Publication - Research and analysis

Growing Up in Scotland: Birth Cohort 2. Results from the First Year

Published: 19 Feb 2013
ISBN:
9781782563617

This Growing up in Scotland report provides a detailed insight into the first set of data collected from the study’s second birth cohort – representative of all children born in Scotland between 1st March 2010 and 28th February 2011 – around the time they were all aged 10 months old.

Growing Up in Scotland: Birth Cohort 2. Results from the First Year
Appendix A

Appendix A

Technical notes

Data collection

Mode of data collection

Interviews were carried out in participants' homes, by trained social survey interviewers using laptop computers (otherwise known as CAPI - Computer Assisted Personal Interviewing). The interview was quantitative and consisted almost entirely of closed questions. There was a brief, self-complete section in the interview in which the respondent, using the laptop, inputed their responses directly into the questionnaire programme.

At this sweep 1, primarily because of the inclusion of questions on the mother's pregnancy and birth of the sample child, interviewers were instructed as far as possible to undertake the interview with the child's mother. Where this was not possible or appropriate, interviews were conducted with the child's main carer who may have been the child's father, a grandparent or other carer.

Length of interview

Overall, the average interview lasted around 74 minutes. The median interview length was
69 minutes.

Timing of fieldwork

Fieldwork was undertaken over a 14-month period commencing in January 2011. The sample was issued in 12 monthly waves at the beginning of each month and each month's sample was in field for a maximum period of two-and-a-half months. For example, sample 2 was issued at the beginning of February 2011 and remained in field until mid-April 2011.

To ensure that respondents were interviewed when their children were approximately the same age, each case was assigned a 'target interview date'. This was defined as the date on which the child turned 101/2 months old. Interviewers were allotted a four-week period based on this date (two weeks either side) in which to secure the interview. In difficult cases, this period was extended up to and including the child's birthday which allowed a further four weeks.

Further details of key analysis variables

Equivalised annual household income

The income that a household needs to attain a given standard of living will depend on its size and composition. For example, a couple with dependent children will need a higher income than a single person with no children to attain the same material living standards. 'Equivalisation' means adjusting a household's income for size and composition so that we can look at the incomes of all households on a comparable basis.

We measure total household income using a single question asked to the mother (or main carer) of the GUS child. This question asks the mother to indicate the total income of their household from all sources before tax - including benefits, interest from savings and so on. Respondents are asked to choose from 17 income bands, ranging from 'Less than £3,999' to '£56,000 or more'.

The way GUS collects income information is different from the more specialised income surveys. For example, the Family Resources Survey (FRS), used as the basis for HBAI and SHBAI, asks each adult household member about their own income and totals household income from all sources. The FRS also verifies income amounts during the survey interview, for example by asking respondents to show details of pay slips and benefit awards.

Clearly there are likely to be differences in quality when just one question collects information on total income, when this is asked about the household rather than the individual, and when banded income is used.

On the other hand, there are indications that prior questioning on sources of income (as is the case in GUS) might improve the reporting of income. Furthermore, the loss of information in using income bands rather than a continuous measure is minor when looking at the lower end of the income distribution as most of the loss of variation is in the top (uncapped) category. Overall, the loss in accuracy of income estimates obtained from a single question tends not to be 'catastrophic' (Micklewright and Schnepf, 2007, p.20) and have to be weighed against the cost and feasibility of collecting detailed income information in GUS given the competing demands from other topics in the survey.

Socio-economic classification (NS-SEC)

The National Statistics Socio-economic Classification (NS-SEC) is a social classification system that attempts to classify groups on the basis of employment relations, based on characteristics such as career prospects, autonomy, mode of payment and period of notice. There are fourteen operational categories representing different groups of occupations (for example higher and lower managerial, higher and lower professional) and a further three 'residual' categories for full-time students, occupations that cannot be classified due to a lack of information or other reasons. The operational categories may be collapsed to form a nine, eight, five or three category system.

This report uses a five category system in which respondents and their partner, where applicable, are classified as managerial and professional, intermediate, small employers and own account workers, lower supervisory and technical, and semi-routine and routine occupations. The variable is measured at respondent, partner or household level. For the household variables, in couple families this corresponds to the highest classification amongst the respondent and his/her partner.

Parental level of education

The respondent was asked to provide information on the nature and level of any school and post-school qualifications they and their partner, where applicable, had obtained. Qualifications are grouped according to their equivalent position on the Scottish Credit and Qualifications Framework which ranges from Access 1 to Doctorate. These are further banded to create the following categories: Degree-level academic or vocational qualifications, Higher Grades or equivalent vocational qualification (eg. SVQ 3), Upper-level Standard Grades (grades 1 to 4) or equivalent vocational qualification (eg. SVQ 1 or 2), Lower-level Standard grades (grades 5 to 7) or equivalent vocational qualifications (eg. Access 1 or 2, National Certificates). The highest qualification is defined for each parent and a household level variable can also be calculated. In couple families this corresponds to the highest classification amongst the respondent and his/her partner.

Area deprivation (SIMD)

Area deprivation is measured using the Scottish Index of Multiple Deprivation (SIMD) which identifies small area concentrations of multiple deprivation across Scotland. It is based on 37 indicators in the seven individual domains of Current Income, Employment, Health, Education Skills and Training, Geographic Access to Services (including public transport travel times for the first time), Housing and a new Crime Domain. SIMD is presented at data zone level, enabling small pockets of deprivation to be identified. The data zones, which have a median population size of 769, are ranked from most deprived (1) to least deprived (6,505) on the overall SIMD and on each of the individual domains. The result is a comprehensive picture of relative area deprivation across Scotland.

