Dealing with data on ethnicity: Principles and practice

Paul Lambert, University of Stirling

Talk presented to the DAMES Node workshop on ‘Data on ethnicity in social survey reseach’ Stirling, 28th Jan 2010.

DAMES (www.dames.org.uk) is an ESRC funded research Node working on ‘Data Management through e-Social Science’

..dealing with data on ethnicity

1) Handling/enhancing categorical data

(‘data management’)

2) Handling/enhancing data on ethnicity

2

3

Categorical data is important..

Principal social survey datumo Basis of most social research reports/analyses/comparisons

It’s rich and complex o We’re often interested in very fine levels of detail / differenceo We usually recode categories in some way for analysis

…how categorical data is managed is of great consequence to the results of analysis…Choices about recoding, boundaries, contrasts made[e.g. RAE analysis: Lambert & Gayle 2009]

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EFFNATIS sample (1999): Subjective ethnic identity

30. English, White, Cosmopolitan (2,10, 1 0.12 86.0329. English, European and White-British 20 2.43 85.91 28. European and White-British (11,13) 7 0.85 83.48 27. White-Italian 1 0.12 82.62 26. English & White-British (2,13) 8 0.97 82.5025. White-British & Cosmopolitan (13,16 5 0.61 81.53 24. English, White-British (2,13) 41 4.98 80.92 23. English & White (2,10) 54 6.56 75.9422. Indian-British, Asian-British (7,15 9 1.09 69.38 21. Indian & British (1,4) 1 0.12 68.29 19. British Moslem 6 0.73 68.17 18. Indian, Asian (4,9) 2 0.24 67.44 17. Moslem 12 1.46 67.19 16. Cosmopolitan 7 0.85 65.74 15. Asian-British 65 7.90 64.88 13. White-British 60 7.29 56.99 11. European 6 0.73 49.70 10. White 7 0.85 48.97 9. Asian 6 0.73 48.12 8. Bangladeshi-British 22 2.67 47.39 7. Indian-British 34 4.13 44.71 6. Pakistani-British 73 8.87 40.58 5. Bangladeshi 10 1.22 31.71 4. Indian 10 1.22 30.50 3. Pakistani 25 3.04 29.28 2. English 79 9.60 26.25 1. British 137 16.65 16.65 yourself? Freq. Percent Cum. describes how you would describe Q.129 Which of the following best

90. White-British, Ukrainian 1 0.12 98.91 89. Indian, Asian-British (4,15) 1 0.12 98.7888. Indian, Black, Asian-British (4,12, 1 0.12 98.6687. Indian-British, Black-British (7,14 1 0.12 98.54 86. English, Indian (2,4) 4 0.49 98.42 85. European, White-British, Irish 1 0.12 97.93 83. Pakistani-British, Italian 1 0.12 97.8182. English, Pakistani-British, Asian ( 1 0.12 97.69 80. Human being 2 0.24 97.5779. English, Indian-British, White, Asi 1 0.12 97.33 78. Pakistani-British, Asian, Moslem 1 0.12 97.21 77. English, Pakistani (2,3) 3 0.36 97.08 73. British with Baltic-Slav origins 1 0.12 96.7272. English, White-British, Cosmopolita 2 0.24 96.60 71. Irish-English 1 0.12 96.35 70. English, White, European (2,10,11) 3 0.36 96.23 69. English, White, Irish 1 0.12 95.8768. Pakistani, Asian-British, Moslem, F 1 0.12 95.75 67. Pakistani, Asian-British (3,15) 1 0.12 95.63 66. Humanoid 1 0.12 95.50 65. Lancastrian 3 0.36 95.38 64. British, European (1,11) 2 0.24 95.02 63. Bangladeshi, Asian, Black (5,9,12) 1 0.12 94.7862. Pakistani-British, Black, Asian-Bri 2 0.24 94.65 61. English, Pakistani-British (2,6) 2 0.24 94.4160. British, White, European, Cosmopoli 1 0.12 94.1759. English, European, White-British, C 3 0.36 94.05 58. Individual 2 0.24 93.68 56. English, European (2,11) 2 0.24 93.44 55. Scottish 1 0.12 93.20 54. British, White (1,10) 2 0.24 93.07 53. British, English (1,2) 9 1.09 92.8352. English, Indian, Asian-British (2,4 1 0.12 91.7451. Pakistani-British, Indian-British ( 1 0.12 91.62 50. English, Asian-British (2,15) 2 0.24 91.49 49. English, Indian-British (2,7) 2 0.24 91.2548. English, Indian-British, Black, Asi 2 0.24 91.01 47. Indian-British, Asian (7,9) 5 0.61 90.7746. Indian, Asian, Asian-British (4,9,1 1 0.12 90.16 45. Neapolitan 1 0.12 90.0444. English, Pakistani-British, Indian- 2 0.24 89.9143. English, Indian-British, Asian-Brit 1 0.12 89.67 42. Pakistani-British, Asian (6,9) 6 0.73 89.55 41. Black, Asian-British (12,15) 2 0.24 88.8240. Pakistani-British, Asian-British (6 2 0.24 88.58 39. Pakistani-born British 1 0.12 88.34 38. Pakistani, Black (3,12) 1 0.12 88.2137. Pakistani-British, Asian, Black-Bri 1 0.12 88.0936. Pakistani-British, Black-British, A 1 0.12 87.9735. Pakistani-Moslem living in Great Br 1 0.12 87.85 34. Kashmiri 4 0.49 87.73 33. Pakistani, Asian (3,9) 3 0.36 87.2432. Pakistani-British, Asian-British (6 6 0.73 86.8831. English, Indian-British, Asian-Brit 1 0.12 86.15

