Here you can search our series of working papers, dating back to 1983. These papers use data from our four cohort studies and cover a wide range of topics, from social inequalities and mobility, to physical health, education and cognitive development. Other papers in the series seek to improve the practice of longitudinal research. At the present time, we are only able to accept papers if at least one author is a member of the CLS research team. Some of the working papers below will subsequently have been published in peer-reviewed journals.
For more information about our working papers series, please email us at firstname.lastname@example.org.
There is no one way to collect and analyse information about digital activity and behaviour, with methodologies varying from interviews and self-reported questionnaires, to diary studies and website analytics. Self-reports of digital behaviour, though widely used, are subject to measurement error, particularly recall problems. In this report, we aim to identify robust, new measures of online activity including direct objective measures.
The aim of this scoping review is to identify research methodologies or tools that could
potentially be used to enhance large-scale surveys, and in particular the CLS cohort studies. This review addresses the following research questions: How is social media data used in social research? What are the opportunities and challenges of using social media data? What are the possibilities for enhancing large-scale surveys by linking to social media data?
This scoping review has been conducted with the aim of finding opportunities for the longitudinal data on human cognition collected from the cohorts at the Centre of Longitudinal Studies UCL to be enhanced by:
1) novel data collection tools e.g. wearables, data from smartphones;
2) novel linkages e.g. consumer data, employer-held data, social media data; and
3) any other methods or measures with scientific utility.
The aim of this report is to identify opportunities for future data collection in the CLS cohorts to be enhanced by novel methods and linkages, specifically those relating to diet and expenditure. Such novel data collection may come from new tools and technologies (i.e wearables and smartphones), or through new data linkages (i.e consumer data or social media).
Implicating physical activity in biomedical and health research relies upon accurate measurement. Ultimately, a tool for assessing physical activity should be versatile, easy to interpret, and accurate in estimating intensity, volume, duration, and frequency of activity (Ainsworth et al., 2015). We conducted a non-systematic rapid review of the literature in this area to identify existing and novel methods of measuring physical activity in large-scale studies. The following sections will outline some commonly used methods for measuring physical activity in population-based cohort studies (e.g. accelerometers), along with some more novel approaches (e.g. combined monitors).
This report aims to investigate the opportunity and feasibility of the use of technologies to measure mental health in Centre of Longitudinal Studies (CLS) cohorts, and the opportunity and feasibility of linking CLS cohorts to nationally held records on mental health service use.
While adult children with siblings can share caring for older parents, adult only children face this responsibility alone. Yet, despite the extensive literature on informal caregiving more generally, research on only children’s parent-care is limited. Given increased longevity and reliance on informal caregiving, as well as an increase in one-child families, there is a need to further investigate only children’s caregiving. This paper investigates whether and how adult only children’s parent-care differs from those with siblings, how sibling composition intersects with gender and how it relates to wellbeing. Using data from three large scale British birth cohorts we analyse parent-care at different ages: 38 and 42 (born 1970), 50 and 55 (born 1958), and 63 (born 1946). Results show that only children are more likely to provide parent-care, with differences greater at later ages. Provision is gendered, and the sibling group composition matters for involvement. While caring is related to wellbeing, we found no evidence that this differs between only children and those with siblings.
A sequential mixed mode data collection, online-to-telephone, was introduced into the National Child Development Study for the first time at the study’s age 55 sweep in 2013. The study included a small experiment, whereby a randomised subset of study members was allocated to a single mode, telephone-only interview, in order to test for the presence of mode effects on participation and measurement. Relative to telephone-only, the offer of the web increased overall participation rates by 5.0 percentage points (82.8% vs. 77.8%, 95% confidence interval 2.7% to 7.3%). Differences attributable to mode of interview were detected in levels of item non-response and response values for a limited number of questions.
This paper addresses the question of whether attending a private school (both at primary and secondary stages) affects voting behaviour and political attitudes in adulthood. The analysis is based upon the British Cohort Study, a nationally representative cohort of children born in one week in April 1970 at age 42 years.
This paper describes the collection of saliva samples from cohort members and their biological parents in the Millennium Cohort Study. It analyses response rates, predictors of response, and details the DNA extraction, genotyping and imputation procedures performed on the data.
This paper presents a systematic data-driven approach to identify predictors of non-response at each sweep of the 1958 National Child Development Study (NCDS) and demonstrates that including such variables in analyses with principled methods can reduce bias due to missing data.
This paper presents a systematic data-driven approach to identify predictors of non-response at wave 8 (age 25-26 years) in Next Steps and demonstrates that including such variables in analyses with principled methods can reduce bias due to missing data.