The Young Lives survey is an innovative long-term project investigating the changing nature of childhood poverty in four developing countries. The purpose of the project is to improve understanding of the causes and consequences of childhood poverty and examine how policies affect children's well-being, in order to inform the development of future policy and to target child welfare interventions more effectively.
The objectives of the study are to provide good quality long-term data about the lives of children living in poverty, trace linkages between key policy changes and child welfare, and inform and respond to the needs of policymakers, planners and other stakeholders. Research activities of the project include the collection of data on a set of child welfare outcomes and their determinants and the monitoring of changes in policy, in order to explore the links between the policy environment and outcomes for children.
The study is being conducted in Ethiopia, India (in Andhra Pradesh), Peru and Vietnam. These countries were selected because they reflect a range of cultural, geographical and social contexts and experience differing issues facing the developing world; high debt burden, emergence from conflict, and vulnerability to environmental conditions such as drought and flood.
The Young Lives study aims to track the lives of 12,000 children over a 15-year period. This is the time-frame set by the UN to assess progress towards the Millennium Development Goals. Round 1 of the study followed 2,000 children (aged between 6 and 18 months in 2002) and their households, from both urban and rural communities, in each of the four countries (8,000 children in total). Data were also collected on an older cohort of 1,000 children aged 7 to 8 years in each country, in order to provide a basis for comparison with the younger children when they reach that age. Round 2 of the study returned to the same children who were aged 1-year-old in Round 1 when they were aged approximately 5-years-old, and to the children aged 8-years-old in Round 1 when they were approximately 12-years-old. Round 3 of the study returned to the same children again when they were aged 7 to 8 years (the same as the older cohort in Round 1) and 14 to 15 years. It is envisaged that subsequent survey waves will take place in 2013 and 2016. Thus the younger children are being tracked from infancy to their mid-teens and the older children through into adulthood, when some will become parents themselves.
Further information about the survey, including publications, can be downloaded from the <a href='http://www.younglives.org.uk/'>Young Lives</a> website.
A school survey was introduced into Young Lives in 2010, following the third round of the household survey, in order to capture detailed information about children’s experiences of schooling. It addressed two main research questions:
• how do the relationships between poverty and child development manifest themselves and impact upon children's educational experiences and outcomes?
• to what extent does children’s experience of school reinforce or compensate for disadvantage in terms of child development and poverty?
The survey allows researchers to link longitudinal information on household and child characteristics from the household survey with data on the schools attended by the Young Lives children and children's achievements inside and outside the school. A wide range of stakeholders, including government representatives at national and sub-national levels, NGOs and donor organisations were involved in the design of the school survey, so the researchers could be sure that the ‘right questions’ were being asked to address major policy concerns. This consultation process means that policymakers already understand the context and potential of the Young Lives research and are interested to utilise the data and analysis to inform their policy decisions. The survey provides policy-relevant information on the relationship between child development (and its determinants) and children’s experience of school, including access, quality and progression. This combination of household, child and school-level data over time constitutes the comparative advantage of the Young Lives study.
School Survey data are currently only available for India and Peru. The Peru data are available from the UK Data Archive under SN 7479.
Further information is available from the Young Lives <a href='http://www.younglives.org.uk/what-we-do/school-survey'>School Survey</a> webpages.
The survey instruments included data collection at the school, class and pupil level, and involved the head teacher, class teacher, and pupil. The instruments comprises of the following components:
• School roster
• Child questionnaire answer sheet 1
• Child questionnaire answer sheet 2
• Child Maths test
• Child Telugu test
• Child English test
• Child language learning experience
• Child observation 1 (Maths)
• Child observation 2 (Maths)
• Child observation 3 (Maths)
• Teacher questionnaire
• Teacher content knowledge test (Maths)
• Maths teacher observation 1
• Maths teacher observation 2
• Head teacher questionnaire
• School observation
• School observation - homework
Galab, S., Centre for Economic and Social Studies (India)
Reddy, P., Centre for Economic and Social Studies (India)
Data Archive Processing Standards
The data were processed to the UK Data Archive's A standard. A rigorous and comprehensive series of checks was carried out to ensure the quality of the data and documentation. Firstly, checks were made that the number of cases and variables matched the depositor's records. Secondly, checks were made that all variables had variable labels and all nominal (categorical) variables had value labels. Where possible, either with reference to the documentation and/or in communication with the depositor, absent labels were created. Thirdly, logical checks were performed to ensure that nominal (categorical) variables had values within the range defined (either by value labels or in the depositor's documentation). Lastly, any data or documentation that breached confidentiality rules were altered or suppressed to preserve anonymity.
All notable and/or outstanding problems discovered are detailed under the 'Data and documentation problems' heading below.
Data and documentation problems
Data conversion information
From January 2003 onwards, almost all data conversions have been performed using software developed by the UK Data Archive. This enables standardisation of the conversion methods and ensures optimal data quality. In addition to its own data processing/conversion code, this software uses the SPSS and StatTransfer command processors to perform certain format translations. Although data conversion is automated, all data files are also subject to visual inspection by a member of the Archives Data Services team.
With some format conversions, data, and more especially internal metadata (i.e. variable labels, value labels, missing value definitions, data type information), will inevitably be lost or truncated owing to the differential limits of the proprietary formats. A UK Data Archive Data Dictionary file (generally in Rich Text Format (RTF)) is usually provided for each data file, enabling viewing and searching of the internal metadata as it existed in the originating format. These files are called: [data file name]_UKDA_Data_Dictionary.rtf
Important information about the data format supplied
The following descriptions provide important information about the Archive's data supply formats. Some of this information is specific to the ingest format of the data, i.e. the format in which the Archive received the data from the depositor. The ingest format for this study was SPSS.
