Temporal Scale and Degree of Consensus as Variables in Cultural Model Research

John B. Gatewood Lehigh University

Catherine M. Cameron Cedar Crest College

Preview/Outline

Conceptual background … cultural models versus cultural consensus approaches

Our Turks & Caicos study … conjoining the cultural models and cultural consensus approaches

Some findings … details, details

Stepping back … toward a typology of “cultural models”

Conceptual Background

CULTURAL MODELS Fine-grain focus on “what people know” Recognizes knowledge is integrated and generative Building composite models from diverse informants is

something non-social scientists just don’t think of doing Produces insightful findings Has intuitive appeal to potential ‘end-users’ of the information

But … Credibility of the model? – replicability, verification,

completeness, etc. Degree of sharing? – expertise gradient or sub-cultural

diversity, competing viewpoints or cognitive plurality, etc. Generalizability of findings?

CONSENSUS ANALYSIS Focus on “how knowledge is distributed” in a population Addresses the fact of intra-cultural diversity Explicit methodology (clear what has been done) Easily coupled with standard survey research; hence, data

lend themselves to standard hypothesis testing, too But …

‘Particulate’ view of knowledge isn’t plausible How to decide on the questions? Devil is in the details – e.g., must counter-balance questions if

using rating-ranking data, how many questions needed to establish accurate respondent-profiles, etc.

Conjoining cultural models and consensus analysis is a way cognitive anthropology can contribute to a better understanding of the social organization of knowledge (a.k.a., socially distributed cognition)

And, when the domain being studied is socially relevant, such research also produces findings that are useful … both to the people we study and other end-users

Our Study in the Turks & Caicos Islands Focus on residents’ (Belonger) understandings of

tourism and its impacts on their life … important to them

Cognitive ethnography…combining “cultural model” approach with“cultural consensus” approach

Two years of data collection, two phases of research

Acknowledgement. This material is based upon work supported by the National ScienceFoundation under Grant No. (BCS-0621241). Any opinions, findings, and conclusions orrecommendations expressed in this material are those of the authors and do not necessarilyreflect the views of the National Science Foundation.

Turks and Caicos Islands ??

“Beautiful by nature” – Tourist Board’s promotional motto

Phase I (summer 2006) – Interviews 30 tape-recorded ethnographic interviews

Purposive sampling … get range of variability

Extract “propositional content” from each informant’s interview

Sort, winnow, and distill ideas expressed Construct a composite cultural model of tourism from

Belongers’ perspectives Develop questionnaire based on propositional content of

the composite cultural model

Cultural Model Overview (take 1)

Characteristicsof tourists

Tourism productand draw

Tourism dynamics(pace of change)

I. The Tourism System

Outlook aboutfuture of tourism

Outlook abouttourism work

Outlook aboutbusiness

opportunities

II. Tourism Work andOpportunities

SocioculturalImpacts( + , - )

EconomicImpacts( + , - )

EcologicalImpacts( + , - )

III. Particular Impacts

Cultural Model Overview (current)

Pace of change- - - - - - - - -Potential for

furtherdevelopment

SocioculturalImpacts( + , - )

EconomicImpacts( + , - )

EcologicalImpacts( + , - )

Characteristicsof tourists

I. The Tourists Themselves

Attitudes abouttourism work- - - - - - - - -

Businessopportunities

II. Belonger EconomicOrientation

III. Impacts of Tourism(general) (specific)

Cultural Model Details

“Most of the tourists who visit Turks and Caicos… <14 statements>.” Are wealthy and used to luxury. Are friendly and polite. Don’t usually expect any special

treatment. Are budget-minded and careful with their

money. Are curious about the islands and its

people. Are mostly loud and rude. … etc.

Characteristicsof tourists

I. The Tourists Themselves

“Most Belongers…<18 statements>.” Appreciate that tourism work is a game you

have to play. Feel that tourism work is like being a

servant. Prefer jobs in the private sector. Will only work in tourism if they can get

management jobs. See lots of opportunities for themselves in

tourism work. Prefer to leave menial jobs to immigrants. … etc.

