Andy Ross

Beyond Transitions: Applying Optimal Matching Analysis to Life Course Research

Life course researchers have increasingly explored optimal matching analysis (OMA) as a tool for the analysis of sequences, such as sections of people’s status biographies. OMA is usually employed in combination with cluster analysis (CA) to create classifications of sequences. In this article, we introduce an analytic strategy that allows assessing the classification’s internal validity. Using ideal typical sequence representations, we test different cluster algorithms and are able to optimise the fit to the data. An application analyses economic activity sequences collected for two British birth cohorts born in 1958 and 1970, investigating historical changes in passages to adulthood. The results suggest that passages into adulthood have become more diverse since the 1970s. The analytic strategy produced a classification with better fit than straightforward CA.

Science related careers: aspirations and outcomes in two British cohort studies

Purpose – Understanding the factors and processes facilitating entry into science related occupations is a first step in developing effective interventions aiming to increase a skilled science base. This paper intends to address individual as well as family and school related influences on uptake of science, engineering, technology and health related careers.Design/methodology/approach – Drawing on data collected for two British birth cohorts: the 1958 National Child Development Study and the 1970 British Cohort Study, a developmental‐contextual model of career development is tested, comparing the experiences of over 17,000 men and women during the transition from school to work.Findings – The findings suggest that there is a persisting gender imbalance both in terms of aspirations and occupational attainment. Interest and attachment to a science related career are formed early in life, often by the end of primary education. School experiences, in particular, are crucial in attracting young people to a ca...

Health and social exclusion in older age: Evidence from Understanding Society, the UK Household Longitudinal Study

Background Social exclusion of the elderly is a key policy focus but evidence on the processes linking health and social exclusion is hampered by the variety of ways that health is used in social exclusion research. We investigated longitudinal associations between health and social exclusion using an analytical framework that did not conflate them. Methods Data employed in this study came from 4 waves of Understanding Society, the UK Household Longitudinal Study 2009–2013. The sample comprised all adults who took part in all 4 waves, were 65 years or more in Wave 3, and had complete data on our variables of interest for each analysis. We used linear regression to model the relationship between Wave 2/3 social exclusion and Wave1–2 health transitions (N=4312) and logistic regression to model the relationship between Wave2/3 social exclusion and Wave 4 health states, conditional on Wave 3 health (N=4244). Results There was a dose–response relationship between poor health in Waves 1 and 2 and later social exclusion. Use of a car, mobile phone and the internet moderated the association between poor health and social exclusion. Given the health status in Wave 3, those who were more socially excluded had poorer outcomes on each of the three domains of health in Wave 4. Conclusions Use of the internet and technology protected older adults in poor health from social exclusion. Age-friendly hardware and software design might have public health benefits.

Re-thinking social exclusion in later life: A case for a new framework for measurement

ABSTRACT Social exclusion is a dynamic multi-dimensional process that is interactive in nature. The complex interplay between domains, whereby each domain can act as a determinant, indicator and/or outcome of social exclusion, hinders understanding of the process and the mechanisms through which social exclusion exists. This article highlights the need to disentangle these pathways and move beyond descriptive accounts of social exclusion, presenting a new working framework that allows direct hypothesis testing of these between-domain relationships. Whilst this working framework can be applied to any population, this article focuses on older adults. Life events that can drive social exclusion such as bereavement and changes in health are more likely to occur in later life, and occur more frequently, increasing the risk of social exclusion for this population. Rooted in the new working framework, this article presents the construction of later life social exclusion measures for use with Understanding Society – the United Kingdom Household Longitudinal Study. The validity of these measures are considered by examining the characteristics of those aged 65 years and over who score the highest, and therefore experience the greatest level of exclusion. This new working framework and developed social exclusion measures provide a platform from which to explore the complex relationships between domains of social exclusion and ultimately provide a clearer understanding of this intricate multi-dimensional process.