SPSS files (*.sav files)
If SPSS was not the ingest format, this format will generally either have been created via the SPSS command processor (e.g. if the ingest format is STATA, SAS, Excel, or dBase). If the ingest format was non-delimited or fixed-width text, SPSS files will have been created using SPSS command syntax.
Issues: There is very seldom any loss of data or internal metadata when importing data files into SPSS. Any problems will have been listed above in the Data and Documentation Problems section of this file.
STATA (*.dta files)
If STATA was not the ingest format, STATA files will generally have been created from SPSS via the StatTransfer command processor. Importantly, StatTransfer's optimisation routine is run so that variables with SPSS write formats narrower than the data (e.g. numeric variables with 10 decimal places of data formatted to FX.2) are not rounded upon conversion to STATA because they are converted to 'doubles ' rather than floats. Discrete user missing values are copied across into STATA (as opposed to being collapsed into a single system missing code).
Issues: There are a number of data and metadata handling mismatches between SPSS and STATA. Where any data or internal metadata has been lost or truncated, it will be logged in the study's SPSS_to_STATA_conversion RTF file. Note that the complete internal metadata has been supplied in the UKDA Data Dictionary file(s): [data file name]_UKDA_Data_Dictionary.rtf
Tab-delimited text (*.tab files)
If tab-delimited text was not the ingest format, tab-delimited files will have been created from via the SPSS command processor, and also from Excel and MS Access files. When exporting from Access data tables to tab-delimited text, the potentially problematic special characters (tabs, carriage returns, line feeds, etc.) allowed by Access memo and text fields may have been removed by the Archive if necessary.
Issues: Date formats in SPSS are always exported to mm/dd/yyyy in tab-delimited text format. There may be a mismatch with the documentation on such variables. Variables that include both date and time such as dd-mm-yyyy hh:mm:ss (e.g. 18-JUN-2011 13:28:00), will lose the time information and become mm/dd/yyyy. All users of the data in tab-delimited format should consult the UK Data Archive Data Dictionary RTF file(s).
If the data was exported from MS Access, more limited 'data documenter' information is generally available in the RTF variable information files. These files may also contain SQL setup information.
MS Excel (*.xls/*xslx files)
If MS Excel was not the ingest format, Excel files may have been created via StatTransfer. The date and time issues noted under tab-delimited format may also apply here.
SAS (*.sas7bdat and *sas files)
If SAS was not the ingest format, SAS files will usually have been created via StatTransfer or SPSS. SAS is not one of the Archive's standard supply formats, and the files are likely to have been created in response to a user request. The usual format is *.sas7bdat files plus a .sas proc formats file. Note that the complete internal metadata has been supplied in the accompanying UK Data Archive Data Dictionary file(s).
Issues: The main loss of information when converting from SPSS to SAS is user-missing value definitions. By editing the .sas file, the user can choose whether to collapse all user-missing values into system missing or preserve theï¿½value and lose the user-missing definition. To achieve the latterï¿½the following section of the .sas file should be removed before running it:
/* User Missing Value Specifications */
Note that the complete internal metadata has been suppliedï¿½in the UKDA Data Dictionary file(s): [data file name]_UKDA_Data_Dictionary.rtf
MS Access (*.mdb/*.mdbx files)
Due to substantial incompatibilities between versions of MS Access, the Archive will only make data available in MS Access format if this is the ingest format and/or the database contains important information in addition to the data tables (coding information, forms, queries, etc.).
The depositor has specified that registration is required and standard conditions of use apply. The depositor may be informed about usage. See <a href='http://ukdataservice.ac.uk/get-data/how-to-access/conditions.aspx'>terms and conditions</a> for further information.
All works which use or refer to these materials should acknowledge these sources by means of bibliographic citation. To ensure that such source attributions are captured for bibliographic indexes, citations must appear in footnotes or in the reference section of publications.
The bibliographic citation for this data collection is:
Boyden, J., Young Lives: School Survey, India, 2010-2011 [computer file]. Colchester, Essex: UK Data Archive [distributor], April 2014. SN: 7478 , http://dx.doi.org/10.5255/UKDA-SN-7478-1
Any publication, whether printed, electronic or broadcast, based wholly or in part on these materials, should acknowledge the original data creators, depositors or copyright holders, the funders of the Data Collections (if different) and the UK Data Archive, and to acknowledge Crown Copyright where appropriate.
Any publication, whether printed, electronic or broadcast, based wholly or in part on these materials should carry a statement that the original data creators, depositors or copyright holders, the funders of the Data Collections (if different) and the UK Data Archive bear no responsibility for their further analysis or interpretation.
Disclaimer and copyrights
Although all efforts are made to ensure the quality of the materials, neither the original data creators, depositors or copyright holders, the funders of the Data Collections, nor the UK Data Archive bear any responsibility for the accuracy or comprehensiveness of these materials.
All rights reserved. No part of these materials may be reproduced, stored in, or introduced into a retrieval system, or transmitted, in any form, or by any means (electronic, mechanical, photocopying, recording or otherwise) without the prior written permission of the UK Data Archive.
UK Data Archive
University of Essex
Essex C04 3SQ
Crown copyright held jointly with the Centre for Economic and Social Studies (India). Crown copyright material is reproduced with the permission of the Controller of HMSO and the Queen’s Printer for Scotland.
Anne Solon, Data and Survey Manager
University of Oxford
Young Lives, Oxford Department of International Development (ODID)