Attitudes abouttourism work- - - - - - - - -

Businessopportunities

II. Belonger EconomicOrientation

Phase II (summer 2007) – Survey Hire and train research team

(six local RA’s, two Lehigh undergraduates) Pre-test and revise questionnaire Survey “300” randomly-selected Belongers

Stratified random sampling using voter registration lists as sampling frames

Finding the targeted respondents?? … *(final N = 277)(no street address; lousy phonebook)

ALSO survey people interviewed in Phase I(our “Special Sample”)

Finding #1: Cultural Consensus Weak cultural consensus across the whole country

exists with respect to the 119 similarly-formatted“cultural model items” in questionnaire

Random Sample (N=277) Ratio of 1st to 2nd eigenvalues = 4.515 Mean 1st factor loading = .499 9 negative loadings, or 3.2% of sample

Consensus Analysis's Factor Loadingsfor TCI Random Sample (N=277)

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Factor 1

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Finding #2: Disaggregating Sample Improves Consensus … Mostly

ISLAND / ISLAND-GROUP NRatio of

EigenvaluesMean 1st Loading

Percent Negative

Providenciales( very developed )

141 4.935 .547 0%

Grand Turk, with Salt Cay( middling development )

74 5.978 .569 0%

South Caicos( little development )

22 7.245 .607 0%

North Caicos & Middle Caicos( little development )

40 2.305 .238 20%

Diversity in the Special Sample Weak cultural consensus in this group (N=29), too

Ratio of 1st to 2nd eigenvalues = 3.355 Mean 1st factor loading = .584, with 0 negative loadings

Hence, use Special Sample to investigate the second largest source of variability… (2nd factor accounts for 21.6% of variance in this respondent-by-respondent correlation matrix)

Examining the 2nd factor loadings for these 29 familiar informants, we began to see a very interpretable pattern…

Consensus Analysis: Special Sample

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Factor 1 Loading

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Cluster 1(n=12)

Cluster 2(n=17)

JOHNSON’S HIERARCHICAL CLUSTERING (average method)

Cluster 1 Cluster 2 A A

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Finding #3: Subcultures Exist

Analyzing the clusters separately, consensus indicators go up sharply

Cluster 1 (n=12) Ratio of 1st to 2nd eigenvalues = 7.061 Mean 1st factor loading = .640, with no negative loadings

Cluster 2 (n=17) Ratio of 1st to 2nd eigenvalues = 9.838 Mean 1st factor loading = .653, with no negative loadings

Conclusion: there are two coherent viewpoints (different ‘answer keys’) in the Special Sample

Two Viewpoints (in Special Sample) Based on the individuals who best represent each

subcultural group (and taking into account the views expressed by them in interviews), the two viewpoints might be characterized as follows

Cluster 1: “Cautiously ambivalent” Some concern about the long-term consequences of tourism;

tourism involves a trade-off between good and bad impacts

Cluster 2: “Pro-tourism, pro-growth” Very positive about changes tourism has wrought;

pro-growth and pro-development; change is progress

Survey Items that Differentiate

Independent-samples t-tests on the 119 cultural model items in questionnaire (Cluster 1 vs. Cluster 2)…

47 items show “statistically significant” group-group differences at the unadjusted α =.05 level

Conversely, the two groups did not differ significantly on 72 items… (reason the Special Sample, as a whole, shows weak consensus)

Cluster 1 vs Cluster 2: 47 Items with "Significant" Contrasts

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Item Name

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Mean1 Mean2

Finding #4: The “Usual Suspects” Don’t Explain the Viewpoints NO difference with respect to:

Age; Sex; Education; Household income How often think about tourism; Speak with tourists Perceived overall financial benefit from tourism

(Variable = self + family + neighbors + island + country) Sources of information

Almost significant contrast (α =.057) : Cluster 1 has traveled to more parts of the world

One significant contrast (α =.033) : Cluster 2 reports more personal financial benefit from tourism

(Variable = self + family)

Extrapolating from Special Sample EMPIRICAL QUESTION:

Is there a similar “viewpoint” variation – the same sort of “subcultural” attitudinal variation – in the larger, Random Sample?

PRELIMINARY OBSERVATION:Overall, response profiles across the whole battery of 119 items are very similar between the Special Sample (as a whole) and the Random Sample … r = .938

Note: Special Sample has greater variance among items means, but very similar pattern of up’s-and-down’s

Both Samples’ Response Profiles are Very Similar Overall … (r = .938) Item Means: Random and Special Samples

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The 119 Items

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Extrapolating…? – Two Approaches1. Profile Matching

Compare each Random Sample respondent with the two “subcultural” response profiles (across 47 items) from the Special Sample

Estimate proportions of “Pro-Tourism” and “Cautiously Ambivalent” groups within the Random Sample based on which profile respondents resemble

2. Thematic Indices Construct multi-item, additive indices to measure different

themes that seem to distinguish the Special Sample’s two “viewpoints”

See whether one or more of these indices correlate with the 2nd factor loadings from consensus analysis (both samples)

