Sociology Strand Progress• YCS Data (making progress)

• Paper for Utrecht Social Stratification Seminar September 2010

• Master Class 25th March Analysing Complex Social Surveys Peter Lynn ISER Essex University

• Collaborative Activity Application with NCeSS Dames Project & NCeSS e-Stats Project

The Secondary Analysis of Existing Large-Scale Datasets ~ Phase 3

• Our Node – contributing to a improved return on investments in large-scale data resources made by the ESRC (and other stakeholders)

• Thoughts for discussion…

• Our methodological strengths…– Statistical modelling, longitudinal data, correlated data structures, attitudinal data

• Substantive interests– criminology, psychology, education, sociology, health & geography

• Particular contribution (that other Nodes and groups might not be able to make)– consultancy work with social scientists (whose interests are substantive rather than methodological)– training and capacity building (what are our unique strength here?)

• Opportunities

- New and emerging datasets e.g. UKHLS, GUS (BC2), Olympic Cohort??

- Under-analysed and unanalysed data - ONS Longitudinal Study, Scottish Longitudinal Study, N.I. Longitudinal Study- BHPS attitudinal data, BHPS personality factors, a quirky example BHPS smacking data - Edin. Study of Youth Trans & Crime

- Better represent complex data structures with advanced modelling- Household structures, complex household structure post separation- Children in dual households- Dependence and inter-dependence within couples (vegetarianism and voting ?)

Understanding Society: the UK Household Longitudinal Study

www.understandingsociety.org.uk/

Professor Vernon Gayle

• ESRC secured funding for Understanding Society from Large Science Facilities fund (normally for physical science infrastructure), Spring 2006

• UKHLS – Understanding Society (US) incorporates and extends the BHPS• 40,000 Households (approx. 100,000 individuals)

• A new equal probability main panel achieved sample of around 27,000 households (January 2009)

• A boost ethnic minority sample, focussed on five main ethnic minority groups, comprising 4,000 households

• The BHPS sample of approximately 8,400 households. BHPS sample data collection as part of the Understanding Society will start with wave 2 in October 2009

• An Innovation Panel of 1500 households to enable methodological research (January 2008)

Data collection plan• 12 month intervals between interviews

• Continuous fieldwork over 24 month field period, with second wave overlapping with first

• Face-to-face interview at wave 1; mixed mode at wave 2, with a proportion of face to face only

• Individual interview not more than 30 minutes interview administered, plus self completion and consents to link data

• Some data collection by self completion from children aged 10-15 from wave 1

Wave 1 content• Annual repeating measures

• Initial conditions and life history, asked once only

• Rotating and intermittent measures first introduced at wave 1

• Young persons questionnaire for sample members aged 10-15

Long term content plans• Annual content – carried forward from wave 1 (< 50% of

content)

• New annual content – event histories over past year, follow-up questions from event or change of status, age specific modules

• Relatively stable characteristics measured occasionally

• Other intermittent modules repeated every 2/3 years

• Scope for including emerging issues

Role of the Innovation Panel• 1,500 household panel, taking place one year ahead of

main stage

• Role is to allow experimentation and methodological development

• For first 2 waves used to explore mixed mode strategy and impacts on attrition / question response

• Later for new data collection methods and innovative content, e.g. web, diaries, biomarkers and health measurement, etc

US Opportunities

• Starting again, compared with BHPS, an opportunity to review activities and see which are worthwhile to continue, which not

• Focus on new research issues

• Opportunities for mixed methods:– Data linkage – administrative (e.g. education), organisation, spatial– Bio-markers and health indicators– Qualitative data– Other non-standard data: diaries, visual, audio

• Use of different modes– e.g. web to collect data with higher frequency– Experiment with new technology as it is introduced

• Overall aim: to build a robust survey structure within which can experiment and innovate while minimising risk

Importance of the large sample size

• 40,000 households gives an opportunity to explore issues where other longitudinal surveys are too small

• Small subgroups, such as teenage parents or disabled people

• • Analysis at regional and sub-regional levels, allowing

examination of the effects of geographical variation

• Large sample size allows high-resolution analysis of events in time, for example focussing on single-year age cohorts

Full age range

• The Understanding Society sample includes full age range at any point in time – so complements age-focused studies sampling elderly people (e.g. ELSA) or young people (e.g. synthetic birth cohort studies approx. 1,000 birth per year)

• Provide a unique look at behaviours and transitions in mid-life – e.g. for issues of pensions and long-term care, associated with old age, policy setting is influenced by earlier behaviour

Ethnic minority research

• Ethnicity strand includes:

–Boost sample for five key groups (Indian, Pakistani, Bangladeshi, Caribbean, Black African)

–Questions focused on ethnicity issues

Biomedical research• Understanding Society will support collection of a wide range of

biomarkers and health indicators

• Opportunity to assess exposure and antecedent factors of health status, understanding disease mechanisms (e.g. gene-environment interaction, gene-to-function links), household and socioeconomic effects and analysis of outcomes using direct assessments or data linkage

• Challenges:

– Consents – Mode of collection– Processing, storage, access issues– Different approaches in social and biomedical

sciences

Growing Up in Scotland (GUS)• http://www.crfr.ac.uk/gus/

• Currently under-analysed, some aspects unanalysed

• The timing, design and content of the MCS & GUS is obviously not identical, but the data coverage is similar (Gayle, Playford & Lambert 2008)

• Approximate sample size of 8,000 overall – 8,000 children were enrolled into the study in 2005-06– 5,000 were babies (~10 months) born between June 2004 and May 2005 – 3,000 were toddlers (~34 months) born between June 2002 and May 2003.

