Cultural Distinctions: A Geometric Data Analysis. Johs. Hjellbrekke & Olav Korsnes Dep. of Sociology University of Bergen

Transcription

1 Cultural Distinctions: A Geometric Data Analysis. Johs. Hjellbrekke & Olav Korsnes Dep. of Sociology University of Bergen

2 The critique(s) against Bourdieu s Distinction (1979) Outdated; may have been right in the 1960-ies, but things have changed since then Not relevant outside the French society Deterministic habitus as a trojan horse for determinism Individualization-processes Popular culture is much more autonomous from the legitimate culture than what Bourdieu claims; it has its own hierarchies and specific forms of capital.

3 Some important contributions Beck individualization thesis; social hierarchies less and less important Lamont moral and symbolic boundaries, boundary work, evaluative repertoires Peterson Omnivore-univore, the homology thesis Bryson patterns of rejection/dislike Chan & Goldthorpe Class or status, strong /strict homology or loose homologies? Hestholm, Sakslind & Skarpenes: no moral evalutions of culture and cultural distinctions

4 Varieties in Societal Perceptions. ISSP1999 Social Inequality III Norway Sweden Germany (West) France USA An elite at the top, few in the 3,1 10,3 10,6 12,4 16,2 middle, many at the bottom A society that looks like a pyramide, with an elite at the top, more in the middle, and most at the bottom 10,9 23,9 26,7 49,8 30,6 A pyramide, but with few 19,3 27,6 25,5 23,1 17,9 people at the bottom. A society where most people 56,0 33,0 23,9 12,9 25,3 are in the middle. Many people near the top, only 7,7 1,3 2,0 0,8 2,7 very few at the bottom Can t choose 3,2 3,9 11,3 0,9 5,4 Total

5 Our approach Cultural distinctions through reported practices What correspondence can be found between the oppositions within this space, and oppositions between positions/groups in the social space? I.e.: can a correspondence between positions in social hierarchies and hierarchies of cultural attendance/practices/preferences be found for the Norwegian case? How many groups/clusters can be identified, and what are their profiles with respect to cultural practices? Are the internal oppositions in these clusters the same as in the global space?

6 Construction of the space analytical strategy Data set: Culture and Media Survey 2008, Central Bureau of Statistics N=1194 (respondents yrs old, with a registered occupation) Personal interviews Active variables = 44 Total # of active categories = 102 Total # of passive categories = 0 (!) Standard Multiple Correspondence Analysis (NOT Specific MCA [cf. Le Roux & Rouanet 2004, 2010) Ascending Hierarchical Cluster Analysis Structured Data Analysis Class Specific MCA of a selected subgroup

7 But before we start Let s remind ourselves In the analysis of questionnaires, it is not enough to do a correspondence analysis to do analyses à la Bourdieu. The fundamental social space must be constructed from an extensive set of relevant variables and ample enough to allow the full multidimensional display of individuals. (Rouanet & al. 2000)

8 Variables Attendance of cultural arrangements/events last 12 months Types of events/music types/expositions Q = 14 K= 40

9 Variables TV-preferences; channels and types of programs/shows Program types: Q=9 Channels: Q=6 K=30 I.e: Binary coded variables

10 Radio Channels Q=6 K=12 Binary variables

11 Newspapers Q=9 K=20

12 Axes: Eigenvalues and modified inertia rates Eigenvalue Percentage Percentage, Benzécri s modified rates Cumulated percentages, modified rates Axis Axis Axis

13 Contributions from blocks of Cultural attendence variables TV, channels & programs Radio, Channels & programs Axis Newsp Incl. internet Axis Axis

14 The Cloud of Individuals, fac.plane 1-2

15 Contributions to axis 1 + Active/ +Inactive /Disengaged Engaged Highbrow

16 Contributions to Axis 2 - TV/Media + TV/Media

17 The Cloud of Individuals, fac.plane 1-3

18 Contributions Axis 3 + Traditional Media: TV/Newspapers, +Highbrow + New Media: Internet/Emerging

19 The Cloud of Individuals, fac.plane 2-3

20 Fac.plane 2-3

21 Structuring factors Age Class Education Sex

22 Axis 1: EGP-classes + Active/Engaged +Inactive/Disengaged + Manual Working Class Highbrow + Service Class

23 Axis 1: Educational level +Inactive/ Disengaged + Lower educ levels + Active/ Highbrow Higher educ. levels

24 Axis 2: (Minor) Sex (Differences) Internal opposition at lower secondary educational levels - TV/Media + TV/Media

25 Axis 3: Age + Traditional Media: TV/Newspapers, +Highbrow Older respondents +Inactive + Active/ Highbrow Younger respondents + New Media: Internet/Emerging