Profile Matching ApproachScatterplot: Respondents’ correlations with respect to the Special Sample’stwo “subcultural” response profiles

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Correlation with Cluster 1 means

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“r2–r1” … a computed variable from information depicted in the scatterplot, where

r1: Pearson r vis-à-vis Cluster 1’s response profile

r2: Pearson r vis-à-vis Cluster 2’s response profile

Thus, Positive values respondent is more similar to the

“Pro-Tourism” (Cluster 2) viewpoint Negative values respondent is more similar to the

“Cautiously Ambivalent” (Cluster 1) viewpoint

Finding #5: The Attitudinal Gradient Found in the Special Sample also Exists in the Random Sample 206 respondents have positive values for “r2–r1”;

71 respondents have negative values Thus, the “pro-tourism” camp outnumbers the

“cautiously ambivalent” camp by about 3-to-1

And… correlation between the “r2–r1” pattern-matching variable and the 2nd consensus factor scores for the Random Sample is VERY high … r = .903 Thus, “second largest source of variation” has

something to do with this attitudinal gradient

Thematic Indices Approach

Candidate items selected from all 119 cultural model questions based on their face validity … subsequently winnowed by standard criteria of index construction using Random Sample’s data

RESULT: Six additive indices … scaled to rangefrom 1-to-5 (1=maximally negative, 3=neutral, 5=maximally positive) Social Impacts (7 items, Cronbach’s α = .780) Heritage Optimism (5 items, Cronbach’s α = .737) General Pro-Tourism Outlook (7 items, Cronbach’s α = .717) Financial Impacts (5 items, Cronbach’s α = .704) Environmental Impacts (5 items, Cronbach’s α = .673) Orientation to Tourism Work (4 items, Cronbach’s α = .636)

To our surprise (and delight), the six thematic indices could be combined to form a single, second-order index

MacroIndex … a two-stage additive index based on 33 items, Cronbach’s α = .812

Histogram of MacroIndex scores for Random Sample (mean = 3.23)

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Finding #6: MacroIndex Correlates VERY Highly with Consensus 2nd Factor Loadings MacroIndex scores are extremely highly correlated with

the 2nd factor loadings from consensus analysis… Random Sample (N=277) r = .922 Special Sample (N=29) r = .975

INTERPRETATION: MacroIndex’s 33 constituent items virtually are the

substantive issues that underlie the “second largest source of variation” among respondents

The attitudinal gradient first discovered in the Special Sample is also present (and now substantively identified) in the Random Sample

[ Methodological aside … ]

It was only by having a “Special Sample” – people we interviewed AND surveyed – that we:

became aware different viewpoints existed, were prompted to investigate how these viewpoints

are associated with distinguishable response patterns in the survey data

…“and now for something completely different” (Bullwinkle)

Varieties of “Cultural Models”

Tongan radiality (Bennardo, this session) Commitment in American marriage (Quinn 1982) Folk theory of mind (D’Andrade 1987) Home heat control (Kempton 1987) Watermen’s understanding of blue crab management

(Paolisso 2002) Employees’ understanding of credit unions (Gatewood &

Lowe 2008) Economic individualism (Strauss 1997) … etc. … IN WHAT WAYS DO THESE “CULTURAL MODELS”

DIFFER?

Toward a Typology of Cultural Models COGNITIVE PROPERTIES

Temporal scale Time to become activated Duration of activation

Inertial characteristics Time to learn / develop Time to unlearn / modify

Functional integrity Number of component parts Degree of integration among the components (e.g., all

activated at once, all activated but separately, or some components can be activated without activating others?)

COGNITIVE PROPERTIES (cont.) Generative capacity Motivational force Degree of implicitness / ease of communication

SOCIAL-DISTRIBUTIONAL PROPERTIES Degree of elaboration across individuals

E.g., components learned separately or as package,‘core’ components widely shared but variable with respect to ‘peripheral’ components, or just idiosyncratic variation?

Patterns of “sharing” across individuals E.g., uniformly and widely shared, subcultural differences,

expertise gradients, perspectival gradients, or free variation? Degree to which X is a topic of discussion

… Finale.

At some point, it might be worthwhile to expand upon D’Andrade’s (1995) ontology of cultural forms

For the time being, we would just note that: our informants, and respondents, took several minutes

to ‘get their thoughts going’ about tourism and its impacts, and

“residents’ understanding of tourism” is not a monolithic thing; rather, the component ideas are complexly distributed among people

Minimally, then, temporal scale and degree of consensus are key variables differentiating kinds of cultural models