• These two groups are sometime referred to as the birth cohort (BC1) and the toddler cohort (child cohort) respectively

• New birth cohort (BC2) in January 2011

Scottish Longitudinal Study Data

Scotland’s most unlucky women!

16

Parents / step parents(co-resident)

Parents / step parents(non-resident)

Older siblings

Household(resident)

Other Household(part-time / non resident)

Possible BHPS data sources & UKHLS

Siblings ResearchSimple example do siblings share similar political views?

Would you vote Conservative? (Wave M Youth File)

SiblingNo Yes Tot

SelfNo 272 14 286

Yes 11 6 17

Tot 283 20 303

Phi=.28; McNemar p=.69

Much more potential with youth files, household data and parental information

Using child files and Scottish (boosters) sample

****DON’T QUOTE THIS! ****

Adults with children under 5 years old

Have you ever spanked your child?No Yes

BHPS (originals) 39% 61% MalesBHPS Scots 34% 66%

BHPS (originals) 49% 51% FemalesBHPS Scots 38% 62%

Males n= 733; p=.27; Females n=916; p<.01

Big Five Personality Trait MeasuresFrom: Benet-Martinez, V. & John, O. P. (1998). Los Cinco Grandes across cultures and ethnic groups: Multitrait multimethod analyses of the Big Five in Spanish and English. Journal of Personality and Social Psychology, 75, 729-750.

Volume A: Introduction, Technical Report and Appendices – The BHPS Data British Household Panel Survey User Manual A3-23

Agreeableness items are labeled with an “A”, Conscientiousness items are labeled with a “C”,Extraversion items are labeled with an “E”, Neuroticism items are labeled with an “N”, and Opennessitems are labeled with an “O”.

Please answer each of the following questions using a 1 to 7 scale, where 1 means “does not apply to me at all” and 7 means “applies to me perfectly”.I see myself as someone who . . .

1. (A) Is sometimes rude to others (reverse-scored). 2. (C) Does a thorough job. 3. (E) Is talkative. 4. (N) Worries a lot.5. (O) Is original, comes up with new ideas. 6. (A) Has a forgiving nature. 7. (C) Tends to be lazy (reverse-scored).8. (E) Is outgoing, sociable. 9. (N) Gets nervous easily. 10. (O) Values artistic, aesthetic experiences.11. (A) Is considerate and kind to almost everyone. 12. (C) Does things efficiently. 13. (E) Is reserved (reverse-scored).14. (N) Is relaxed, handles stress well (reverse-scored). 15. (O) Has an active imagination.

Asked in Wave 0 (2005)

Geographical Data

Over the past year ISER, has released a range of geographical identifiers about the location of BHPS households for all the currently available waves of BHPS data. These range from Local Authority Districts to Lower Level Super Output Areas, down to Postcode Grid References which indicate the position of a household’s postcode to the nearest metre.

The identifiers allow users to merge to the BHPS area characteristics such as measures of deprivation, air pollution, the location of telephone masts and much more and will be of considerable interest to researchers looking at issues such as the effect that neighbourhood environment has on people’s changing lives. Area classifications for Output Areas and Rural-Urban Indicators. are also available.

While some of the geographical identifiers may be downloaded from the ESDS/UK Data Archive website after agreeing to five confidentiality conditions, a special license agreement is required for the more detailed geographical identifiers such as Wards. Postcode Grid References will be accessed using Secure Data Service which offers secure remote access for approved researchers.

The Secondary Analysis of Existing Large-Scale Datasets ~ Phase 3

• Our Node – contributing to a improved return on investments in large-scale data resources made by the ESRC (and other stakeholders)

• Thoughts for discussion…

• Our methodological strengths…– Statistical modelling, longitudinal data, correlated data structures, attitudinal data

• Substantive interests– criminology, psychology, education, sociology, health & geography

• Particular contribution (that other Nodes and groups might not be able to make)– consultancy work with social scientists (whose interests are substantive rather than methodological)– training and capacity building (what are our unique strength here?)

• Opportunities

- New and emerging datasets e.g. UKHLS, GUS (BC2), Olympic Cohort??

- Under-analysed and unanalysed data - ONS Longitudinal Study, Scottish Longitudinal Study, N.I. Longitudinal Study- BHPS attitudinal data, BHPS personality factors, a quirky example BHPS smacking data - Edin. Study of Youth Trans & Crime

- Better represent complex data structures with advanced modelling- Household structures, complex household structure post separation- Children in dual households- Dependence and inter-dependence within couples (vegetarianism and voting ?)