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UK EFFNATIS survey (1999)

[Heckmann et al 2001]

79. Islamic 0 0 1 0 1 78. Dramatic 0 0 4 0 4 77. Bollywood 0 0 1 0 1 75. Ambient 1 0 0 0 1 72. Lo-fi 1 0 0 0 1 68. Urdu 0 2 0 0 2 65. Hindi 0 1 2 0 3 64. Classical Hindi 0 1 1 0 2 63. English 0 2 0 0 2 60. Dance Floor 2 0 0 0 2 51. Goth 1 0 0 0 1 50. Break Beat 1 1 0 0 2 48. Swing 0 1 0 0 1 47. Blues 1 0 0 0 1 46. Love songs / ball 0 2 0 0 2 45. Rock 'n' Roll 1 0 0 0 1 44. Soft Rock 2 1 0 0 3 43. Alternative 2 0 0 0 2 41. Drum 'n' Bass 3 0 0 0 3 40. All 19 5 2 0 26 38. Asian Pop Music 0 2 0 0 2 37. Jungle 1 0 0 0 1 36. Bangra 0 5 2 0 7 35. Indian 0 11 3 1 15 31. Punk 3 0 0 0 3 30. Country 2 0 0 0 2 28. Reggae 0 3 2 1 6 27. Easy Listening 3 0 0 0 3 26. Grunge 1 0 0 0 1 24. Eighties 2 0 0 0 2 23. Seventies 3 0 0 0 3 22. Sixties 4 0 0 0 4 21. Classical 4 2 1 1 8 20. Jazz 2 1 1 0 4 18. Folk 1 0 0 0 1 17. Motown 1 0 0 0 1 16. Rock / Heavy Meta 32 2 2 0 36 15. Indie / Britpop 46 4 3 1 54 14. Rap 4 11 2 1 18 13. Acid Jazz 1 0 0 0 1 12. Hardcore 3 0 0 0 3 11. Garage 0 2 1 0 3 10. Techno 1 0 0 0 1 9. House 18 0 1 0 19 8. Trance 0 0 1 0 1 7. Asian Music 0 11 12 2 25 6. Hip Hop 5 6 2 0 13 5. R'n'B 19 16 5 5 45 4. Soul 8 31 10 3 52 3. Dance 94 5 9 1 109 2. Chart 27 2 0 0 29 1. Pop 107 32 20 4 163 music1b 1. Autoch 2. Pakist 3. Indian 4. Bangla Total

Total 97 601 698 4. Bangladeshi 6 14 20 3. Indian 32 56 88 2. Pakistani 58 105 163 1. Autochthonous 1 426 427 uketh2 1. Ethnic 2. No EM Total music, by ethnicity Favourite type of

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1 11 12 13 20 21 22 23 24 31 32 33 34 41 42 51 52 61 71 72 73 74 81 82 83 91 92 93

maximum: 335

Men's jobs (frequencies)

