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

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

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

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  1. Temporal Scale and Degree of Consensus as Variables in Cultural Model Research John B. Gatewood Lehigh UniversityCatherine M. Cameron Cedar Crest College

  2. 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”

  3. 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?

  4. 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.

  5. 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

  6. 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.

  7. Turks and Caicos Islands ??

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

  9. 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

  10. II. Tourism Work andOpportunities I. The Tourism System III. Particular Impacts Characteristicsof tourists Outlook aboutfuture of tourism SocioculturalImpacts( + , - ) Tourism productand draw Outlook abouttourism work EconomicImpacts( + , - ) Tourism dynamics(pace of change) Outlook aboutbusinessopportunities EcologicalImpacts( + , - ) Cultural Model Overview (take 1)

  11. I. The Tourists Themselves Characteristicsof tourists SocioculturalImpacts( + , - ) II. Belonger EconomicOrientation EconomicImpacts( + , - ) Attitudes abouttourism work- - - - - - - - -Businessopportunities EcologicalImpacts( + , - ) Cultural Model Overview (current) III. Impacts of Tourism (general) (specific) Pace of change- - - - - - - - -Potential forfurtherdevelopment

  12. 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. I. The Tourists Themselves Characteristicsof tourists

  13. “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. II. Belonger EconomicOrientation Attitudes abouttourism work- - - - - - - - -Businessopportunities

  14. 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”)

  15. 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

  16. Finding #2: Disaggregating Sample Improves Consensus … Mostly

  17. 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…

  18. Cluster 1(n=12) Cluster 2(n=17)

  19. JOHNSON’S HIERARCHICAL CLUSTERING (average method) Cluster 1 Cluster 2 A A A A A 1 A A A A A A 1 A | A A A A A A A A A A A A A A A A A 2 0 1 7 0 0 1 1 0 2 7 2 | 3 2 2 2 0 1 0 1 0 0 0 2 1 2 2 2 3 6 3 5 a 6 2 1 2 9 1 b 7 | 0 9 3 5 1 9 4 4 8 5 7 4 0 0 8 2 1 ------ - - - - - - - - - - - - | - - - - - - - - - - - - - - - - - 0.7129 . . . . . . . XXX . . . | . . . . . . . . . . . . . . . . . 0.6934 . . . . . . . XXX . . . | . . . . . . . . . . . . . . . XXX 0.6613 . . . . . . . XXX . . . | . . . . . . . . . . . . . . XXXXX 0.6417 . . . . . . XXXXX . . . | . . . . . . . . . . . . . . XXXXX 0.6060 . . . . . . XXXXX . . . | . . . . . . . . . XXX . . . XXXXX 0.6025 . . . . . XXXXXXX . . . | . . . . . . . . . XXX . . . XXXXX 0.5926 . . . . . XXXXXXX . . . | . . . . . . . . . XXX . . XXXXXXX 0.5754 . . . . . XXXXXXX . . . | . . . . XXX . . . XXX . . XXXXXXX 0.5694 . . . . . XXXXXXX . . . | . . . . XXX . . . XXXXX . XXXXXXX 0.5656 . . XXX . XXXXXXX . . . | . . . . XXX . . . XXXXX . XXXXXXX 0.5420 . . XXX . XXXXXXX . . . | . . . . XXX XXX . XXXXX . XXXXXXX 0.5290 . . XXX . XXXXXXX . . . | . . . . XXX XXX . XXXXX XXXXXXXXX 0.5282 . . XXX . XXXXXXX . . . | . . . . XXX XXX XXXXXXX XXXXXXXXX 0.5191 . . XXX . XXXXXXXXX . . | . . . . XXX XXX XXXXXXX XXXXXXXXX 0.5085 . . XXX . XXXXXXXXX . . | . . . . XXX XXX XXXXXXXXXXXXXXXXX 0.4899 . . XXX . XXXXXXXXX . . | . . . . XXX XXXXXXXXXXXXXXXXXXXXX 0.4688 . . XXX XXXXXXXXXXX . . | . . . . XXX XXXXXXXXXXXXXXXXXXXXX 0.4458 . . XXX XXXXXXXXXXX XXX | . . . . XXX XXXXXXXXXXXXXXXXXXXXX 0.4440 . . XXX XXXXXXXXXXX XXX | . . XXX XXX XXXXXXXXXXXXXXXXXXXXX 0.4327 . . XXX XXXXXXXXXXX XXX | . . XXX XXXXXXXXXXXXXXXXXXXXXXXXX 0.4132 . XXXXX XXXXXXXXXXX XXX | . . XXX XXXXXXXXXXXXXXXXXXXXXXXXX 0.3634 . XXXXX XXXXXXXXXXX XXX | . . XXXXXXXXXXXXXXXXXXXXXXXXXXXXX 0.3483 . XXXXXXXXXXXXXXXXX XXX | . . XXXXXXXXXXXXXXXXXXXXXXXXXXXXX 0.3380 . XXXXXXXXXXXXXXXXXXXXX | . . XXXXXXXXXXXXXXXXXXXXXXXXXXXXX 0.3184 . XXXXXXXXXXXXXXXXXXXXX | . XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 0.3038 . XXXXXXXXXXXXXXXXXXXXX | XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 0.2818 . XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX 0.2241 XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

  20. 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

  21. 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

  22. 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)

  23. 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)

  24. 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

  25. Both Samples’ Response Profiles are Very Similar Overall … (r = .938)

  26. Extrapolating…? – Two Approaches 1. 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)

  27. Profile Matching Approach Scatterplot: Respondents’ correlations with respect to the Special Sample’stwo “subcultural” response profiles

  28. “r2–r1” … a computed variable from information depicted in the scatterplot, wherer1: Pearson r vis-à-vis Cluster 1’s response profiler2: 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

  29. 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

  30. 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)

  31. 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)

  32. 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

  33. [ 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

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

  35. 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?

  36. 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?)

  37. 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

  38. … 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

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