1. Children’s subjective well-being Findings from national surveys in England International Society for Child Indicators Conference, 27 th July 2011

2. Overview Gwyther Rees

3. The research programme Collaboration between The Children’s Society and University of York Main aims:  Understand the concept of well-being taking full account of young people’s perspectives  To establish self-report measures and use these to: Identify reasons for variations in well-being Monitor changes in well-being over time

4. Principles  Focus on young people’s views and ideas  Adopt a holistic approach  Take account of diversity  Focus on present as well as future well-being  Adopt a positive approach

5. Research phases 2005 survey Exploratory qualitative research with 8,000 young people aged 13 to 15 in schools, plus literature review 2008 survey 7,000 young people aged 10 to 15 in schools 2010 survey 5,400 young people aged 8 to 15 in schools Quarterly surveys 2,000 young people aged 8 to 15 in households every 3 months from July 2010

6. 2008 survey

7. Overall well-being Three measures:  Happiness with life as a whole (0 to 10)  Cantril’s ladder (0 to 10)  Shortened version of Huebner’s life satisfaction scale (5 items) (0 to 20)

8. 2008 survey: Overall well-being Most young people happy and satisfied But around 7% of young people relatively unhappy – low well-being

9. Variations in well-being  Decline in well-being with age  Slightly lower well-being amongst females  Some variation also re: family structure, family economic status  Little or no variation by factors such as ethnicity, religion, country of birth  All of these factors only explained around 7% of variation in overall well-being  However…

10. Life events: Being bullied

11. Family relationships ‘My family gets along well together’

12. Conclusions from 2008 Explaining variations in well-being:  Individual and family factors explain relatively little of variation  Poverty needs further exploration  Recent life events may play a more significant role  Other research suggests that we need to take account of personality Need to further investigate different approaches to measuring subjective well-being

13. 2010 survey

14. Survey method  A questionnaire was developed after cognitive testing and piloting  Two-stage cluster sampling  Participants filled the questionnaire online  Administered by NFER  Data collection took place between December 2010 and January 2011  Over 5,400 young people aged 8 to 15 from mainstream primary and secondary school in England took part

15. Data processing and analysis  Data cleaning and analysis by SPSS  Checking psychometric properties by factor analysis, Cronbach’s Alpha  Univariate analysis - mean or percentages  Bivariate analysis – parametric and non- parametric  Multivariate analysis - Multiple linear regression, logistic regression and tobit regression Preliminary findings only - limitations

16. Today’s presentations 1.Approaches to measuring children’s subjective well-being 2.Life events and subjective well-being 3.Personality and subjective well-being 4.Child-centred measures of child poverty and links with subjective well-being

17. Approaches to measuring children’s well-being

18. Happiness with life as a whole Single item measure (0 to 10) Mean = 7.6. Below the mid-point = 9.2%.

19. Cantril’s ladder Single item measure – ‘worst possible life’ to ‘best possible life’ (0 to 10) Mean = 7.5. Below the mid-point = 7.8%.

20. Life satisfaction Shortened version of Huebner’s Student Life Satisfaction Scale. Five items measured on five- point Likert scale:  My life is going well  My life is just right  I wish I had a different kind of life  I have a good life  I have what I want in life Single factor. Cronbach’s alpha = 0.86. However queries about reliability with children below the age of 10.

21. Life satisfaction

22. Sum of five items (0 to 20) Mean = 14.4. Below the mid-point = 10.3%.

23. Correlations Cantril’s ladderHappiness with life Life satisfaction.742.741 Cantril’s ladder.739

24. Other properties Missing data Test-retest reliability Life satisfaction15%*.84 Cantril’s ladder<1%.59 Happiness with life<1%.63

25. Distributions for different age groups

26. Associations with other variables A mixed picture, although the differences are not large:  Life satisfaction is most strongly associated with age and gender. Cantril’s ladder is least strongly associated.  Cantril’s ladder is most strongly associated with family economic status  Happiness with life is most strongly associated with recent experiences of bullying

27. Extending the approach Our research also measures subjective well-being in specific domains, e.g.:  Family relationships  School  Appearance  Amount of choice in life We have used single and multi-item measures across these domains. In 2008 survey found that single item measures had almost as much explanatory power as multi- item measures re: life satisfaction.

28. Example: Family relationships Single item (from 0 to 10):  How happy are you with your relationships with your family? Multi-item (5 items each on 5 point Likert scale), e.g.:  I enjoy being at home with my family  My parents (or carers) treat me fairly

29. Reliability and stability  Test-retest reliability of single item measures in the range 0.48 (health) to 0.72 (family relationships).  Reliability for multi-item scales generally higher.  However, single-item measures relatively stable across four waves of survey work – mean scores and rank order of domains

30. Selected domain means: quarterly survey, four waves

31. Other learning points from domain measures Queries re: wording of statements in multi-item scales – e.g. ‘ My family gets along well together ’, ‘ My parents and I do fun things together ’.  Measures of subjective well-being?  Normative assumptions?  Completeness? Multi-item measures do not necessarily show stronger associations with other variables than single-item measures.

32. Conclusions Multi-item measures:  Good reliability and short-term stability  Particularly suitable for small samples and measures of change. Single item measures:  Lower levels of missing data  Reasonably stable for large samples  Contain less assumptions / more open? Further cross-national research needed to explore validity and reliability and relative merits.