90 86

85 84

83 82

81 80

78 76

75 74

73 72

71 70

69 66

64 63

62 61

60 59

55 54

53 52

51 50

49 48

46 45

44 43

42 41

40 39

37 36

35 34

33 32

31 30

29 28

27 26

25 24

23 22

21 20

19 18

17 16

15 14

13 12

11 10

0

1 11 12 13 20 21 22 23 24 31 32 33 34 41 42 51 52 61 71 72 73 74 81 82 83 91 92 93

maximum: 895

Women's jobs (frequencies)

Source: British Household Panel Survey, last reported current jobs of adults, waves 1-17, N Males = 10223; N Females=9934X-asis shows ISCO-88 Sub-Major group of job; Y-axis shows ISCO-88 3rd and 4th digit codes.

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Data management and categorical data

In DAMES, we identify three important categorical variables (occupations, educational qualifications, ethnicity), and collect information about them in order to improve ‘data management’ and hence exploitation of such data

‘Key’ social science variables Existing resources (and metadata & support on those resources) UK and beyond

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‘Occupational Information Resources’

Small databases (square electronic files) linking lists of occupational positions (occupational unit groups) with information about those positions

Many existing resources already used in academic research (> 1000)

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Educational information resources

Small databases (often on paper) linking lists of educational qualifications with information about them

Many existing resources (>500), but less communication between them

[Part of UK scheme from ONS (2008)]

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Ethnic Minority/Migration Information Resources

Data which links measures of ethnicity / migration status with other information

In high demand, but few existing resources (? < 500)

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Standardizing categorical data

‘Standardization’ refers to treating variables for the purposes of analysis, in order to aid comparison between variables

o {In the terminology of survey research analysts}

1. Arithmetic standardization to re-scale metric values [zi = (xi – x) / sd]

2. Ex-ante harmonisation (during data production) [ensuring measures of the same concept, collected from different contexts, are recorded in coordinated taxonomies]

3. Ex-post harmonisation [adapting measures of the same concept, collected from different contexts, using a coordinated re-coding procedure]

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The big issue: standardization for comparisons

‘Comparisons are the essence’ [Treiman, 2009: 382]

↔ to make statements about differences [in measures] over contexts

Categorical data is highly problematic..

Can’t immediately conduct arithmetic standardization Struggle to enforce harmonised data collection

• ..which may not in any case be suitable.. Struggle to achieve ex-post harmonisation

• Non-linear relations between categories• Shifting underlying distributions

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Two conventional ways to make comparisons [e.g. van Deth 2003]

Measurement equivalence= ex ante harmonisation (or ex post harmonisation)

Meaning equivalence= Arithmetic standardisation (or ex ante or ex post harmonisation)

Much comparative research flounders on an insufficient recognition of strategies for equivalence

(“One size doesn’t fit all, so we can’t go on”)

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Measurement equivalence

Measurement equivalence by assertion

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Measurement equivalence can go wrong

Show tabplot here

Unskilled

Skilled manual

Petty-bourg.

Non-manual

Salariat

Source: Females from LFS/GHS, using data from Li and Heath (2008)

percent of year category

Goldthorpe class scheme harmonised over time

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Meaning equivalence

For categorical data, equivalence for comparisons is often best approached in terms of meaning equivalence

(because of non-linear relations between categories and shifting underlying distributions)

(even if measurement equivalence seems possible)

Arithmetic standardisation offers a convenient form of meaning equivalence by indicating relative position with the structure defined by the current context

For categorical data, this can be achieved by scaling categories in one or more dimension of difference

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Managers and Administrators

Professional

Associate professional and technical

Clerical and secretarial

Craft and related

Personal and protective servicesSales

Plant and machine operativesOther occupations

.

higher degree

first degree

teaching qf

other higher qf

nursing qf

gce a levels

gce o levels or equiv

commercial qf, no o levels

cse grade 2-5,scot grade 4-5apprenticeship

other qf

no qf

.white

black-carib

black-african

black-other

indianpakistani

bangladeshi

chinese

other ethnic grp

2030

4050

0 1 2 3Source: British Household Panel Survey 2007, adults aged 18+ and father's Cambridge Scale score.Points at 1-3 show category mean. Points at 0 show individual values (scaled mean=28, sd=6; pop. mean=28, sd=18).

