Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
Reliability of Longitudinal Social Surveys of Access to Higher Education: The Case of Next Steps in England
by
Gorard, Stephen
, Siddiqui, Nadia
, Boliver, Vikki
in
Access
/ Antwortverhalten
/ Aspiration
/ Attainment
/ Attrition
/ Bias
/ Daten
/ Datengewinnung
/ Educational attainment
/ Educational inequality
/ Families & family life
/ Großbritannien
/ Haushaltseinkommen
/ Higher education
/ Hochschulbildung
/ Hochschulzugang
/ household income
/ Housing
/ Housing tenure
/ Inequality
/ Longitudinal studies
/ longitudinal study
/ Längsschnittuntersuchung
/ Missing data
/ Next Steps
/ Parents & parenting
/ Polls & surveys
/ Power
/ Private schools
/ Qualität
/ Reliability
/ Response bias
/ sampling bias
/ Social background
/ Social classes
/ Socioeconomic status
/ Soziale Herkunft
/ Sozioökonomischer Faktor
/ Students
/ Ungleichheit
/ Variables
/ Wealth
2019
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Reliability of Longitudinal Social Surveys of Access to Higher Education: The Case of Next Steps in England
by
Gorard, Stephen
, Siddiqui, Nadia
, Boliver, Vikki
in
Access
/ Antwortverhalten
/ Aspiration
/ Attainment
/ Attrition
/ Bias
/ Daten
/ Datengewinnung
/ Educational attainment
/ Educational inequality
/ Families & family life
/ Großbritannien
/ Haushaltseinkommen
/ Higher education
/ Hochschulbildung
/ Hochschulzugang
/ household income
/ Housing
/ Housing tenure
/ Inequality
/ Longitudinal studies
/ longitudinal study
/ Längsschnittuntersuchung
/ Missing data
/ Next Steps
/ Parents & parenting
/ Polls & surveys
/ Power
/ Private schools
/ Qualität
/ Reliability
/ Response bias
/ sampling bias
/ Social background
/ Social classes
/ Socioeconomic status
/ Soziale Herkunft
/ Sozioökonomischer Faktor
/ Students
/ Ungleichheit
/ Variables
/ Wealth
2019
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Reliability of Longitudinal Social Surveys of Access to Higher Education: The Case of Next Steps in England
by
Gorard, Stephen
, Siddiqui, Nadia
, Boliver, Vikki
in
Access
/ Antwortverhalten
/ Aspiration
/ Attainment
/ Attrition
/ Bias
/ Daten
/ Datengewinnung
/ Educational attainment
/ Educational inequality
/ Families & family life
/ Großbritannien
/ Haushaltseinkommen
/ Higher education
/ Hochschulbildung
/ Hochschulzugang
/ household income
/ Housing
/ Housing tenure
/ Inequality
/ Longitudinal studies
/ longitudinal study
/ Längsschnittuntersuchung
/ Missing data
/ Next Steps
/ Parents & parenting
/ Polls & surveys
/ Power
/ Private schools
/ Qualität
/ Reliability
/ Response bias
/ sampling bias
/ Social background
/ Social classes
/ Socioeconomic status
/ Soziale Herkunft
/ Sozioökonomischer Faktor
/ Students
/ Ungleichheit
/ Variables
/ Wealth
2019
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Reliability of Longitudinal Social Surveys of Access to Higher Education: The Case of Next Steps in England
Journal Article
Reliability of Longitudinal Social Surveys of Access to Higher Education: The Case of Next Steps in England
2019
Request Book From Autostore
and Choose the Collection Method
Overview
Longitudinal social surveys are widely used to understand which factors enable or constrain access to higher education. One such data resource is the Next Steps survey comprising an initial sample of 16,122 pupils aged 13-14 attending English state and private schools in 2004, with follow up annually to age 19-20 and a further survey at age 25. The Next Steps data is a potentially rich resource for studying inequalities of access to higher education. It contains a wealth of information about pupils' social background characteristics - including household income, parental education, parental social class, housing tenure and family composition - as well as longitudinal data on aspirations, choices and outcomes in relation to education. However, as with many longitudinal social surveys, Next Steps suffers from a substantial amount of missing data due to item non-response and sample attrition which may seriously compromise the reliability of research findings. Helpfully, Next Steps data has been linked with more robust administrative data from the National Pupil Database (NPD), which contains a more limited range of social background variables, but has comparatively little in the way of missing data due to item non-response or attrition. We analyse these linked datasets to assess the implications of missing data for the reliability of Next Steps. We show that item non-response in Next Steps biases the apparent socioeconomic composition of the Next Steps sample upwards, and that this bias is exacerbated by sample attrition since Next Steps participants from less advantaged social backgrounds are more likely to drop out of the study. Moreover, by the time it is possible to measure access to higher education, the socioeconomic background variables in Next Steps are shown to have very little explanatory power after controlling for the social background and educational attainment variables contained in the NPD. Given these findings, we argue that longitudinal social surveys with much missing data are only reliable sources of data on access to higher education if they can be linked effectively with more robust administrative data sources. This then raises the question - why not just use the more robust datasets?
This website uses cookies to ensure you get the best experience on our website.