26 Subgroups (clusters) within this space Ascending hierarchical cluster analysis Ward s method Done on total inertia, i.e. all the axes in the analysis (all the dimensions in the space) Identified on the basis of similarities/dissimiliarities across the active set of variables Calculated on the basis of the individuals axis coordinates on all dimensions

27 How many clusters? How many clusters? What are their profiles? How large are they? Do they intersect in the factorial planes? 6 retained for interpretation

28 Cluster 1: 26%. Actives/Engaged. Over-represented categories, active variables Over-represented categories, supplementary variables 1-4 art exhib. 12 months Artium highest gen educ One type of art exhibition Women 1-4 theater visits 12 mnths HED 3-4 years One type of concerts EGP 1 (Higher service) 1-4 concerts 12 mnths Underrepresented: 1-4 museum visits 12 mnths No univ. educ Men

29 Cluster 2: 10,5%. Hyperactives/hyper-engaged Over-represented categories, active variables Over-represented categories, supplementary variables 5+ theater Artium gen educ level 5+ art exhibitions HED 5-6 yrs 5+ museums NOK 1 mill+ HH income 5+ concerts HED 7yrs+ 5+ ballet/dance EGP 1 Higher service 5+ cinema Underrepresented National newspapers P2 national radio No university educ.

30 Cluster 3: 4,7%. TV - traditional Over-represented categories, active variables TV Nature TV Other NRK 1 TV News Over-represented categories, supplementary variables No university education Underrepresented: HED 3-4 yrs NRK 2

31 Cluster 4: 8%. TV-/Media cluster Over-represented categories, active variables NRK2 TV debate Over-represented categories, supplementary variables Educ level: Realskole Age group yrs old NRK1 TV news TV info soc P2 Underrepresented: Age group yrs old 2 types of concerts 1-4 museum visits 12 mnths

32 Cluster 5: 25.1%. Music listeners, Over-represented categories, active variables radio/concerts Over-represented categories, No art exhib/types Age group 2 1 type of concert Men 1-4 concerts No TV debate supplementary variables No NRK2 Underrepresented: Age group yrs

33 Cluster 6: 25,8%. Inactives Over-represented categories, active variables No concerts/types No art exhibitions/types No theater No cinema Over-represented categories, supplementary variables Oldest age group: Age group 4 65 yrs + No university education No museums No and Low incomes: festival Underrepresented: No library/library books HED 3-4 yrs, 5-6 yrs 7yrs+

34 6 clusters in fac.plane 1-2

35 4 of 6 clusters in fac.plane 1-3

36 Step 3: Class Specific Analysis (CSA) Specific MCA: Developed by Brigitte Le Roux, restriction of analysis to the categories of interest (Le Roux & Rouanet 2004) Class Specific MCA: Developed by Brigitte Le Roux (see Le Roux & Rouanet 2004, 2010) in order to study a class of indivuals/cases with reference to the complete sample/population, i.e. an analysis of a subcloud with reference to the global cloud. What are the principal axes of the subcloud? How are they interpreted, compared to the the axes in the global cloud?

37 CSA CSA of cluster 2: The Hyper-Active/Hyper- Engaged Internal oppositions at the top of the capital hierarchy High vs. Low frequentation? Omnivores vs. Univores? Univores vs. Univores?

38 Factorial plane 1-2, cloud of individuals

39 Contributing points axis 1 10 categories 91.6% of the contribution + Theater + Museum + Art exhib

40 Contribution points axis 2 18 categories 88.1% of the contribution Univorousness art & music? Opera - NO Omnivorousness art & music

41 Factorial plane 1-3

42 Factorial plane 2-3

43 Contributing points Axis 3 17 categories 83.2% of the contribution + Radio/Internet + Museum visits

44 Thank you for your attention!

45 Basic references Bourdieu, Pierre (1979). La Distinction. Critique Sociale du Jugement. Paris: Ed. De Minuit. Le Roux, Brigitte & Rouanet, Henry (2004). Geometric Data Analysis. From Correspondence Analysis to Structured Data Analysis. Dordrecht: Kluwer - Springer. Le Roux, Brigitte & Rouanet, Henry (2010). Multiple Correspondence Analysis. #163, QASSseries. Thousand Oaks: Sage.