‘Effect proportional scaling’ using parents’ occupational advantage

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What we do and what we ought to do (when standardizing categories)

Research applications tend to select a favoured categorisation of a concept and stick with it Due to coordinated instructions [e.g. Blossfeld et al. 2006] Due to perceived lack of available alternatives Due to perceived convenience

To make statistical analyses more robust we should… Operationalise and deploy various scalings and arithmetic

measures Try out various of categorisations and explore their distributional

properties … and keep a replicable trail of all these activities..

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2) Handling data on ethnicity & standardizing categorical data

GESDE projects are concerned with allowing social science researchers to navigate, and exploit, heterogeneous information resources

Occupational Information Resources (GEODE) Educational Information Resources (GEEDE) Ethnic minority/Migration Information Resources (GEMDE)

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Plenty of interest, and data, on ‘ethnic minority groups’, ‘immigration’, ‘immigrants’

Data includes: Generic & specialist studies collecting ethnic ‘referents’ ‘ethnic identity’; ‘nationality’, parents’ nationality; country of birth;

language spoken; religion; ‘race’

National research and data management: Most countries have evolving standard definitions of ethnic groups

International research and data management Seen as highly problematic in many fields except immigration data Lambert, P.S. (2005). Ethnicity and the Comparative Analysis of

Contemporary Survey Data. In J. H. P. Hoffmeyer-Zlotnick & J. Harkness (Eds.), Methodological Aspects in Cross-National Research (pp. 259-277). Manheim: ZUMA-Nachrichten Spezial 11.

…but working with ethnicity data in surveys is hard…!

- It’s sparse - It’s collinear (e.g. to age)

- It’s dynamic (cf. comparative research)

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Ethnic group in the World Values Survey - Britain

Count

18 0 0 0 18

0 1484 0 999 2483

0 0 1 0 1

15 0 0 0 15

1 0 0 0 1

0 0 3 0 3

0 0 11 0 11

0 0 1 0 1

0 0 4 0 4

0 0 12 0 12

9 0 2 0 11

0 0 7 0 7

1124 0 1044 0 2168

0 0 8 0 8

1167 1484 1093 999 4743

-5 Missing; Unknown

-4 Not asked

-1 Don´t know

40 Asian

70 Asian - Central (Arabic)

80 Asian - East (Chinese,Japanese)

90 Asian - South (Indian,Hindu, Pakistani,Bangladeshi)

130 Bangladeshi

200 Black African

210 Black-Caribbean

220 Black-Other / Black

810 Pakistani

1400 White / CaucasianWhite

8000 Other

Total

1981-1984 1989-1993 1994-1999 1999-2004

Wave

Total

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Ethnic group in the World Values Survey - Mexico

Count

0 1 0 1

0 0 29 29

0 832 0 832

0 364 0 364

5 8 0 13

0 84 0 84

7 14 3 24

544 0 0 544

240 0 564 804

346 0 648 994

86 0 0 86

0 0 25 25

303 335 254 892

0 685 12 697

1531 2323 1535 5389

-5 Missing; Unknown

-2 No answer

-1 Don´t know

70 Asian - Central (Arabic)

80 Asian - East (Chinese,Japanese)

90 Asian - South (Indian, Hindu,Pakistani, Bangladeshi)

220 Black-Other / Black

310 Coloured (medium)

320 Coloured (dark)

330 Coloured (light)

630 Indian (American)

640 Indigenous

1400 White / Caucasian White

8000 Other

Total

1989-1993 1994-1999 1999-2004

Wave

Total

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UK: ONS & ESDS data guides

Input harmonisation within decades Output harmonisation between decades

o Bosveld, K., Connolly, H., & Rendall, M. S. (2006). A guide to comparing 1991 and 2001 Census ethnic group data. London: Office for National Statistics.

Academic strategies – ad hoc ‘black’ group, etcAddition of extra categories over timeMixed ethnicities, marriages…

UK Focus on ‘ethnic identity’, lack of attention to alternative referents

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Comparative research solutions?

Measurement equivalence might be achieved by:

o Survey data collection o Connecting related groupso Longitudinal linkage

Functional equivalence for categories: o Simplified categorical distinctions o Immigrant cohorts o Scaling ethnic categories

…Principles and practice…

3 themes in DAMES ought, in our perspective, to help here

1)Replicability / transparency

2)Plurality of approaches

3)Ease access (to off-putting operations)

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Replicability / transparency

Document your own recodes Access somebody else’s recodes Identify commonly used recodes (& use them..!)

