Sociological classifications: The

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Sociological classifications The GESDE
services for classifications involving
occupations, educational qualifications and
ethnicity

• Paul Lambert, University of Stirling
• Talk presented to the Census Programme Workshop
  on Spatial and Social Classifications, University
  of Leeds, 8 June 2010
• This work draws upon materials from the DAMES
  (www.dames.org.uk) project, an ESRC funded
  research Node working on Data Management through
  e-Social Science

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Intro Sociological classifications? GESDE?

• Several key variables in social science
  research are not just sociological, but are much
  debated there
• Complex categorical measures and variable
  operationalisation recommendations/debates
• Individual level measures of social positioning
• GESDE 3 related online services which are
  Grid Enabled Specialist Data Environments
• GEODE the o is for data on Occupations
• GEEDE the e is for data on Educational
  qualifications
• GEMDE the m is for data on ethnic Minorities

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Example Occupational not geographical
inequality
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The e-Social Science endeavoursee
http//www.merc.ac.uk/ for up-to-date links

• A number of UK projects seeking to improve social
  science research by capitalising on emerging
  computer science techniques
• Handling distributed data collaborative
  technologies large and complex data secure data
• The Grid embodies these technologies, but more
  generic terms like e-Social Science Digital
  Social Science are increasingly preferred
• GESDE Grid Enabled Specialist Data
  Environments

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Example Understanding New Forms of Digital
Records (DReSS) http//web.mac.com/andy.crabtree/N
CeSS_Digital_Records_Node/DReSS.html

• transcribed talk
• audio
• video
• digital records
• system logs
• location

video
code tree
transcript
system log
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Todays talk from the Data Management though
e-Social Science node

• DAMES www.dames.org.uk
• ESRC Node funded 2008-2011
• Aim Useful social science provisions
• Specialist data topics occupations education
  qualifications ethnicity social care health
• Mainstream packages and accessible resources
• Resources from data support providers e.g.
  ESDS, CESSDA
• Academics own provisions e.g.
  www.camsis.stir.ac.uk/occunits/distribution.html

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To us Data management means

• the tasks associated with linking related data
  resources, with coding and re-coding data in a
  consistent manner, and with accessing related
  data resources and combining them within the
  process of analysis DAMES Node..
• Usually performed by social scientists themselves
• Pre-analysis tasks (though often revised/updated)
• Inputs also from data providers
• Usually a substantial component of the work
  process
• But may not be explicitly rewarded (and sometimes
  penalised)
• differentiate from archiving / controlling data
  itself

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Some components

• Manipulating data
• Recoding categories / operationalising
  variables
• Linking data
• Linking related data (e.g. longitudinal studies)
• combining / enhancing data (e.g. linking micro-
  and macro-data)
• Secure access to data
• Linking data with different levels of access
  permission
• Detailed access to micro-data cf. access
  restrictions
• Harmonisation standards
• Approaches to linking concepts and measures
  (indicators)
• Recommendations on particular variable
  constructions
• Cleaning data
• missing values implausible responses extreme
  values

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Example recoding data

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Example Linking data

• Linking via ojbsoc00
• c1-5 original data / c6 derived from data / c7
  derived from www.camsis.stir.ac.uk

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..plus the centrality of keeping clear records of
DM activities

• Reproducible (for self)
• Replicable (for all)
• Paper trail for whole lifecycle
• Cf. Dale 2006 Freese 2007
• In survey research, this means using clearly
  annotated syntax files
• (e.g. SPSS/Stata)
• Syntax Examples
• www.dames.org.uk/workshops/
• www.longitudinal.stir.ac.uk

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Part 2 Variables on occupations, educational
qualifications ethnicity

• Well known challenges exploiting survey measures
  of each concept
• ..our response is usually too conservative..
• Better data management could/should allow us to
  get much more from data
• Take account of more precisely measured
  differences
• Scales/ranks from complex categorical measures
• Longitudinal/cross-national comparisons
• Complex multivariate models, interaction effects
• We have something to offer here GESDE

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GESDE Grid Enabled Specialist Data Environments

• Online facilities for collecting together, and
  distributing, specialist data resources
• Occupations GEODE project began 2005
• Education and Ethnicity GEEDE and GEMDE began
  Feb. 2008
• Capacity building aims improving use of measures
  of these concepts by
• improving access to relevant information
• providing training / advice on good practice

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Data curation tool
The curation tool obtains metadata and supports
the storage and organisation of data resources in
a more generic way
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2(a) Data on occupations

• Occupational unit groups standardised lists of
  occupational titles
• E.g. via CASCOT, www2.warwick.ac.uk/fac/soc/ier/pu
  blications/software/cascot/

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..data on occupations..