In this report, the data zones are grouped into quintiles. Quintiles are percentiles which divide a distribution into fifths, ie., the 20th, 40th, 60th, and 80th percentiles. Those respondents whose postcode falls into the first quintile are said to live in one of the 20% least deprived areas in Scotland. Those whose postcode falls into the fifth quintile are said to live in one of the 20% most deprived areas in Scotland.

Analysis of BC2 data uses SIMD 2009, whereas BC1 data uses SIMD 2006. Further details on SIMD can be found on the Scottish Government website:
http://www.scotland.gov.uk/Topics/Statistics/SIMD/Overview

Scottish Government Urban Rural Classification

The Scottish Government Urban Rural Classification was first released in 2000 and is consistent with the Government's core definition of rurality which defines settlements of 3000 or less people to be rural. It also classifies areas as remote based in drive times from settlements of 10,000 or more people. The definitions of urban and rural areas underlying the classification are unchanged.

The classification has been designed to be simple and easy to understand and apply. It distinguishes between urban, rural and remote areas within Scotland. The classification can be used in several forms each denoting different levels of detail. Within this report, we have mostly used the sixfold classification which includes the following categories:

  • Large urban areas: Settlements of over 125,000 people
  • Other urban areas: Settlements of 10,000 to 125,000 people
  • Accessible small towns: Settlements of between 3000 and 10,000 people and within 30 minutes drive of a settlement of 10,000 or more
  • Remote small towns: Settlements of between 3000 and 10,000 people and with a drive time of over 30 minutes to a settlement of 10,000 or more
  • Accessible rural: Areas with a population of less than 3000 people and within 30 minutes drive of a settlement of 10,000 or more
  • Remote rural: Areas with a population of less than 3000 people and with a drive time of over 30 minutes to a settlement of 10,000 or more

Further information on the classification can be found on the Scottish Government website: http://www.scotland.gov.uk/Topics/Statistics/About/Methodology/UrbanRuralClassification

Multivariate analysis

Description of analysis undertaken

Many of the factors we are interested in are related to each other as well as being related to the outcome variables of interest. For example, younger mothers are more likely to have lower qualifications, to be lone parents, and to live in areas of high deprivation. Simple analysis may identify a relationship between maternal age and breastfeeding, for example. However, this relationship may be occuring because of the underlying association between maternal age and education. Thus, it is actually the lower education levels amongst younger mothers which is associated with a lower likelihood of breastfeeding than the fact that they are younger in age.

To take these possible confounds into account, in relation to breastfeeding and a range of other parent and child behaviours and outcomes, multivariate regression analysis was used. This analysis allows the examination of the relationships between an outcome variable and multiple explanatory variables whilst controlling for the inter-relationships between each of the explanatory variables. This means it is possible to identify an independent relationship between any single explanatory variable and the outcome variable; to show, for example, that there is a relationship between maternal age and breastfeeding that does not simply occur because both education and maternal age are related.

Factors associated with the following outcomes and behaviours have been examined via logistic regression analysis in this report:

  • Folic acid in first three months of pregnancy'
  • Any vitamin D intake around pregnancy'
  • Attendance at antenatal classes
  • Use of the internet as a source of information
  • Breastfeeding exclusively for six weeks or more
  • Breastfeeding for six weeks or more (including as part of mixed feeding)
  • Starting solids before five months[62]
  • Highly traditional, authoritarian attitudes
  • High parenting stress
  • Having one or more negative feelings about being a parent
  • High home chaos
  • Unrestricted TV
  • Looking at books with child less than daily
  • Nursery rhymes/songs with child less than daily
  • Visiting friends less than weekly
  • Child watches TV for more than 2 hours daily
  • Having a less warm mother-child relationship
  • Child had four or more health problems
  • Child was in the lowest CSBS quartile (with the least well developed communication skills)

The logistic regression employed both stepwise and non-stepwise approaches. Stepwise regression assesses each variable for significance, entering the most significant variable first and adjusting significance based on variables already entered into the equation, so that the final equation contains only those variables that remain significant when other variables are entered into the model. For the analysis which did not use the stepwise approach, a single model was compiled incorporating a wide range of predictor variables.

Interpreting regression results

The results of the regression analyses are summarized and described in the text of the relevant chapters. Full results are available on request. Regression results are given in odds ratios together with the probability that the association is statistically significant. The predictor variable was significantly associated with the outcome variable if p<0.05. The models determined the odds of being in the particular category of the outcome variable (eg. breastfeeding exclusively for six week) for each category of the independent variable (eg. parental education categories). Odds are expressed relative to a reference category, which has a given value of 1. Odds ratios greater than 1 indicate higher odds, and odds ratios less than 1 indicate lower odds.

To understand an odds ratio we first need to describe the meaning of odds. The definition of odds is similar but significantly different to that of probability. This is best explained in the form of an example. If 200 mothers out of a population of 1000 breastfed, the probability (p) of breastfeeding is 200/1000, thus p=0.2. The probability of not breastfeeding is therefore 1-p = 0.8. The odds of breastfeeding are calculated as the quotient of these two mutually exclusive events. So, the odds in favour of breastfeeding to not breastfeeding is therefore 0.2/0.8=0.25. Suppose that 150 out of 300 degree-educated mothers breastfeed compared to 50 out of 150 who have no qualifications. The odds of a degree-educated mother breastfeeding are 0.5/0.5=1.0. The odds of mother with no qualifications breastfeeding is 0.3333/0.6666=0.5. The odds ratio of breastfeeding is the ratio of these odds, 1.0/0.5=2.0. Thus the odds of breastfeeding are twice as high among degree-educated mothers (compared to mothers who have no qualifications - the 'reference category').

There are a number of inter-related issues regarding the breastfeeding/weaning 'age' variables that should be borne in mind when considering the results of analyses reported in chapter 4.


Contact

Email: Sharon Glen