeoffs vs. social preferences: Upper Missouri River breaks national
monument. Unpublished master’s thesis, University of Montana, Missoula, MT.
August 22, 2008
6:35
14
Q1
LSC
ULSC_A_335469
R. J. Tanner et al.
Cauley, L. (2004). Integrating social equity into the measurement of human values in outdoor recreation. Unpublished master’s thesis, University of Montana, Missoula, MT.
DiStefano, C. (2002). The impact of categorization with confirmatory factor analysis. Structural
Equation Modeling, 9(3), 327–346.
English, D. B. K., Marcoullier, D. W., & Cordell, H. K. (2000). Tourism dependence in rural America:
Estimates and effects. Society and Natural Resources, 13, 185–202.
Fulton, D. C., Manfredo, M. J., & Lipscomb, J. (1996). Wildlife value orientations: A conceptual and
measurement approach. Human Dimensions of Wildlife, 1(2), 24–47.
Henneberger, J. (1996). Transformations in the concept of the park. Trumpter, 13, 127–133.
Hu, L., & Bentler, P. M. (1999). Cutoff criterion for fit indexes in covariance structure analysis:
Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.
Kyle, G., Graefe, A., Manning, R., & Bacon, J. (2004). Effects of place attachment on users’
perceptions of social and environmental conditions in a natural setting. Journal of Environmental
Psychology, 24(2), 213–225.
Manfredo, M. J., Teel, T. L., & Bright, A. D. (2003). Why are public values towards wildlife changing?
Human Dimensions of Wildlife, 8(4), 287–306.
Manning, R., Valliere, W., & Minteer, B. (1999). Values, ethics, and attitudes toward national forest
management: An empirical study. Society & Natural Resources, 12(5), 421–436.
Manning, R. E., Freimund, W. A., & Marion, J. L. (2004). Research to support application of the
visitor experience and resource protection (VERP) framework to backcountry planning at Zion
National Park: Final report. National Park Service.
McKinney, M., & Harmon, W. (2004). The western confluence: A guide to governing natural resources. Washington, DC: Island Press.
Mislevy, R. J. (1986). Recent developments in the factor analysis of categorical variables. Journal of
Educational Statistics, 11(1), 3–31.
Myers, C. G., & Close, E. P. (1998). Wilderness values and ethics. In D. L. Kulhavy & H. Legg
(Eds.), Wilderness & natural Areas in Eastern North America: Research, Management and
Planning (pp. 291–295). Nacogdoches, TX: Center for Applied Studies, Stephen F. Austin State
University.
Olsson, U., Drasgo, F., & Doransm, N. J. (1982). The polyserial correlation coefficient. Psychometrika,
47, 337–347.
Raymond, C., & Brown, G. (2006). A method for assessing protected area allocations using a typology
of landscape values. Journal of Environmental Planning and Management, 49(6), 797–812.
Ting Hsiang Lin, C., & Dayton, M. (1997). Model selection information criteria for non-nested latent
class models. Journal of Behavioral and Educational Statistics, 22(3), 249–264.
Vaske, J. J., & Donnelly, M. P. (1999). A value-attitude-behavior model predicting wildland preservations voting intentions. Society & Natural Resources, 12(6), 523–537.
Vaske, J. J., Needham, M. D., & Cline Jr., R. C. (2007). Clarifying interpersonal and social values
conflict among recreationists. Journal of Leisure Research, 39(1), 182–195.
Vining, J., & Saunders, C. D. (2004). Conservation psychology. In M. J. Manfredo, J. J. Vaske,
B. L. Bruyere, D. R. Field, & P. J. Brown (Eds.), Society and natural resources: A summary of
knowledge (pp. 46–57). Jefferson, MO: Modern Litho.
Yankelovich, D. (1991). Coming to public judgment: Making democracy work in a complex world.
Syracuse, NY: Syracuse University Press.
Yu, C. (2002). Evaluating cutoff criteria of model fit indices for latent variable models with binary
and continuous outcomes. Unpublished doctoral dissertation, UCLA, Los Angeles, CA.
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| File Type | application/pdf |
| File Title | ULSC_A_335469.dvi |
| File Modified | 2008-09-02 |
| File Created | 2008-08-22 |