• find ways of attaching summary information about
  occupations to occupational unit groups

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Comparability problems gt value of documenting
methods comparing alternatives
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GEODE Our contribution

• GEODE acts as a library style service for access
  to occupational information resources
• We encourage people to supply data theyve
  produced, and we upload data ourselves
• Researchers are encouraged to use the portal to
  find and exploit suitable data
• Services search, browse, deposit data, link
  data, user ratings

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GEODE (v1) Occupational data
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Using occupational data Example as a measure of
marked social disadvantage Lambert Gayle (2009)
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All jobs, male scale threshold38.51
Occupational unit groups with gt 90 in BHPS sample
Remember that these jobs scores are
cross-classified by employment status
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Can everyone be linked to occupations? (BHPS
wave 17, excluding NI)
poor poor
N men N fem m f
All 5695 6793
(2) cji Current job, indv 3869 3832 22.4 11.6
(3) rji Current or recent job, indv 4414 4958 26.5 16.9
(4) cjd Current Hld dom job 4250 4636 11.1 9.2
(5) rjd Current/recent Hhld dom job 5293 6210 14.8 13.5
(6) pjd (5) parents job if lt 30 and missing or student 5295 6216 14.8 13.5
(7) pjd2 (5) parents job if missing or student 5623 6686 16.4 15.9
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2(b) Data on educational qualifications

• Similar issues arise with the use of educational
  data
• Specialist resources exist which can enhance
  measures of educational data
• Many users arent aware of alternative coding
  schemes or harmonised approaches
• GEEDE acts as a service for bringing together and
  disseminating relevant data resources on
  educational measures

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Example recoding data

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Family and Working Lives Survey (54 vars per educ
record)
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2(c) Data on ethnicity

• We can conceive of similar information resources
  and data analysis requirements for measures of
  ethnicity
• There are generally fewer published resources /
  agreed standards in this domain
• GEMDE publishes resources but puts more emphasis
  on understanding complex ethnicity data

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why is working with ethnicity data in surveys so
hard?

• - Its sparse - Its collinear (e.g. to age,
  location)
• - Its dynamic (cf. comparative research)

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• Data includes
• Generic specialist studies collecting ethnic
  referents
• ethnic identity nationality, parents
  nationality country of birth language spoken
  religion race complex categorical data
• National research
• Most countries have evolving standard definitions
  of ethnic groups, though not all surveys follow
  them
• Some surveys cover large numbers from many/all
  groups
• Most surveys only have sparse representation of
  most groups
• Comparative research (international/longitudinal)
  
• Seen as highly problematic in many fields except
  immigration studies
• 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.

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EFFNATIS sample (1999) Subjective ethnic
identity
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UK EFFNATIS survey (1999) Heckmann et al 2001
Penn Lambert 2009
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A data management contribution

• Preserve information on what was done with
  categorical data
• Communicate information on what should/could be
  done

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Standardizing categorical data

• Measurement equivalence (e.g. van Deth, 2003)
  is often not feasible for complex categorical
  measures
• 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/approximated by scaling categories in
  one or more dimension of difference

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Effect proportional scaling using parents
occupational advantage
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What was that then?

• We can represent categories through positions on
  a scale
• In turn, we can use position in the dimension as
  a category score which then plugs into a further
  analysis (e.g. regression main and interaction
  effects)
• ..Some options for data on ethnicity..
• Stereotyped Ordered Logistic Regression (SOR)
  models, summarize dimensions of difference
  according to regression predictor values
• e.g. Lambert and Penn, 2001
• Geometric data analysis for distances between
  people, or things
• cf. Prandy, 1979 Bennett et al., 2009
• Assign category scores by hand (a priori or by
  selected average)

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GEMDE seeks to promote replicability /
transparency

• Document your own recodes
• Access somebody elses recodes
• Identify commonly used recodes ( use them..!)