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Plurality of approaches Diminishing excuses for not trying out

multiple operationalisations…

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0.0

5.1

.15

ES5

ES2E9

E6E5

E3E2

G13G11

G10G7

G5G3

G2K4

R7WR

WR9O17

O8O4

MNI9

I99CM

CFCM2

CF2CG

ISEISIOP

AWMWG1

WG2WG3

GN1

Increase in R-squared Increase in BIC

Britain-.

050

.05

.1.1

5

ES5

ES2E9

E6E5

E3E2

G13G11

G10G7

G5G3

G2K4

R7WR

WR9O17

O8O4

MNI9

I99CM

CFCM2

CF2CG

ISEISIOP

AWMWG1

WG2WG3

GN1

Sweden

Source: BHPS and LNU 1991, adults aged 23-55 in work in 1991, N=4536 Britain, 2504 Sweden. Model 1: ISEI = linear age + gender ; Model 2: ISEI = (Model 1) + occupation-based social classificationGraph shows improvement in R2 for OLS regression, Model 2 v's Model 1,plus scaled BIC statistic (Model 2 BIC - Model 1 BIC / Model 1 BIC). Unweighted data.

Explanatory power of schemes in predicting father's ISEI

Figure 12: R-2 and BIC for Fathers ISEI

Making complex things easier

Organising complex categorical dataLabelling, recoding, etc

Effect proportional scalingStandardisation Interaction terms

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1. White

2. Mixed 3. Indian

5. Bangladeshi

6. Other Asian

7. Black-Caribbean

8. Black African

9. Other Black

10. Chinese

11. Other ethnic group

4. Pakistani

-2-1

01

2

Source: BHPS wave 17, n = 12626, % 'White' = 97.3

Identified principally by age, gender attitudes and household incomeSOR model dimension scores for BHPS ethnic groups

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Data used Department for Education and Employment. (1997). Family and Working Lives

Survey, 1994-1995 [computer file]. Colchester, Essex: UK Data Archive [distributor], SN: 3704.

Heckmann, F., Penn, R. D., & Schnapper, D. (Eds.). (2001). Effectiveness of National Integration Strategies Towards Second Generation Migrant Youth in a Comparative Perspective - EFFNATIS. Bamberg: European Forum for Migration Studies, University of Bamberg.

Inglehart, R. (2000). World Values Surveys and European Values Surveys 1981-4, 1990-3, 1995-7 [Computer file] (Vol. 2000). Ann Arbor, MI: Institute for Social Research [Producer]; Inter-university Consortium for Political and Social Research [Distributor].

Li, Y., & Heath, A. F. (2008). Socio-Economic Position and Political Support of Black and Ethnic Minority Groups in the United Kingdom, 1972-2005 [computer file]. 2nd Edition. Colchester, Essex: UK Data Archive [distributor], SN: 5666.

University of Essex, & Institute for Social and Economic Research. (2009). British Household Panel Survey: Waves 1-17, 1991-2008 [computer file], 5th Edition. Colchester, Essex: UK Data Archive [distributor], March 2009, SN 5151.

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References Agresti, A. (2002). Categorical Data Analysis, 2nd Edition. New York: Wiley. Lambert, P. S., & Gayle, V. (2009). Data management and standardisation: A

methodological comment on using results from the UK Research Assessment Exercise 2008. Stirling: University of Stirling, Technical paper 2008-3 of the Data Management through e-Social Science research Node (www.dames.org.uk)

Long, J. S. (2009). The Workflow of Data Analysis Using Stata. Boca Raton: CRC Press.

Simpson, L., & Akinwale, B. (2006). Quantifying Stablity and Change in Ethnic Group. Manchester: University of Manchester, CCSR Working Paper 2006-05.

Stevens, S. S. (1946). On the theory of scales of measurement. Science, 103, 677-680.

Treiman, D. J. (2009). Quantitative Data Analysis: Doing Social Research to Test Ideas. New York: Jossey Bass.

van Deth, J. W. (2003). Using Published Survey Data. In J. A. Harkness, F. J. R. van de Vijver & P. P. Mohler (Eds.), Cross-Cultural Survey Methods (pp. 329-346). New York: Wiley.