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..and making complex analysis of ethnicity data
easier..

• Organising complex categorical data
• Labelling, recoding, etc
• Effect proportional scaling
• Standardisation
• Interaction terms

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The GEODE model for GEMDE?

• .A service for MUGs and MIRs
• Define/register Minority Unit Groups
• Define/register Minority Information Resources
• Explore data resources and obtain help in
  approaching analysis of complex, sparse data

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What's a MIR?

• 'Minority Information Resource'.
• This is our own terminology. By a MIR, we mean
  any piece of information which supplies
  systematic data on a minority unit group (MUG)
  classification. We've used this term to be
  deliberately similar to the phrase 'Occupational
  Information Resources' that we used on GEODE
• E.g. summary statistical data about the
  categories from and documentation or information
• E.g. recodings which have been used in a
  particular study
• Social scientists are not in general aware of the
  existence of MIRs (cf. wides use of popular
  Occupational Information Resources). In GEMDE we
  seek to publicise little know resources and
  promote their uptake We argue that better
  communication and dissemination of MIRs is in
  fact an important step towards better scientific
  practice of replication and standardisation of
  research.
• In our terms, every MIR necessarily links to a
  MUG (but not every MUG has a MIR).

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The GEMDE prototypeLiferay portal with access
to MUGs and MIRs, first release Jan 2010

• Shibboleth access for registered users
• Guest level access
• Deposit MUGs/MIRs
• Search/browse deposited resources

• Feedback on resources (user ratings)
• Review live data (e.g. pooled LFS records)
• Expert and user quality ratings

gt see the lab session...
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Screenshot here!
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Summary Principles for supporting data on
sociological classifications

• Find specialist data information resources and
  preserve information on them
• Promote easy-to-use means of coding these
  variables and incorporating them in multivariate
  analyses
• Lab session Examples of analysis using
  sociological classifications (using SPSS), and
  our prototype online services for finding
  information resources

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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.
• Office for National Statistics. Social and Vital
  Statistics Division and Northern Ireland
  Statistics and Research Agency. Central Survey
  Unit, Quarterly Labour Force Survey, January -
  March, 2008 computer file. 4th Edition.
  Colchester, Essex UK Data Archive distributor,
  March 2010. SN 5851.
• 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

• Bennett, T., Savage, M., Silva, E. B., Warde, A.,
  Gayo-Cal, M., Wright, D., et al. (2009). Culture,
  Class, Distinction. London Routledge.
• Dale, A. (2006). Quality Issues with Survey
  Research. International Journal of Social
  Research Methodology, 9(2), 143-158.
• Freese, J. (2007). Replication Standards for
  Quantitative Social Science Why Not Sociology?
  Sociological Methods and Research, 36(2),
  153-171.
• Lambert, P. S., Gayle, V. (2009). 'Escape from
  Poverty' and Occupations. Colchester, Essex and
  www.iser.essex.ac.uk/events/conferences/bhps-2009-
  conference/overview Paper presented to the BHPS
  Research Conference, 9-11 July 2009
• Lambert, P. S., Penn, R. D. (2001). SOR models
  and Ethnicity data in LIS and LES Country by
  Country Report. Syracuse University, Syracuse,
  New York 13244-1020 Luxembourg Income Study
  Paper No. 260, Maxwell School of Citizenship and
  Public Affairs.
• Penn, R. D., Lambert, P. S. (2009). Children of
  International Migrants in Europe Comparative
  Perspectives. Basingstoke Palgrave.
• Prandy, K. (1979). Ethnic discrimination in
  employment and housing. Ethnic and Racial
  Studies, 2(1), 66-79.
• Simpson, L., Akinwale, B. (2006). Quantifying
  Stablity and Change in Ethnic Group. Manchester
  University of Manchester, CCSR Working Paper
  2006-05.
• 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.